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ESMA produce cloud outsourcing guidance for investment banks and service providers – Out-Law.com

The ESMA guidelines also set out a list of contractual requirements to be included in contracts with cloud suppliers. This list likewise differentiates between arrangements for critical and important functions and those for non-critical and important functions.

While the EBA's guidelines make clear that institutions must insist that cloud providers ensure that sub-outsourcer's grant the "same contractual rights of access and audit as those granted by the service provider", this obligation is not explicitly set out by ESMA. However, cloud providers must ensure that the contractual rights ensure that all contractual obligations between the cloud provider and the regulated entity "are continuously met".

Specific differences between the EBA's guidelines and those developed by ESMA on information security, auditing rights, data locations and exit exist too.

Guideline 4 requires information security requirements to be included within the cloud outsourcing written agreements. For critical and important functions, on a risk-based approach, a list of requirements is to be complied with.

While the nature of what is set out in this list is broadly similar to those specified by the EBA and EIOPA, the detail is different.

Unique to the ESMA draft guidelines are explicit requirements to:

It is not clear why ESMA has called out some information security measures which are not explicitly referred to in the EBA's outsourcing guidelines, but not others set out in the EBA's sister guidelines on ICT and security risk management.

Regulated entities are also asked to "consider" various matters in relation to encryption and key management, tenant isolation in shared environments, operations and network security and application programming interfaces. The detail provided in the ESMA guidelines in relation to these areas does not replicate what is outlined in the EBA and EIOPA guidelines.

Guideline 4 also requires that regulated entities ensure that the cloud service provider "complies with internationally recognised information security standards". This is slightly different to the EBA's outsourcing guidelines which require regulated entities to "ensure that service providers, where relevant, comply with appropriate IT security standards". Background information issued alongside the EBA's guidelines does, however, explain that regulated entities "must ensure that they meet internationally accepted information security standards and this also applies to outsourced IT infrastructures and services".

ESMA's guidelines make a number of references to the need for firms to know and document the locations where their data will be stored and processed in the cloud. However, some provisions refer to the need to specify where the 'countries' data is located, while others make reference to 'countries and regions'.

Regulated entities are to set out "the location(s) (namely countries) where relevant data will be stored and processed (location of data centres)", as information kept in the cloud register and shared with the regulator prior to entering into a written agreement, and within written agreements for outsourcings which relate to critical or important functions. Separately, as part of its overall approach to risk management of critical and important functions, regulated entities are to "adopt a risk-based approach to data storage and data processing location(s) (namely country or region).

During the consultation period there will be opportunity to clarify whether ESMA intends regulated entities to keep track of the individual countries within regions, such as the EU or EEA, where data is stored, or whether this is an unintentional oversight in the text. Clarity on this point is important some cloud providers may not want to reveal the specific country data is stored in.

The ESMA guidelines are broadly in line with the EBA's guidelines in respect of the provision institutions must make for exiting from cloud outsourcing arrangements. However, there are some technical differences in the language used.

Exit plans need to be updated if an outsourced function changes. The written outsourcing agreement also needs to set out "an obligation for the CSP to orderly transfer the outsourced function and all the related data from the CSP and any sub-outsourcer to another CSP indicated by the firm or directly to the firm in case the firm activates the exit strategy."

Taken literally, this requirement is broader than that set out by the EBA as the cloud provider must transfer all of the related data, not only that data which is relevant or will be useful to the regulated entity in the future.

The ESMA guidelines only address the auditing rights institutions must secure from cloud providers for themselves and regulators in the context of critical and important outsourcing arrangements. The requirements are broadly similar to those set out in the EBA's guidelines.

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How Crowdfunding platforms help businesses cushion the effects of the COVID-19 crisis – Born2Invest

Germany is gradually returning to normal, but this can hardly compensate for the economic damage in many places. This means that municipal crowdfunding platforms are in greater demand than ever before.

It was only a few days after the corona lockdown that municipal utilities and municipal suppliers realized the potential their crowdfunding platforms offer for restaurateurs, retailers and clubs to cushion the financial crisis caused by the pandemic.

