Huss Park Attractions, the German ride manufacturer, has launched the Sky Tower Multimedia. This combines the highly successful HUSS Sky Tower observation experience with the latest HUSS film-based attraction, the Explorer. It is a world-first and provides a one-of-a-kind experience. 
The Sky Tower Multimedia’s ring-shaped passenger cabin lifts and rotates, making it a striking landmark at any site. The attraction moves gently and offers a unique viewing experience. It is a completely inclusive family ride with broad appeal for all ages. Visible from afar, the Sky Tower fulfils a fundamental human yearning to be among the clouds with a ‘bird’s-eye view’.The Sky Tower Multimedia enhances the trip and experience by immersing passengers in a virtual reality world or presenting customized movie content on an additional level, separated from the actual world by an iris. The custom content could be anything from a deep-sea dive to a voyage to outer space. Following the movie experience, guests rise to a height of up to 80-meters, enjoying a 360-degree unrestricted view through the full double-curved glass windows as they glide smoothly up and down the structure.
The Sky Tower Multimedia measures 20-meters in diameter and can hold 70 passengers per cycle for a maximum capacity of 1400 people per hour. There are no restrictions, meaning people of all abilities can enjoy the ride. The passenger capsule has a full double-curved laminated safety glass window front, and guests enter and exit simultaneously through two automatic doors. The innovative capsule design offers passengers unobstructed vistas and a new level of freedom – on arrival, they naturally split into two directions, strolling along the row of seats on both sides of the cabin on a walkway that is broad enough for comfortable access but narrow enough for a steep viewing angle. Guests sit around the cabin on outward-facing seats with a panoramic view. For ultimate comfort, the interior has air conditioning and direct and indirect LED lighting. Available cabin upgrades include 20 LED head-up displays angled at the front window to present additional content/information during the ride cycle, a high-quality, full-powered audio system with subwoofers under the seats and show effects to enhance the experience. The multimedia upgrade also includes a CCTV camera system. 
The adventure begins with a themed pre-show as customers wait at the launch platform to watch the cabin soar upwards or descend into the theatre below. The cabin is smoothly accelerated upwards to a pre-set velocity when all doors are closed. The cabin gently rotates after clearing the entryway, allowing passengers to enjoy the full tower panorama with unrestricted views at increasing heights. Once at the maximum height, the cabin makes at least one complete circle, giving passengers a stunning 360° view of the surrounding environment.The tower is a technological marvel. Content onboard can be produced in live-action, CGI (computer-generated imagery), or a combination of both types. Once below the surface, passengers can travel to any location, from the macro to the micro and everything in between, including beneath the sea, through a storm’s eye, into space, back in time to a Jurassic forest, forward in time to a futuristic city, and through some of the most breathtaking scenery on Earth. Various lighting effects and distinctive surface decorative elements can enhance the Sky Tower’s visual impact. The almost 85-meter cylindrical tower can be illuminated from the base, top, and cabin with coloured spotlights, creating a dynamic spectacle. Multifunctional lighting can make the top spire’s machine room and flagpole stand out. The cabin’s outside circumference below the windows has many independently controllable light spots, enhancing its daylight appearance.
A Long March 2D rocket lifted off at 11:06 p.m. Eastern, May 19 (0306 UTC, May 20), from the Taiyuan Satellite Launch Center, north China. The China Aerospace Science and Technology Group (CASC) confirmed launch success within an hour of liftoff.Aboard were four Beijing-3C remote sensing satellites. These are likely to enter roughly circular, 600-kilometer-altitude sun-synchronous orbits.The Long March 2D notably carried grid fins to help constrain the landing zone of its first stage. Taiyuan is deep inland and falling spent rocket stages can prove hazardous and disruptive downrange.The satellites were launched for Twenty First Century Aerospace Technology Co. Ltd. (21AT) of Beijing. The satellites were built by CASC’s China Academy of Space Technology (CAST). 21AT has earlier ordered satellites from Surrey Satellite Technology Ltd. (SSTL) of the United Kingdom.The Beijing-3C constellation consists of four 0.5-meter panchromatic, 2-meter multispectral resolution intelligent remote sensing satellites. Two of the quartet are also known as Nanning-2 and Zhengzhou Airport Satellite, according to 21AT. The former will provide services to Nanning, capital of Guangxi Zhuang Autonomous Region, and the region itself.The constellation will work with other, previously launched Beijing-3 satellites. These will combine to provide high-resolution remote sensing satellite data. They will also assist the development of new productive forces in commercial aerospace, and contribute to the modernization of the national governance system and governance capabilities, according to 21AT.The Beijing constellation is far from China’s largest remote sensing constellation. Changguang Satellite Technology (CGST), a spinoff from the Chinese Academy of Sciences’ CIOMP, has more than 100 Jilin-1 series satellites in orbit. These include optical and video satellites, with panchromatic resolution of around 0.70 meters. In 2022 it expanded its plans to launch 300 satellites by 2025.Sunday’s launch was China’s 23rd orbital launch of 2024. The country aims to launch around 100 times this year, with roughly 30 planned to be conducted by commercial launch service providers. Kuaizhou-11 and Ceres-1 solid rockets from commercial entities Expace and Galactic Energy respectively are expected to launch in the coming days. China’s Chang’e-6 lunar far side sample return spacecraft is currently in lunar orbit, awaiting an opportunity to land.