Without further ado, numerous crowdfunding platforms relaxed their access requirements so that those affected can be helped quickly and without the administrative burden associated with local government funds. As the Leipzig Group emphasisez: The current situation presents us all with major challenges including associations and initiatives. Solidarity and support is needed more than ever. And since we as the Leipzig Group understand services of general interest to mean much more than just the supply of energy, mobility and water, it is a matter close to our hearts to prove once again that we are there for Leipzig.

The people of Leipzig have put in a strong showing. $56.000 (50,000) was raised for the sports club SC DHfK Leipzig alone to cover the running costs and to maintain the offer for post-corona times.

Potsdam, Jena, Oberhausen and also the people in the Wemag network area showed similar solidarity. The willingness to support the boards in the region stood out in particular. There were several project inquiries on this topic, one of which made it onto the platform of the Schwerin-based company. Instead of the planned $565 (500), $5,436 (4,815) was collected. The entire WEMAG promotion pot flowed into this project in April. It is a great example of how several small amounts can make a big difference and how well the platform works to find many supporters quickly and easily, said Wemag.

One of the decisive factors for the success of the municipal crowdfunding projects as a Corona emergency aid measure was the rapid, technical adaptation of the platforms. It did not even take 24 hours until the Wemag crowdfunding platform was open to all those affected. However, it was not only the old hands of the municipal crowd funding that showed solidarity; VKU members without their own crowd also supported crisis-stricken restaurateurs, retailers and clubs. This was made possible through the national crowdfunding platform of VKU Verlag (https://www.kommunales-crowdfunding.de/).

Under the motto Crowd against Corona, not only all projects of the 20 existing crowdfunding platforms are advertised on the national platform, but also municipal utilities and suppliers without their own crowd funding platform can use the offer of VKU Verlag. Project starters can apply directly to the national platform, and once the project goes online, it is played out via a widget on the respective homepage of the municipal company. To increase the chances of successful financing, the projects are assigned to the respective crowd newcomers by postcode.

In the meantime, according to Torsten Lhrs, managing director of fairplaid, more than 30 partners have accepted the free offer of help and helped to develop it further in order to help as many associations and institutions in the regions as possible. They are using promotion pots and promotion forms at a value of over $135,500 (120,000). Together with our team, we are now working on these projects on a daily basis in order to prepare them in the best possible way and make them successful.

This has already worked for Stadtwerke Troisdorf, which has already successfully supported two projects. While the tennis club TCT Haus Rott has collected almost $2,200 (2,000) for a hygiene tower, the citys womens center can now build up hygiene protection walls with over $565 (500). Through crowdfunding, maximum coverage is achieved and attention is drawn to the population. An association or a company alone would not be able to achieve this on its own, said the Troisdorf resident.

In addition, the success proves how well the system is received by the people on site. The VKU Verlag has opened its national platform for the time being until the end of July. The Troisdorf-based company wants to use this time to test crowdfunding as a new sponsoring approach, as this is the only way to find out the advantages and disadvantages of the previous model.

Stadtwerke Speyer also uses the offer of VKU Verlag and plans to publish a project on its homepage soon: With crowdfunding, Stadtwerke Speyer is breaking new ground. What particularly impressed us about crowdfunding is that we are not the sole donor, but are supporting a project sponsor who wants to convince people of his project. Projects that involve several people are usually associated with strong emotional ties, creativity and innovation. Qualities that also go well with Stadtwerke Speyer.

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(Featured image by tvjoern via Pixabay)

DISCLAIMER: This article was written by a third party contributor and does not reflect the opinion of Born2Invest, its management, staff or its associates. Please review our disclaimer for more information.

This article may include forward-looking statements. These forward-looking statements generally are identified by the words believe, project, estimate, become, plan, will, and similar expressions. These forward-looking statements involve known and unknown risks as well as uncertainties, including those discussed in the following cautionary statements and elsewhere in this article and on this site. Although the Company may believe that its expectations are based on reasonable assumptions, the actual results that the Company may achieve may differ materially from any forward-looking statements, which reflect the opinions of the management of the Company only as of the date hereof. Additionally, please make sure to read these important disclosures.

First published in ZfK, a third-party contributor translated and adapted the article from the original. In case of discrepancy, the original will prevail.

Although we made reasonable efforts to provide accurate translations, some parts may be incorrect. Born2Invest assumes no responsibility for errors, omissions or ambiguities in the translations provided on this website. Any person or entity relying on translated content does so at their own risk. Born2Invest is not responsible for losses caused by such reliance on the accuracy or reliability of translated information. If you wish to report an error or inaccuracy in the translation, we encourage you to contact us.

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8 Ways Your Business Can Benefit From Machine Learning – MarketScale

With the rise of artificial intelligence solutions, machine learning is also growing rapidly in the world of business. Machine Learning is a subfield of artificial intelligence where algorithms are constantly learning and improving themselves. Its able to do so by processing huge amounts of data. Just like the human brain, it can learn from observation and make smarter decisions. The more data it has, the smarter it gets.

Machine learning can help improve your processes and streamline your business in the wake of the COVID-19 pandemic. Here are 8 ways that your business can benefit from machine learning.

Machine learning can analyze past customer behavior and make sales predictions based on it. As a business owner, no money goes wasted purchasing unnecessary inventory. They simply fill orders based on the amount forecasted by the machine.

Studying previous sales data can help machine learning technology to provide better recommendations to business owners. As a result, customers get the right offers at the right time. This means more sales without having to plan or bet on ads.

Machine learning takes the guesswork out of marketing. By processing huge amounts of data, it can identify highly relevant variables that businesses may have overlooked. This allows you to create more targeted marketing campaigns that customers are more likely to engage with.

Data entry is one of the easier tasks for a business but because its so repetitive, its more vulnerable to errors. This can be avoided with the help of machine learning which not only processes data fast but also does it accurately. This allows skilled human employees to focus more on meaningful tasks and provide extra value to your organization.

Email providers used to fight spam using rule-based programming. It remained problematic for a while since it did not properly catch all spam emails coming into inboxes. Machine learning today can detect spam more accurately using neural networks to get rid of junk and phishing emails. It does so by constantly identifying new threats and trends across the network.

Machine learning can produce smart assistants which can improve productivity in the workplace. For example, we now have intelligent virtual assistants who can transcribe and schedule meetings.

This is especially important for manufacturing firms where maintenance is completed regularly. failing to maintain equipment in a timely and accurate way can be very costly. With machine learning, factories can gain insights and patterns which might have been overlooked before. This reduces the chances of failure and increases productivity in manufacturing.

Your business can make more informed decisions with machine learning since it can process massive amounts of data in a short amount of time. All too often, entrepreneurs take weeks or months to create a meaningful marketing plan. Machine learning eliminates the guesswork and provides accurate insights into the business. This allows entrepreneurs to take actionable data and make decisions that can help the business succeed.

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Breaking Down COVID-19 Models Limitations and the Promise of Machine Learning – EnterpriseAI

Every major news outlet offers updates on infections, deaths, testing, and other metrics related to COVID-19. They also link to various models, such as those on HealthData.org, from The Institute for Health Metrics and Evaluation (IHME), an independent global health research center at the University of Washington. Politicians, corporate executives, and other leaders rely on these models (and many others) to make important decisions about reopening local economies, restarting businesses, and adjusting social distancing guidelines. Many of these models possess a shortcomingthey are not built with machine learning and AI.

Predictions and Coincidence

Given the sheer numbers of scientists and data experts working on predictions about the COVID-19 pandemic, the odds favor someone being right. Like the housing crisis and other calamitous events in the U.S., someone took credit for predicting that exact event. However, its important to note the number of predictors. It creates a multiple hypothesis testing situation where the higher number of trials increases the chance of a result via coincidence.

This is playing out now with COVID-19, and we will see in the coming months many experts claiming they had special knowledge after their predictions proved true. There is a lot of time, effort, and money invested in projections, and the non-scientists involved are not as eager as the scientists to see validation and proof. AI and machine learning technologies need to step into this space to improve the odds that the right predictions were very educated projections based on data instead of coincidence.