Enhancing Customer ExperienceOne of the most significant applications of machine learning is in enhancing customer experience. Businesses are leveraging ML algorithms to analyze customer data and predict behaviours, preferences, and needs. This predictive capability allows companies to offer personalized experiences, improving customer satisfaction and loyalty.For instance, e-commerce giants like Amazon use machine learning to recommend products based on customer's past purchases and browsing history. These personalized recommendations increase the likelihood of sales and enhance the overall shopping experience. Similarly, streaming services like Netflix use ML to suggest shows and movies tailored to individual viewer preferences, thereby increasing user engagement and retention.Optimizing Supply Chain ManagementSupply chain management is another area where machine learning is making a significant impact. Companies are using ML algorithms to forecast demand, optimize inventory levels, and improve logistics. These advancements lead to cost savings, reduced waste, and improved efficiency.For example, ML can analyze historical sales data to predict future demand accurately. This allows businesses to maintain optimal inventory levels, reducing the risk of overstocking or stockouts. Additionally, machine learning can optimize delivery routes and schedules, minimizing transportation costs and ensuring timely deliveries. Companies like DHL and FedEx are already utilizing ML to enhance their logistics operations, resulting in faster and more reliable delivery services.Improving Healthcare OutcomesThe healthcare industry is experiencing a revolution with the integration of machine learning. ML is helping healthcare providers deliver better patient care by enabling early diagnosis, personalized treatment plans, and efficient hospital management.Hospitals are also using ML to optimize their operations. Predictive analytics can forecast patient admission rates, allowing hospitals to allocate resources more efficiently and reduce waiting times. Overall, the benefits of machine learning in healthcare are transforming healthcare by providing data-driven insights that enhance patient care and operational efficiency.Automating Financial ProcessesMachine learning is streamlining financial processes by automating routine tasks and providing actionable insights. Businesses are using ML to automate tasks such as data entry, invoice processing, and financial reporting, reducing the risk of errors and freeing up employees to focus on more strategic activities.For example, machine learning algorithms can extract information from invoices and automatically match them with purchase orders, reducing the need for manual intervention. This not only speeds up the process but also reduces the likelihood of errors. Additionally, ML can analyze financial data to identify trends and anomalies, providing businesses with insights that inform strategic decision-making.Enhancing Marketing StrategiesMarketing is another area where machine learning is driving significant improvements. Businesses are using ML to analyze customer data, segment audiences, and optimize marketing campaigns. This data-driven approach enables companies to target the right customers with the right messages, increasing the effectiveness of their marketing efforts.For instance, the case studies of machine learning analyze customer interactions across various channels, such as social media, email, and website visits, to identify patterns and preferences. This information allows marketers to create personalized campaigns that resonate with their target audience. Additionally, ML can optimize ad placements and bidding strategies in real-time, maximizing the return on investment for digital advertising.
Artificial intelligence (AI) has seen tremendous progress over the last decade. Technologies like machine learning, neural networks, natural language processing, robotics and more have moved from research labs into real-world applications. As we enter 2024, the pace of AI innovation shows no signs of slowing down. The size of the worldwide AI market is anticipated to reach approximately USD 2575.16 billion by 2032.Beas Dev Ralhan, CEO, Next Education, will shed light on several key advancements that can be expected that could take AI capabilities to new heights across industries.Advancing Natural Language AIOne domain where AI has made significant strides recently is natural language processing. Applications like optimised content creation, language translation, text summarisation, sentiment analysis and conversational systems rely on a deeper grasp of language structure, meaning and context.In 2024, key improvements in the following natural language abilities of AI can be expected:
Contextual Understanding: AI models will get better at analysing vocabularies, writing styles, terminologies etc. to determine the contextual meaning of text, speech and other modes of communication. This allows ideas to be understood based on wider concepts, related information and past references.Personalisation: Smart content systems powered by AI will be able to tweak communication and recommendations to match the interests, priorities and needs of individual users or companies. This accounts for personas, demographics, industry-specific trends and other signals to serve customised information to the right stakeholders.Multilingual Fluency: AI-based translation abilities across diverse languages will see enhancements to preserve contextual meaning better. Systems can dynamically determine communication intent to make translations more accurate in both text and speech formats while switching languages seamlessly.Background Knowledge: By combining language models with structured knowledge about the world, AI can gain useful background context to better understand human communication and respond more intelligently. Integration of knowledge graphs and ontologies can enrich language AI to work dynamically beyond just training data.Next-Generation AI AssistantsIn 2024, digital assistants, exemplified by platforms like Alexa, Siri, and Google Assistant, are on the cusp of a transformative shift. Integral to our daily lives, these AI companions are evolving for an advanced user experience. A key upgrade is the broadening of domain knowledge, transcending traditional roles to provide detailed information across specialised areas, from financial advisory and health explanations to legal contract summaries.The next AI assistant iteration emphasises heightened reasoning capabilities and integrated experiences. These companions will showcase sharper logical reasoning, enabling precise planning, problem-solving, and tailored recommendations. The integration of experiences will dissolve barriers between apps and devices, allowing seamless engagement via voice, vision, gestures, and more.An omnipresent AI layer will dynamically personalise responses, highlighting a shift towards integrated, user-friendly interactions.AI Transforming EducationThe education sector also stands to gain enormously by harnessing the power of AI across learning, teaching, administration and enabling access. Edtech innovations to watch out for in 2024 include - AI-driven personalised and adaptive learning platforms that continuously improve student engagement and knowledge levels. Virtual teacher assistants can monitor hundreds of students simultaneously, providing prompts and clarification.Automated quality checks using computer vision ensure integrity, reduce errors and enhance efficiency across evaluation systems. Chat-based tutors and mentors can offer affordable peer learning opportunities at scale.All while robust analytics help policymakers, educators and platforms streamline tools, costs and access. AI promises to both democratise foundational literacy and make specialised world-class teaching expertise available to students universally.Next Strides in RoboticsThe integration of AI and robotics, a process evolving over decades, is now poised to reach unprecedented heights in 2024. This year may mark a significant milestone as robots transition into ubiquitous fixtures in daily life, delivering value across various functions.Notably, household robots are benefiting from advances in computer vision, natural communication, and locomotion, making affordable consumer robots capable of assisting in homes and workplaces. These robots can perform tasks ranging from cleaning and inventory management to scheduling events, making deliveries, and providing companionship.In the realm of warehouse automation, AI-powered robotics are addressing supply chain challenges by automating repetitive tasks in warehousing, storage, and logistics, enhancing speed, efficiency, and accuracy in inventory handling.Furthermore, the self-driving expansion is set to accelerate, with autonomous passenger vehicles meeting operational, policy, and pricing thresholds, likely resulting in the shipment of over a million self-driving units across various car segments, meeting user trust and safety requirements.To concludeAs this overview reveals, 2024 may mark an inflexion point where many AI technologies transition from hype into mature reliable solutions while also exploring uncharted territories with potential. Core ingredients like data, computing and algorithms will keep improving incrementally to unlock new possibilities.