Modeling Meets its Limits

The models predicting infection rates, total mortality, and intensive care capacity are simpler constructs. They are adjusted when the conditions on the ground materially change, such as when states reopen; otherwise, they remain static. The problem with such an approach lies partly in the complexity of COVID-19s different variables. These variables mean the results of typical COVID-19 projections do not have linear relationships with the inputs used to create them. AI comes into play here, due to its ability to ignore assumptions about the ways the predictors building the models might assist or ultimately influence the prediction.

Improving Models with Machine Learning

Machine Learning, which is one way of building AI systems, can better leverage more data sets and their interrelated connections. For example, socioeconomic status, gender, age, and health status can all inform these platforms to determine how the virus relates to current and future mortality and infections. Its enabling a granular approach to review the impacts of the virus for smaller groups who might be in age group A and geographic area Z while also having a preexisting condition X that puts people in a higher COVID-19 risk group. Pandemic planners can use AI in a similar way as financial services and retail firms leverage personalized predictions to suggest things for people to buy as well as risk and credit predictions.

Community leaders need this detail to make more informed decisions about opening regional economies and implementing plans to better protect high-risk groups. On the testing front, AI is vital for producing quality data that are specific for a city or state and takes into account more than just basic demographics, but also more complex individual-based features.

Variations in testing rules across the states require adjusting models to account for different data types and structures. Machine learning is well suited to manage these variations. The complexity of modeling testing procedures means true randomization is essential for determining the most accurate estimates of infection rates for a given area.

The Automation Advantage

The pandemic hit with crushing speed, and the scientific community has tried to quickly react. Enabling faster movement with modeling, vaccine development, and drug trials is possible with automated AI and machine learning platforms. Automation removes manual processes from the scientists day, giving them time to focus on the core of their work, instead of mundane tasks.

According to a study titled Perceptions of scientific research literature and strategies for reading papers depend on academic career stage, scientists spend a considerable amount of time reading. It states, Engaging with the scientific literature is a key skill for researchers and students on scientific degree programmes; it has been estimated that scientists spend 23% of total work time reading. Various AI-driven platforms such as COVIDScholar use web scrapers to pull all new virus-related papers, and then machine learning is used to tag subject categories. The results are enhanced research capabilities that can then inform various models for vaccine development and other vital areas. AI is also pulling insights from research papers that are hidden from human eyes, such as the potential for existing medications as possible treatments for COVID-19 conditions.

Machine learning and AI can improve COVID-19 modeling as well as vaccine and medication development. The challenges facing scientists, doctors, and policy makers provide an opportunity for AI to accelerate various tasks and eliminate time-consuming practices. For example, researchers at the University of Chicago and Argonne National Laboratory collaborated to use AI to collect and analyze radiology images in order to better diagnose and differentiate the current infection stages for COVID-19 patients. The initiative provides physicians with a much faster way to assess patient conditions and then propose the right treatments for better outcomes. Its a simple example of AIs power to collect readily available information and turn it into usable insights.

Throughout the pandemic, AI is poised to provide scientists with improved models and predictions, which can then guide policymakers and healthcare professionals to make informed decisions. Better data quality through AI also creates strategies for managing a second wave or a future pandemic in the coming decades.

About the Author

PedroAlves is the founder and CEO of Ople.AI,a software startup that provides an Automated Machine Learning platform to empower business users with predictive analytics.

While pursuing his Ph.D. in ComputationalBiology from Yale University, Alves started his career as a data scientist and gained experience in predicting, analyzing, and visualizing data in the fields of social graphs, genomics, gene networks, cancer metastasis, insurance fraud, soccer strategies, joint injuries, human attraction, spam detection and topic modeling among others. Realizing that he was learning by observing how algorithms learn from processing different models, Alves discovered that data scientists could benefit from AI that mimics this behavior of learning to learn to learn. Therefore, he founded Ople to advance the field of data science and make AI easy, cheap, and ubiquitous.

Alves enjoys tackling new problems and actively participates in the AI community through projects, lectures, panels, mentorship, and advisory boards. He is extremely passionate about all aspects of AI and dreams of seeing it deliver on its promises; driven by Ople.

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What is machine learning, and how does it work? – Pew Research Center

At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.

In a digital world full of ever-expanding datasets like these, its not always possible for humans to analyze such vast troves of information themselves. Thats why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.

Our latest video explainer part of our Methods 101 series explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about how weve used machine learning and other computational methods in our research, including the analysis mentioned in this video, you can explore recent reports from our Data Labs team.

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Pursue a future in big data and machine learning with these classes – Mashable

Products featured here are selected by our partners at StackCommerce.If you buy something through links on our site, Mashable may earn an affiliate commission.All instructorscome from solid technical backgrounds.

Image: pexels

By StackCommerceMashable Shopping2020-06-05 19:43:23 UTC

TL;DR: Get involved with the world's most valuable resource data, of course with The Complete 2020 Big Data and Machine Learning Bundle for $39.90, a 96% savings as of June 5.

Big data has gotten sobigthat the adjective doesn't even do it justice any longer. If anything, it should be described as gargantuan data, given how the entire digital universe is expected to generate 44 zettabytes of data by the end of this year. WTF is zettabytes? It's equal to one sextillion (1021) or270 bytes. It's a lot.

It's never been clearer that data is the world's most valuable resource, making now an opportune time to get to grips with all things data. The Complete 2020 Big Data and Machine Learning Bundle can be your springboard to exploring a career in data science and data analysis.

Big Data and Machine Learning are intimidating concepts, which is why this bundle of courses demystifies them in a way that beginners will understand. After you've familiarized yourself with foundational concepts, you will then move onto the nitty-gritty and get the chance to arm yourself with skills including analyzing and visualizing data with tools like Elastisearch, creating neural networks and deep learning structures with Keras, processing a torrential downpour of data in real-time using Spark Streaming, translating complex analysis problems into digestible chunks with MapReduce, and taming data using Hadoop.

Look, we know all this sounds daunting, but trust that you'll be able to learn and synthesize everything, all thanks to the help of expert instructors who know their stuff.

For a limited time, you can gain access to the bundle on sale for only $39.90.

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Machine learning can give healthcare workers a ‘superpower’ – Healthcare IT News

With healthcare organizations around the world leveraging cloud technologies for key clinical and operational systems, the industry is building toward digitally enhanced, data-driven healthcare.

And unstructured healthcare data, within clinical documents and summaries, continues to remain an important source of insights to support clinical and operational excellence.

But there are countless nuggets of important unstructured data something that does not lend itself to manual search and manipulation by clinicians. This is where automation comes in.

Arun Ravi, senior product leader at Amazon Web Services is copresenting a HIMSS20 Digital presentation on unstructured healthcare data and machine learning, Accelerating Insights from Unstructured Data, Cloud Capabilities to Support Healthcare.

There is a huge shift from volume- to value-based care: 54% of hospital CEOs see the transition from volume to value as their biggest financial challenge, and two-thirds of the IT budget goes toward keeping the lights on, Ravi explained.

Machine learning has this really interesting role to play where were not necessarily looking to replace the workflows, but give essentially a superpower to people in healthcare and allow them to do their jobs a lot more efficiently.

In terms of how this affects health IT leaders, with value-based care there is a lot of data being created. When a patient goes through the various stages of care, there is a lot of documentationa lot of datacreated.

But how do you apply the resources that are available to make it much more streamlined, to create that perfect longitudinal view of the patient? Ravi asked. A lot of the current IT models lack that agility to keep pace with technology. And again, its about giving the people in this space a superpower to help them bring the right data forward and use that in order to make really good clinical decisions.

This requires responding to a very new model that has come into play. And this model requires focus on differentiating a healthcare organizations ability to do this work in real time and do it at scale.

How [do] you incorporate these new technologies into care delivery in a way that not only is scalable but actually reaches your patients and also makes sure your internal stakeholders are happy with it? Ravi asked. And again, you want to reduce the risk, but overall, how do you manage this data well in a way that is easy for you to scale and easy for you to deploy into new areas as the care model continues to shift?

So why is machine learning important in healthcare?

If you look at the amount of unstructured data that is created, it is increasing exponentially, said Ravi. And a lot of that remains untapped. There are 1.2 billion unstructured clinical documents that are actually created every year. How do you extract the insights that are valuable for your application without applying manual approaches to it?