TikTok’s latest machine learning ad solutions are proof that the platform wants to automate as much of its advertising as possible.The product, dubbed Performance Automation, was announced at the platform’s fourth annual TikTok World product summit today — its first official summit since Biden signed the TikTok “divest or sell” bill last month, and subsequently the entertainment app took the U.S. government to court to appeal.It’s safe to say TikTok wants advertisers to believe it’s not entertaining the idea of being booted out of the U.S. anytime soon. If that wasn’t already obvious during its NewFront earlier this month, this latest announcement makes it clearer that it’s business as usual for the platform right now. Or at least trying to make it as clear as possible that advertisers can park their contingency plans and keep spending on TikTok. “TikTok is actively working to keep marketers engaged and on the platform despite the legislative challenges,” said Traci Asbury, social investment lead at Goodway Group. “They [TikTok] have complete confidence in their upcoming legal appeals and are actively encouraging marketers to keep adopting best practices and usage of the platform’s capabilities to make positive impacts on their businesses.”What is Performance Automation, and how does it work?Well, you probably already know about TikTok’s Smart Performance Campaign, which was launched last year. The campaign uses “semi-automation capabilities” including auto-targeting, auto-bidding and auto-creative.But Performance Automation, which is still in early testing, goes one step further, by automating more of the process, including the creative. With this campaign, advertisers input the necessary assets, budget and goals, and TikTok’s predictive AI and machine learning will select the best creative asset, to ensure the best campaign is put in front of the right customer at the right time. As a TikTok spokesperson confirmed, the platform is moving toward a “performance automation vision” and this latest product is the next step on that journey.And that’s not all. The platform has also launched a similar capability for its TikTok Shop, dubbed TikTok Shop Marketing Automation. Like Performance Automation, this works by automating bidding, budgeting, ad management and creative for TikTok Shop products. Since TikTok Shop is only available in select regions, this latest product is currently rolled out in South-East Asia, and in testing in the U.S.Ohio-based health and wellness brand Triquetra Health is one of those early testers. According to Adolfo Fernandez, global product strategy and operations at TikTok, the brand already achieved 4x their return on investment in TikTok Shop within the first month of using this new automation product, and increased sales on the platform by 136%. He did not provide exact figures.To be clear, Performance Automation and TikTok Shop Marketing Automation aren’t their official names. These are just temporary identities the platform is using until they roll out the products officially.Still, all sounds familiar? That’s because it is. Performance Automation is similar to what the other tech giants have been doing for a while now, and what TikTok started to dabble in with its Smart Performance Campaign last year. Think Google’s Performance Max, Meta’s Advantage+ and now even Amazon’s Performance+ — they all play a similar role for their respective platforms. TikTok just joining the pack simply confirms that automation is the direction that advertising as an industry is heading. In many ways, this was inevitable. Meta, Google et al have amassed billions of ad dollars over the years by making it as easy as possible for marketers to spend on their ads. From programmatic bidders to attribution tools, the platforms have tried to give marketers fewer reasons to spend elsewhere. Machine learning technologies that essentially oversee campaigns are the latest manifestation of this. Sooner or later TikTok was always going to make a move.Still, there are concerns aplenty over how these technologies work — they are, after all, the ultimate “set it and forget it” type of campaign. Marketers hand over the assets and data they want the platform to work with, and the technology takes it from there. That’s it. Marketers have no way of knowing whether these campaigns are doing what the platform says they’re doing because they’re unable to have them independently verified. It remains to be seen whether TikTok’s own effort will take a similar stance or break with tradition.Speaking of measurement, TikTok is also launching unified lift — a new product which measures TikTok campaign performance across the entire decision journey, using brand and conversion lift studies. KFC Germany has already tried it out and drove a 25% increase in brand recall and saw an 81% increase in app installs, according to Fernandez, without providing exact figures.What else is new?Among the other announcements were: TikTok One: a centralized home where advertisers can access TikTok’s pool of nearly 2 million creators, agency partners as well as creative tools. TikTok Symphony: a creative AI suite, which aims to provide marketers with even more efficiency through script writing, video production and optimizing assets.Plus a couple of entertainment experiences including interactive add-ons for TopView – advertisers can add pop-out elements and countdown stickers to their TikToks, as well as Duet with branded mission: which allows brands to invite TikTok creators to duet with their branded mission videos.Where are marketers’ heads at?Well for now, nothing much has changed. Marketers have contingency plans in place, but that’s just standard business practice. Beyond that, everything as far as TikTok goes is pretty much business as usual.Colleen Fielder, group vp of social and partner marketing solutions at Basis Technologies said her team is not actively recommending any of their clients discontinue spending on TikTok. They’re continuing to include the platform on proposals.“We knew TikTok was going to sue the U.S. government, and that may push this 9-12 month timeline even further back, which gives us a longer lead time to continue running on TikTok and / or identify alternative platforms as needed,” she said.For Markacy, it’s a similar state of play. “We have a loose partnership with digital media company Attn, which is heavily invested in TikTok,” said Tucker Matheson, co-CEO of the company. “They’re still getting big proposals for work, which is a positive sign.”
"The program promises not only to speed research into the neurobiology of behavior, but also to enable comparison and reconcile results that disagree due to differences in how individual laboratories observe, analyze and classify behaviors," said Sam Golden, assistant professor of biological structure at the University of Washington School of Medicine."The approach allows labs to develop behavioral procedures however they want and makes it possible to draw general comparisons between the results of studies that use different behavioral approaches," he said.A paper describing the program appears in the journal Nature Neuroscience. Golden and Simon Nilsson, a postdoctoral fellow in the Golden lab, are the paper's senior authors. The first author is Nastacia Goodwin, a graduate student in the lab.The study of the neural activity behind animal behavior has led to major advances in the understanding and treatment of such human disorders as addiction, anxiety and depression.Much of this work is based on observations painstakingly recorded by individual researchers who watch animals in the lab and note their physical responses to different situations, then correlate that behavior with changes in brain activity.For example, to study the neurobiology of aggression, researchers might place two mice in an enclosed space and record signs of aggression. These would typically include observations of the animals' physical proximity to one another, their posture, and physical displays such as rapid twitching, or rattling, of the tail.Annotating and classifying such behaviors is an exacting, protracted task. It can be difficult to accurately recognize and chronicle important details, Golden said. "Social behavior is very complicated, happens very fast and often is nuanced, so a lot of its components can be lost when an individual is observing it."To automate this process, researchers have developed AI-based systems to track components of an animal's behavior and automatically classify the behavior, for example, as aggressive or submissive.Because these programs can also record details more rapidly than a human, it is much more likely that an action can be closely correlated with neural activity, which typically occurs in milliseconds.