Automating all of this really helps a healthcare organization reduce the expense and the time that is spent trying to extract these insights, he said. And this creates a unique opportunity, not just to innovate, but also to build new products, he added.

Ravi and his copresenter, Paul Zhao, senior product leader at AWS, offer an in-depth look into gathering insights from all of this unstructured healthcare data via machine learning and cloud capabilities in their HIMSS20 Digital session. To attend the session, click here.

Twitter:@SiwickiHealthITEmail the writer:bill.siwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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How to choose between rule-based AI and machine learning – TechTalks

By Elana Krasner

Companies across industries are exploring and implementing artificial intelligence (AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According to McKinsey, embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth. But with that promise comes challenges.

Computers and machines dont come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they need to carry out tasks like driving a car or diagnosing a disease?

There are multiple ways to achieve AI, and existential to them all is data. Without quality data, artificial intelligence is a pipedream. There are two ways data can be manipulatedeither through rules or machine learningto achieve AI, and some best practices to help you choose between the two methods.

Long before AI and machine learning (ML) became mainstream terms outside of the high-tech field, developers were encoding human knowledge into computer systems as rules that get stored in a knowledge base. These rules define all aspects of a task, typically in the form of If statements (if A, then do B, else if X, then do Y).

While the number of rules that have to be written depends on the number of actions you want a system to handle (for example, 20 actions means manually writing and coding at least 20 rules), rules-based systems are generally lower effort, more cost-effective and less risky since these rules wont change or update on their own. However, rules can limit AI capabilities with rigid intelligence that can only do what theyve been written to do.

While a rules-based system could be considered as having fixed intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. There is still a layer of underlying rules, but instead of a human writing a fixed set, the machine has the ability to learn new rules on its own, and discard ones that arent working anymore.

In practice, there are several ways a machine can learn, but supervised trainingwhen the machine is given data to train onis generally the first step in a machine learning program. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.

The anticipated benefits to AI are high, so the decisions a company makes early in its execution can be critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to achieve. What problems are you trying to solve, or challenges are you trying to meet?

The decision to implement a rules-based or machine learning system will have a long-term impact on how a companys AI program evolves and scales. Here are some best practices to consider when evaluating which approach is right for your organization:

When choosing a rules-based approach makes sense:

When to apply machine learning:

The promises of AI are real, but for many organizations, the challenge is where to begin. If you fall into this category, start by determining whether a rules-based or ML method will work best for your organization.

About the author:

Elana Krasner is Product Marketing Manager at 7Park Data, a data and analytics company that transforms raw data into analytics-ready products using machine learning and NLP technologies. She has been in the tech marketing field for almost 10 years and has worked across the industry in Cloud Computing, SaaS and Data Analytics.

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Machine Learning Tools Used By The Kaggle Experts – Analytics India Magazine

There isnt a dearth of ML tools today. However, for a beginner, to know about the tool stack of those who win Kaggle competitions consistently is of great help. One can later go ahead and pick the tool of their choice. In the next section, we look at the top tools, frameworks, cloud services, libraries used by the Kaggle masters and Grand Masters, which they revealed to us in our exclusive interviews. That said, we have to admit that all these top Kagglers are of the opinion that one should not fall in love with tools, and it is all right as long any tools get the job done right!

4x Kaggle GM, Abhishek Thakur says that he frequently finds himself using TensorFlow for NLP problems and PyTorch for computer vision problems.

When it comes to favourite Python libraries, Thakur is in praise for Scikit-learn and how significant this library is in providing many necessary components to put a model into production.

Thakur, however, believes that there isnt a shortage of libraries or frameworks one can use these days, and its all good as long as one understands what is happening in the background.

Arthur says that a basic laptop would sometimes suffice. However, sometimes he rents some GPUs of Google cloud platform with Kaggle vouchers, depending on the competition.

Here is what Arthurs toolkit looks like:

A Kaggle master ranked in the top 20 in the competitions leaderboard, Mathurin says that he prefers Python to R, though he had been using R until 2015. Mathurin who has been in this field for over a decade and a half, his renewed interest in algorithms made him switch to Python gradually.