A video frame of two mice whose behavior is being analyzed by SimBA. The dots represent the body parts being tracked by the program. Credit: Nastacia GoodwiOne such program, developed by Nilsson and Goodwin, is called SimBA, for Simple Behavioral Analysis. The open-source program features an easy-to-use graphical interface and requires no special computer skills to use. It has been widely adopted by behavioral scientists."Although we built SimBA for a rodent lab, we immediately started getting emails from all kinds of labs: wasp labs, moth labs, zebrafish labs," Goodwin said.But as more labs used these programs, the researchers found that similar experiments were yielding vastly different results."It became apparent that how any one lab or any one person defines behavior is pretty subjective, even when attempting to replicate well-known procedures," Golden said.Moreover, accounting for these differences was difficult because it is often unclear how AI systems arrive at their results, their calculations occurring in what is often characterized as "a black box."Hoping to explain these differences, Goodwin and Nilsson incorporated into SimBA a machine-learning explainability approach that produces what is called the Shapely Additive exPlanations (SHAP) score.Essentially, what this explainability approach does is determine how removing one feature used to classify a behavior, say tail rattling, changes the probability of an accurate prediction by the computer.By removing different features from thousands of different combinations, SHAP can determine how much predictive strength is provided by any individual feature used in the algorithm that is classifying the behavior. The combination of these SHAP values then quantitatively defines the behavior, removing the subjectivity in behavioral descriptions."Now we can compare (different labs') respective behavioral protocols using SimBA and see whether we're looking, objectively, at the same or different behavior," Golden said."This approach allows labs to design experiments however they like, but because you can now directly compare behavioral results from labs that are using different behavioral definitions, you can draw clearer conclusions between their results. Previously, inconsistent neural data could have been attributed to many confounds, and now we can cleanly rule out behavioral differences as we strive for cross-lab reproducibility and interpretability," Golden said..
Every minute, computers worldwide grab millions of gigabytes of data. But the question is, what effort will you put into fetching sense out of this vast data? How does a data scientist utilize this data for the applications that bring power to the modern world?Data science is a continuously evolving field that implements scientific methods and algorithms to manage complicated data sets. Data scientists use languages such as R and Python to analyze and harness the available data. To become successful data scientists, professionals must have key knowledge of programming and languages, especially Python and statistics. Books are a great way to enhance the knowledge spectrum, and there are many accessible books on data science with Python. Likewise, you should invest in the greatest book to understand data science using Python.In this article, you will learn about the overview and key takeaways of the top books on data science with Python in 2024.Top Books on Data Science With Python in 2024To learn data science with Python, select from the top data science with Python books discussed below:Popular Books Automate the Boring Stuff with Python (Practical Programming for Total Beginners) - Al SweigartAutomate the boring stuff with Python is a detailed guide to automating tasks by implementing Python. Al Sweigart, the author, has written this best book for data science with Python in an easy-to-understand and follow language, even for programming beginners.Key Takeaways:This book includes multiple topics, such as file manipulation, Web scraping and functioning Excel files.It offers step-by-step guidance for performing each task, along with code, snippets, and examples for the readers to understand the concepts easily.Moreover, this book includes major topics, such as debugging and error handling.Python Data Science Handbook: Tools and Techniques for Developers - Jake Vander PlasIt is the best book to learn Python for data science and to function with data in Python. Jake Vander Plas is an experienced data scientist who wrote this book in an easily understandable language.Key Takeaways:This book includes topics such as matplotlib, NumPy, pandas, and seaborn.This book explains each concept with a step-by-step guide, along with gold snippets and examples, for a clear understanding.It includes major topics such as data, visualization, machine learning, and data manipulation.Python programming language is used to elaborate the concepts.Become a Data Science & Business Analytics Professional28%Annual Job Growth By 202611.5 MExpected New Jobs For Data Science By 2026
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I just completed 3 classes under this program - Data Science Using SAS, R and Big Data Hadoop and Spark Developer. I am currently enrolled in Python training. What I like the most is that the live recordings from each class are lifetime references for us to review in the future. The self-running videos in each topic were also very useful as they cover theory which might not have been covered during the live classes. I have made significant gains so far in my knowledge of key technologies and tools in Data Science. Together with electives offered under this program, I will eventually be getting a comprehensive foundation training in Data Science.
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Simplilearn has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent.Not sure what you’re looking for?View all Related ProgramsPython Crash Course: A Hands-On, Project-Based Introduction to Programming - Eric MatthesIt is a fast-paced, detailed introduction to Python that will help you to write programs, solve problems, and a lot more. The first half of the book will teach you about basic programming concepts and the process of making your programs interactive. However, the second half will put your knowledge into practice with three major projects, including data visualization with Python’s super handy libraries, a space, invaders, an inspired arcade game, and a simple web application that you can deploy online.Key Takeaways:You will learn how to use powerful Python tools and libraries.This book will teach you to work with data to generate interactive visualizations, customize and create web pages, and deploy them online safely.Learn to manage errors and mistakes to solve your programming problems.Python for Data Analysis - Data Wrangling with Pandas, NumPy, and IPython - Wes McKinneyThis book provides complete instructions for processing, manipulating, crunching, and cleaning data sets in Python. Hence, it is a practical, modern introduction to data science tools in Python. It is an ideal option for beginner data analysis and Python programmers new to scientific computing and data science.Key Takeaways:Through this book, the readers can learn advanced and basic features in NumPy, beginning with data, analysis, tool, transform, cleaning, merging and reshaping the dataCreate visualizations with matplotlib and more.Learn to solve real-world data analysis problems with detailed and total examples.Think Python - How To Think Like a Computer Scientist - Allen B. DowneThis book stands as the best Python for data science. Allen B. Downey saw several students struggling with this topic, so he wrote this book. Key Takeaways:This book offers basic knowledge of programming, arithmetic operators, and running Python.Readers can learn several operations such as composition, math, functions, stack diagrams, and flow of execution.It includes debugging runtime and syntactic and semantic errors.Moreover, this book offers an analysis of search algorithms and basic Python operations.Learning Python - Mark Lutz and David AscherThe first part of this book provides all the necessary information to the programmers, including content on classes, operators, types, functions, exceptions, statements, and modules. For aspiring programmers or data scientists, the learning of Python book includes additional information, such as consideration choices and talks of program start, updated summaries of syntax, highlighting object-oriented programming, an updated discussion of documentation sources and more.Key Takeaways:This book includes a detailed understanding of the technological strengths of Python.Every chapter includes a collection of activities, testing your Parton knowledge and measuring your comprehension.It puts a major focus on the detailed core language.By reading this book, you can learn to use Python for component integration, database development, systems, programming, and GUIs.It also includes programming for images, artificial intelligence, XML and games using Python.Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C. Müller and Sarah GuidoIts authors wrote this book to use machine learning and Python without any undergraduate degree or Ph.D. wishing to apply machine learning.Key Takeaways:This book clearly explains the process to chain models and encapsulate your process through pipelines.Explain the importance of the way in which machine-learned data is presented and the parts of data that must be taken into consideration.It includes references to highly sophisticated subjects and offers a high-level summary.For researchers, data scientists, and scientists working on commercial applications, this book provides helpful techniques.It also discusses the most popular machine learning algorithms used at present and examines their advantages and disadvantages.Data Science from Scratch: First Principles with Python - Joel GrusFor learners who hold an aptitude for programming and mathematics abilities, this book is the best option to guide them through statistics and arithmetic at the heart of data signs, along with the hacking skills needed to begin your career as a data scientist.Key Takeaways:It explains the machine learning basics.This book teaches readers the Python crash course.It explains the process of investigating recommendation systems, Network analysis, databases, NLP and MapReduce.Moreover, it holds information on data collection, cleaning, exploration, manipulation, and munging.Parallel Computing for Data Science: With Examples in R, C++ and CUDA - Norman MatloffIt is the first Python computing book written exclusively on algorithms, parallel data structures, applications, software tools, and data science.Key Takeaways:The main focus of this book is on computation. It shows the process of computing on three kinds of platforms: graphics processing units, multicore systems, and clusters.It also discusses software packages that have more than one kind of hardware and can be used in multiple programming languages.Data Science For Dummies - Lillian PiersonData science for dummies is the best starting point for IT students and professionals who quickly want to cover the multiple areas of expensive data science space. By focusing on business cases, the book includes several topics about data science, big data, and data engineering, as well as how these three major areas are combined to produce tremendous value.Key Takeaways:It provides a background in data engineering and big data before moving ahead to data science and its application for generating value.This book includes coverage of big data, frameworks and applications, such as MPP platforms, Spark, and No SQL.It includes machine learning explanations along with its algorithms, as well as the evolution of the Internet of Things and artificial intelligence.Readers also get to acknowledge data visualization techniques, which are used to summarize, showcase, and communicate the generated data insights.Become a Data Scientist through hands-on learning with demo projects, live training, and 24/7 support! Start learning now!Practical Data Science with R - Nina Barrameda Zumel, John MountThis book explains basic principles without theoretical mumbo-jumbo and uses real cases to collect, analyze, and curate the data essential for the success of your business. With the application of statistical analysis, techniques, and R programming language, it carefully elaborates examples in regard to business intelligence, marketing, and decision support.Key Takeaways:This book helps developers and business analysts to increasingly collect, analyze, accurately, and report on essential business data.The R programming language and its tools offer a straightforward process to handle day-to-day data science tasks without an in-depth academy. Torre on advanced mathematics.Moreover, this book also represents the process of applying useful statistical techniques and R programming language to everyday business situations.Data Analytics with Hadoop: An Introduction for Data Scientists - Jenny Kim, Benjamin BengfortIf you are willing to use machine learning and statistical techniques across a huge data set, this guide shows the reason why the Hadoop ecosystem is the best option for the job. Key Takeaways:Through this book, data analysts and data scientists can learn to perform multiple techniques, ranging from writing Spark applications and MapReduce with Python to using data management and advanced modeling with Hive, Spark MLlib, and HBase.Readers can also learn about data systems and analytical processes available to empower and build data products that can handle huge sets of data.This book also provides core concepts behind cluster computing and Hadoop. Introduction to Machine Learning with Python: A Guide for Data Scientists - Sarah Guido, Andreas C. MullerIf you’re a Python user, this book will provide you with practical ways to create your machine-learning solutions.Key Takeaways:It offers crucial steps required for creating machine learning applications with Python.The authors have focused mainly on the practical aspects of utilizing machine learning algorithms.Moreover, this book is familiar to NumPy and matplotlib.Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python - Thomas W. MillerIn this book, the author explains the essential principles, concepts, and theory of real-world applications.Key Takeaways:In this book, topics such as segmentation, brand and product positioning, target marketing, choice modeling, new product development, customer retention and much more are covered.The author also integrates important insights and information on modeling techniques in predictive analysis.Python Data Analysis - Ivan IdrisThis practical guide will provide you with a clear understanding of data analysis pipelines via machine learning techniques and algorithms implementation.Key Takeaways:Readers get to explore data science and its multiple-process models.Learn to perform data manipulation through Pandas and NumPy to clean, aggregate, and handle missing data.Understand feature engineering and data processing through scikit-learn and pandas.Become a Data Science & Business Analytics Professional28%Annual Job Growth By 202611.5 MExpected New Jobs For Data Science By 2026
Applied Data Science with Python3 simulation test papers for self-assessment
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Data ScientistIndustry-recognized Data Scientist Master’s certificate from Simplilearn
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11 months
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Tham Chup Wai
I just completed 3 classes under this program - Data Science Using SAS, R and Big Data Hadoop and Spark Developer. I am currently enrolled in Python training. What I like the most is that the live recordings from each class are lifetime references for us to review in the future. The self-running videos in each topic were also very useful as they cover theory which might not have been covered during the live classes. I have made significant gains so far in my knowledge of key technologies and tools in Data Science. Together with electives offered under this program, I will eventually be getting a comprehensive foundation training in Data Science.
A.Anthony Davis
Simplilearn has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent.Not sure what you’re looking for?View all Related ProgramsTop Books for AnalysisPython for Data Analysis - Wes McKinneyThis book offers complete instructions for processing, manipulating, crunching, and cleaning data sets in Python.Key Takeaways:It comes with practical case studies showing you the process to solve a broad set of data analysis problems. You also get to learn the most updated versions of IPython, pandas, NumPy and Jupyter.It introduces data science tools in Python.Python for Data Science For Dummies - John Mueller, Luca MassaronThis write-up provides the easiest and fastest way to learn Python programming and statistics.Key Takeaways:This book is written for people who are freshers in data analysis.It discusses Python data analysis programming and statistics basics.Moreover, it focuses on Google Colab, making it possible to write Python code in the cloud.Python Data Analysis: Perform Data Collection, Data Processing, Wrangling, Visualization, and Model Building Using Python - Ivan Idris, Armando Fandango, Avinash NavlaniThis guide offers detailed information on data analysis pipelines using machine learning techniques and algorithms.Key Takeaways:Learn about data science and its process models.Study how to perform data manipulation through NumPy and Pandas to clean, aggregate, and manage missing values.It is an amazing book for business analytics, data analysts, data scientists, and statisticians who are willing to learn to use Python for data analysis. Hands-On Data Science for Marketing: Improve Your Marketing Strategies with Machine Learning Using Python and R - Yoon Hyup HwangThis book explains how you can drive successful marketing campaigns through data science and implement machine learning to improve customer engagement, product recommendations, and retention.Key Takeaways:Learn to extract insights from data to increase profitability and optimize marketing strategies.Study how to implement data science techniques to acknowledge the drivers behind the failures and successes of marketing campaigns.Understand and predict customer behavior and create effective, personalized and targeted marketing strategies.Hands-On Data Analysis with Pandas: A Python Data Science Handbook for Data Collection, Wrangling, Analysis, and Visualization - Stefanie MolinGet professional with Pandas by working with mastered data, discovery, and real data sets, as well as data preparation, data manipulation, and managing data for analytical tasks through this book.Key Takeaways:Learn to perform data analysis and manipulation tasks by using pandas.Practice Pandas application in multiple real-world domains with the assistance of step-by-step examples.Get hands-on data analysis with pandas for beginners and those boosting their skills in data science.Become a Data Science & Business Analytics Professional28%Annual Job Growth By 202611.5 MExpected New Jobs For Data Science By 2026
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Tham Chup Wai
I just completed 3 classes under this program - Data Science Using SAS, R and Big Data Hadoop and Spark Developer. I am currently enrolled in Python training. What I like the most is that the live recordings from each class are lifetime references for us to review in the future. The self-running videos in each topic were also very useful as they cover theory which might not have been covered during the live classes. I have made significant gains so far in my knowledge of key technologies and tools in Data Science. Together with electives offered under this program, I will eventually be getting a comprehensive foundation training in Data Science.
A.Anthony Davis
Simplilearn has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent.Not sure what you’re looking for?View all Related ProgramsPython Data Analytics: Data Analysis and Science Using Pandas, Matplotlib and the Python Programming Language - Fabio NelliThis book helps you tackle data acquisition and analysis with the power of the Python programming language. Moreover, this book includes coverage of an open source, pandas, easy-to-use data analysis, tools, and data structures for Python programming, language, and BSD-licensed library, providing high-performance.Key Takeaways:Learn how flexible and intuitive it is to recognize and communicate meaningful data patterns using data export, reporting systems, and Python scripts.This book includes information about processing, opening, managing, storing and analyzing data through the Python programming language.Python data analytics stands as an invaluable reference with multiple examples of assessing and storing data in a database.Hands-On Data Analysis with Pandas: Efficiently Perform Data Collection, Wrangling, Analysis, and Visualization Using Python - Stefanie MolinThis book is a great choice for data analysis, data science beginners, and Python developers who are willing to explore every step of scientific computing and data analysis through a wide range of data sets. Moreover, data scientists who want to apply Pandas in their machine-learning workflow can also get valuable information through this book.Key Takeaways:Learn how to use Python data science libraries to analyze data sets in the real world.Understand how to solve analysis and common data or presentation problems through Pandas.Build Python packages, scripts and modules for reusable analysis code.Preparation Tips for Data Science with PythonPython preparation requires constant practice and selecting the best and most informative book for Python data science. Some of the key Python preparation tips include:Start your preparation with a clear understanding of the foundations or fundamentals of this programming language, such as properties, data, types, strings, glasses, functions, files, outputs or inputs.Study advanced Python data science techniques and apply them to real-world micro projects.Create a data science portfolio when learning Python. Acknowledge data, cleaning, machine learning, and data visualization projects.Apply your theoretical knowledge to practice by working on small Python projects. Hands-on training is the greatest possible way to become an expert Python programmer.Be sure to study Pandas, Scikit-learn, NumPy, and Matplotlib libraries, as they are the four key Python data science libraries.Become a Data Scientist through hands-on learning with demo projects, live training, and 24/7 support! Start learning now!More Ways to Learn Python for Data ScienceThere are numerous ways to learn Python for data science. Some of the most useful ways to learn Python for data science include:Academic Pursuit: Students who are passionate about learning Python for data science can select a data science course for their graduate or undergraduate studies where they are taught this subject.Professional Training: Individuals seeking to become data science professionals can enroll in online courses to avail of a Python data science certification. Amongst the finest training resources is Simplilearn, where you can enroll for data science certification and learn under the guidance of experts.Self-Study: Read multiple articles, blogs, posts and Python tutorials.Practical Application: Select the best Python for data science handbook, and then apply the learned information to real-world projects.ConclusionSome of the best data science Python books, written by great writers, have been discussed above. Individuals willing to get trained and become Python programming experts must select the best book to learn data science with Python from the ones listed above to become data science experts. By gaining a Simplilearn’s Data Science with Python certification, you can build the desired skills to design machine learning and data science models like a professional.
Working together, NASA and IBM Research have developed a new artificial intelligence model to support a variety of weather and climate applications. The new model – known as the Privthi-weather-climate foundational model – uses artificial intelligence (AI) in ways that could vastly improve the resolution we’ll be able to get, opening the door to better regional and local weather and climate models. Foundational models are large-scale, base models which are trained on large, unlabeled datasets and can be fine-tuned for a variety of applications. The Privthi-weather-climate model is trained on a broad set of data – in this case NASA data from NASA’s Modern-Era Retrospective analysis for Research and Applications (MERRA-2)– and then makes use of AI learning abilities to apply patterns gleaned from the initial data across a broad range of additional scenarios. “Advancing NASA’s Earth science for the benefit of humanity means delivering actionable science in ways that are useful to people, organizations, and communities. The rapid changes we’re witnessing on our home planet demand this strategy to meet the urgency of the moment,” said Karen St. Germain, director of the Earth Science Division of NASA’s Science Mission Directorate. “The NASA foundation model will help us produce a tool that people can use: weather, seasonal and climate projections to help inform decisions on how to prepare, respond and mitigate.” With the Privthi-weather-climate model, researchers will be able to support many different climate applications that can be used throughout the science community. These applications include detecting and predicting severe weather patterns or natural disasters, creating targeted forecasts based on localized observations, improving spatial resolution on global climate simulations down to regional levels, and improving the representation of how physical processes are included in weather and climate models.“These transformative AI models are reshaping data accessibility by significantly lowering the barrier of entry to using NASA’s scientific data,” said Kevin Murphy, NASA’s chief science data officer, Science Mission Directorate at NASA Headquarters. “Our open approach to sharing these models invites the global community to explore and harness the capabilities we’ve cultivated, ensuring that NASA’s investment enriches and benefits all.” Privthi-weather-climate was developed through an open collaboration with IBM Research, Oak Ridge National Laboratory, and NASA, including the agency’s Interagency Implementation and Advanced Concepts Team (IMPACT) at Marshall Space Flight Center in Huntsville, Alabama. Privthi-weather-climate can capture the complex dynamics of atmospheric physics even when there is missing information thanks to the flexibility of the model’s architecture. This foundational model for weather and climate can scale to both global and regional areas without compromising resolution. “This model is part of our overall strategy to develop a family of AI foundation models to support NASA’s science mission goals,” said Rahul Ramachandran, who leads IMPACT at Marshall. “These models will augment our capabilities to draw insights from our vast archives of Earth observations.” Privthi-weather-climate is part of a larger model family– the Privthi family– which includes models trained on NASA’s Harmonized LandSat and Sentinel-2 data. The latest model serves as an open collaboration in line with NASA’s open science principles to make all data accessible and usable by communities everywhere. It will be released later this year on Hugging Face, a machine learning and data science platform that helps users build, deploy, and train machine learning models. “The development of the NASA foundation model for weather and climate is an important step towards the democratization of NASA’s science and observation mission,” said Tsendgar Lee, program manager for NASA’s Research and Analysis Weather Focus Area, High-End Computing Program, and Data for Operation and Assessment. “We will continue developing new technology for climate scenario analysis and decision making.” Along with IMPACT and IBM Research, development of Privthi-weather-climate featured significant contributions from NASA’s Office of the Chief Science Data Officer, NASA’s Global Modeling and Assimilation Office at Goddard Space Flight Center, Oak Ridge National Laboratory, the University of Alabama in Huntsville, Colorado State University, and Stanford University.