A look at Mathurins toolkit, which he keeps coming back to:

Duc, who is ranked in the world top 50 and also a chief data engineer and co-founder of the Vietnamese AI startup, Palexy, says that he and his team usually use one server with 2x1080Ti with a Kaggle kernel. For a competition like DeepFake, he prefers renting a server with 4x1080Ti on AWS.

Talking about frequently used tools, Duc said that he usually finds himself using Keras-TensorFlow, OpenCV, albumentation, lgbm, scikit-learn. A data engineer by profession, Duc says that the role of a data engineer is collecting data and preparing the data pipeline, and for a data engineering team to build the necessary infrastructure and architecture for data generation, they use SQL, MySQL, Spark, Hadoop, Hive, etc.

Whereas, in case of a data scientist who is responsible for obtaining insights from data and formulating these insights into a model to communicate with the clients, data scientists use statistics, visualisation (matplotlib, seaborn), modeling (sklearn, TensorFlow, PyTorch), etc

An AI engineer and a grandmaster, Darragh usually runs code off the command line and Spyder IDE and mainly leverages AWS and prototypes on his Macbook Pro, which he believes, is enough to check if a pipeline is working well before deploying. Regarding the frameworks, Darragh has expressed his liking for PyTorch over other frameworks for the kind of freedom it offers to experiment compared to others.

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Machine Learning as a Service (MLaaS) Market Size, Share & Trends Analysis Report By Product Types, And Applications Forecast To 2026 – 3rd Watch…

GlobalMarketers.biz presents an updated and Latest Study on Machine Learning as a Service (MLaaS) Market 2020-2026. The report comprises market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While focusing on the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.

It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

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Key market Players of Machine Learning as a Service (MLaaS):

Yottamine AnalyticsErsatz Labs, Inc.GoogleSift Science, Inc.MicrosoftBigMLAmazon Web ServicesIBMHewlett PackardAT&TFuzzy.aiHypergiant

Global Machine Learning as a Service (MLaaS) Market is the title of an upcoming market research report at Globalmarketers. The market has been studied in depth to present vital data and information, including revenue share of each segment, region, and country, revenue growth driving factors, and restraints. In addition, potential revenue opportunities in untapped regions and economies, and threats are included. Key players and their details are presented in the company profile section of the report. The section comprises revenue and financial information and details, recent developments, strategies, acquisitions and mergers, and geographic reach and footprint. The global Machine Learning as a Service (MLaaS) market is segmented by product type, distribution channel, and regions and countries.

Global Machine Learning as a Service (MLaaS) Market Segmentation:

By Product Type:

Cloud and Web-based Application Programming Interface (APIs)Software ToolsOthers

By End-User

Cloud and Web-based Application Programming Interface (APIs)Software ToolsOthers

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The Questions Answered by Machine Learning as a Service (MLaaS) Market Report:

What are the Key Manufacturers, raw material suppliers, equipment suppliers, end users, traders and distributors in the Machine Learning as a Service (MLaaS) Market?

What are Growth factors influencing Machine Learning as a Service (MLaaS) Market Growth?

What are production processes, major issues, and solutions to mitigate the development risk?

What is the Contribution from Regional Manufacturers?

What are the Market opportunities and threats faced by the vendors in the global Machine Learning as a Service (MLaaS) Industry?

What are the Key Market segments, market potential, influential trends, and the challenges that the market is facing?

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Global Machine Learning as a Service (MLaaS) MarketRegional Analysis:

The Europe market is expected to account for majority revenue share over the forecast period owing to increasing demand for premium products in countries such as the Scotland, Italy, and Germany. The Asia Pacific market is expected to register a steady growth rate in the foreseeable future. China accounts for major production and exports of Machine Learning as a Service (MLaaS). Domestic consumption is also highest in the country. Chinas improving and rapidly growing economy in recent years and rising standard of living is projected to further support market growth.

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Machine Learning as a Service (MLaaS) Market Size, Share & Trends Analysis Report By Product Types, And Applications Forecast To 2026 - 3rd Watch...

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