In the digital age, where data reigns supreme and personalized experiences are paramount, artificial intelligence (AI) has emerged as a game-changer in the field of marketing. From predictive analytics to chatbots, AI technologies are revolutionizing how businesses attract, engage, and retain customers. This article delves into the dynamics of the artificial intelligence in marketing market, exploring current trends, market drivers, restraints, opportunities, regional insights, key competitors, and the promising trajectory of future growth.Download Sample Report Copy Of This Report From Here: www.amecoresearch.com/sample/276950Current Market TrendsThe artificial intelligence in marketing market is witnessing several notable trends that are reshaping the landscape of marketing strategies and campaigns. One significant trend is the adoption of AI-powered predictive analytics to anticipate customer behavior and preferences. By analyzing vast amounts of data, AI algorithms can identify patterns, trends, and insights that enable marketers to tailor their messaging and offers to specific audience segments.Moreover, chatbots and virtual assistants powered by AI technology are becoming increasingly prevalent in customer service and engagement. These intelligent bots can handle inquiries, provide personalized recommendations, and even facilitate transactions, enhancing the overall customer experience and streamlining communication channels.Market DriversSeveral factors are driving the growth of the artificial intelligence in marketing market. Firstly, the exponential growth of data generated through digital channels, social media platforms, and e-commerce transactions has created a demand for advanced analytics and automation tools to extract actionable insights. AI enables marketers to leverage this data deluge to make informed decisions and deliver targeted marketing campaigns with precision and efficiency.Furthermore, the shift towards omnichannel marketing strategies and the proliferation of mobile devices have intensified the need for AI-driven solutions that can deliver seamless and cohesive customer experiences across multiple touchpoints. AI technologies such as natural language processing (NLP) and machine learning (ML) empower marketers to engage with consumers in real-time and deliver personalized content that resonates with their preferences and behaviors.RestraintsDespite its potential, the artificial intelligence in marketing market faces certain challenges that may hinder its widespread adoption. One major restraint is the lack of skilled professionals with expertise in AI and data analytics. As AI technologies continue to evolve rapidly, there is a growing demand for talent capable of developing, implementing, and optimizing AI-driven marketing solutions.Moreover, concerns regarding data privacy, security, and ethics pose challenges to the adoption of AI in marketing. Marketers must navigate regulatory frameworks and ensure compliance with data protection laws while leveraging customer data to drive personalized experiences.OpportunitiesThe artificial intelligence in marketing market presents numerous opportunities for innovation and growth. As AI technologies become more sophisticated and accessible, businesses of all sizes can leverage AI-driven solutions to gain a competitive edge and enhance customer engagement. From personalized recommendations to automated email campaigns, AI empowers marketers to deliver timely and relevant messages that resonate with their target audience.Furthermore, the integration of AI with emerging technologies such as augmented reality (AR) and virtual reality (VR) opens up new avenues for immersive and interactive marketing experiences. Brands can leverage AI-powered AR/VR applications to create memorable brand experiences, drive customer engagement, and differentiate themselves in crowded markets.Regional Market InsightsThe artificial intelligence in marketing market exhibits varying dynamics across different regions. North America leads the market, driven by the presence of tech giants, innovative startups, and a strong ecosystem of AI research and development. The region's advanced infrastructure, digital maturity, and high adoption rates of AI technologies in marketing contribute to its dominance in the global market.Europe is also a significant market for artificial intelligence in marketing, with countries like the UK, Germany, and France at the forefront of AI innovation and adoption. Asia-Pacific, particularly China and India, presents immense growth opportunities fueled by rapid digitalization, expanding e-commerce markets, and increasing investments in AI technology.Future Market Growth PotentialThe future outlook for the artificial intelligence in marketing market is highly promising, driven by ongoing advancements in AI technology, increasing adoption of digital marketing strategies, and evolving consumer expectations. As AI algorithms become more sophisticated and capable of handling complex tasks, the role of AI in marketing will continue to expand, enabling marketers to unlock new insights, optimize campaigns, and drive revenue growth.Moreover, the proliferation of AI-powered voice assistants, smart speakers, and IoT devices presents new opportunities for marketers to engage with consumers through voice-enabled interactions and personalized recommendations. As AI becomes more integrated into everyday life, the potential for AI-driven marketing solutions to deliver hyper-targeted, contextually relevant experiences will only continue to grow.In conclusion, the artificial intelligence in marketing market represents a transformative force in the realm of marketing and customer engagement. As businesses embrace AI-driven solutions to navigate the complexities of the digital landscape, the market holds immense potential for growth, innovation, and value creation for both businesses and consumers alike.To Check Complete Toc Here:CHAPTER 1. Industry Overview of Artificial Intelligence in Marketing MarketCHAPTER 2. Research ApproachCHAPTER 3. Market Dynamics And Competition AnalysisCHAPTER 4. Manufacturing Plant AnalysisCHAPTER 5. Artificial Intelligence in Marketing Market By OfferingCHAPTER 6. Artificial Intelligence in Marketing Market By Deployment ModeCHAPTER 7. Artificial Intelligence in Marketing Market By TechnologyCHAPTER 8. Artificial Intelligence in Marketing Market By ApplicationCHAPTER 9. Artificial Intelligence in Marketing Market By End UserCHAPTER 10. North America Artificial Intelligence in Marketing Market By CountryCHAPTER 11. Europe Artificial Intelligence in Marketing Market By CountryCHAPTER 12. Asia Pacific Artificial Intelligence in Marketing Market By CountryCHAPTER 13. Latin America Artificial Intelligence in Marketing Market By CountryCHAPTER 14. Middle East & Africa Artificial Intelligence in Marketing Market By CountryCHAPTER 15. Player Analysis Of Artificial Intelligence in Marketing MarketCHAPTER 16. Company ProfileGlobal Artificial Intelligence in Marketing Industry Segment AnalysisArtificial Intelligence AI in Marketing Market By Deployment Mode·Cloud·On-premiseArtificial Intelligence AI in Marketing Market By Technology·Machine Learning·Computer Vision·Natural Language Processing·Context-Aware ComputingArtificial Intelligence AI in Marketing Market By Application·Social Media Advertising·Content Curation·Search Advertising·Analytics Platform·Sales & Marketing Automation·OthersArtificial Intelligence AI in Marketing Market By End User·BFSI·Consumer Goods·Retail·Enterprise·Media & Entertainment·OthersRegional Market InsightsThe artificial intelligence in marketing market exhibits varying dynamics across different regions. North America leads the market, driven by the presence of tech giants, innovative startups, and a strong ecosystem of AI research and development. The region's advanced infrastructure, digital maturity, and high adoption rates of AI technologies in marketing contribute to its dominance in the global market.Europe is also a significant market for artificial intelligence in marketing, with countries like the UK, Germany, and France at the forefront of AI innovation and adoption. Asia-Pacific, particularly China and India, presents immense growth opportunities fueled by rapid digitalization, expanding e-commerce markets, and increasing investments in AI technology.
Having the most efficient clinical workflows with enhanced diagnostic capabilities is a major goal for clinicians and referring physicians.They need fast, secure access to both patient data and their diagnostic or viewing tool set. But the cost barriers and the complexity of owning and maintaining healthcare information systems can compromise the quality of care.To address this challenge, Philips recently partnered with Amazon Web Services (AWS) to develop Philips HealthSuite Imaging — a new radiology cloud service that provides on-demand access to advanced medical imaging software.Philips and AWS are building on their relationship, advancing artificial intelligence (AI) in healthcare to accelerate the development of cloud-based generative AI applications that provide clinical decision support, help enable more accurate diagnoses, and automate administrative tasks.“With healthcare systems under increasing pressure, the focus of clinicians’ has shifted from technical specifications toward more efficient workflows that lead to accurate diagnoses – and that’s what we are delivering here,” said Shez Partovi, Chief Innovation and Strategy Officer and Business Leader Enterprise Informatics at Philips.Comprehensive Enterprise ImagingThe availability of Philips HealthSuite Imaging on AWS is a new addition to Philips’ broad capabilities in enterprise informatics, enabling improved image access speeds, reliability and data orchestration for radiologists and clinicians across the entire imaging workflow – from diagnosis to therapy selection, treatment and follow-up.With Philips HealthSuite Imaging, clinicians can access the latest innovations from any location, and healthcare organizations can reduce costs previously invested in on-premises hardware or data centers to host their image management platform.This can help clinicians manage growing workloads amid staff shortages and speed time to diagnosis and treatment, enhancing patient outcomes. Other key benefits that Philips HealthSuite Imaging offers include:Effective remote workflows and reading: Create a virtual community of healthcare professionals to enhance patient care. Read, report and collaborate from virtually anywhere without compromising security, efficiency or quality.Business continuity: Mission critical imaging departments can be protected with alternative resilience plans that deliver an “always on” service.Security: Protect the organization’s technology from becoming obsolete by outsourcing IT infrastructure while keeping it secure and scalable.Scalable and Future ready: The ability to scale up and add services any time with continuous access to the latest features.According to Madhuri Sebastian, Business Leader, Philips Radiology Informatics, Philips decided to focus on the cloud for radiology, “To liberate diagnostic radiology from on premise to being accessible from anywhere so you can read, report and collaborate from anywhere. This really allows us to deepen our clinical collaboration between the radiologists and clinicians.”Sebastian said the cloud helps “unburden the health systems from all for the work they have to do to manage and maintain their IT systems by themselves.” This helps eliminate capital spikes, the time and cost associated with upgrades and tech refreshes. “It now can all be done ubiquitously in the cloud.”Philips currently operates in more than 1,000 sites around the world and manages and maintains more than 60 billion images, “so we know what it means to operate at scale in the cloud,” Sebastian said.“What we are really focused on is how do we take the efficiencies from the cloud into the product? How does the product become a much simpler solution for radiologists to use?”Scalability, Security and AccessibilitySharing large datasets over the cloud without compromising quality is a complex task and Philips HealthSuite Imaging can help simplify that task.Matthieu Ferrant, Product Management Leader, Philips Radiology Informatics, said that Philips’ experience with scaling systems and ensuring uptime quality of service helps ease the complexity. “What we are doing is leveraging the power of AWS to scale, and to make sure that the accessibility of that powerful network is also brought to our end customers and to make sure that this high availability is truly serving the customers.“We also ensure that everything is duplicated, so [the users] never have a reason to be concerned. If one link goes down, you get another link — that’s the power of AWS linking that we enable through Philips HealthSuite Imaging.”According to Ferrant, even when there may be connectivity issues due to a rural location or network availability, “we always have business continuity options to ensure we alleviate any variability in the network connectivity that a customer might have.”“We have a very vast experience of scaling and ensuring that you have operational uptime and effective clinical care at all times when we are operating out of the cloud.”Philips also has a great deal of experience helping customers transition to new systems, Ferrant said. “Upgrading [users] is something that is in our DNA. Transferring to the cloud is something that comes very naturally to us with the expertise that we have built. Thanks to our partnership with AWS, we can actually do that at scale and ensure that we can transition customers at a much faster pace than we have ever done before … and that pace is going to [continue] to accelerate.”Ferrant said Philips provides a specific path to the cloud for its Vue PACS and IS Radiology PACS customers. “We do have dedicated paths that allow customers to take advantage of the fast, transparent and seamless transition from their current system to our HealthSuite Imaging offering in the cloud. We know our customers well. We know their workflows. We are going to make sure that their transition to HealthSuite Imaging in the cloud is seamless and will ensure continued clinical care without any disruption.”Removing the BurdenThe Philips and Amazon Web Services partnership helps improve processing speed, stability, security and mobility, while helping clinician and referring physicians leverage new machine learning and AI capabilities.“For Philips, it is about deepening the workflows and deepening the collaboration that clinicians can do, unlocking AI and simplifying,” Madhuri said. “If we can orchestrate the efficiencies, that’s when we can really unburden the health systems from all of the headaches they have today managing [their] systems and then improve the quality of care while lowering their total cost of ownership.”
