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Cloud vs in-house disaster recovery – Times of Malta

More and more companies of all sizes are moving to the cloud, but why is it time to also move disaster recovery systems to the cloud?

Partnering with a provider that meets their disaster-recovery (DR) needs will allow organisations to protect themselves from threats such as system failures and focus on growing their business rather than addressing unknown risk factors. One of the benefits of using disaster recovery as a service is that one does not have to invest money and resources in owning and maintaining a disaster recovery environment on the ground. It may be tempting to implement every step of a disaster rescue plan in-house, but smaller companies that lack a dedicated IT team may find it easier to use a third-party solution.

Cloud computing is cheap because of its economy of scale, and outsourced tasks usually gives one exactly what they need. The plot thickens for companies that use software as a service (SaaS) provider, which in turn relies on third-party cloud providers to host their services.

There are also several smaller, lesser-known players who focus much of their efforts on providing high-quality disaster recovery services (DRaaS), but there is a shortage of them. A single point disaster can weaken ones business, and a backup and logging service is extremely important if one needs to perform disaster recovery after a failure and see where something went wrong.

DRAAS can be a great option for small- and medium-sized enterprises that lack the expertise to test an effective disaster and recovery plan. Proper management of the location and nature of the backups, as well as the availability of backup data, can cause a single point of failure or disaster that can weaken the company.

A disaster can also affect a wide geographical area, which means that backups can be affected even if they are in the same region as the main office.

If one wants to use the cloud for DR/business continuity (BC) planning, there are some problems they need to face. For disaster recovery, this means how critical business applications behave in a cloud environment. If one relies on cloud disaster and recovery software, they also need to examine the specifics of what they are buying. These tests not only help to know whether the disaster recovery plan is working but can also help in gaining insight into problems that can occur during a disaster.

It is a misconception that one does not have to worry about resilience and recovery when one deploys their workloads in the cloud. While such services exist, there is certainly much more to consider before an organisation can be considered safe. It is important to note that while cloud providers have certain responsibilities, companies and cloud customers are responsible for planning an effective disaster recovery strategy. One probably has a plan to protect their company data, employees and businesses. Management will feel safer knowing that one knows the risks and has adapted their disaster and recovery plan accordingly.

This is one of the main reasons why an emergency rescue plan is needed for both cloud services and in-house services, as well as cloud providers and cloud customers. Other problems that could put a business in a bad situation if it is not prepared include lack of access to critical infrastructure and other critical resources such as backup and recovery equipment, and other issues.

Basically, the nature of the cloud makes it less secure than traditional options, and there needs to be more preparation to ensure security on the software platform and infrastructure level to ensure security. While the cloud provides excellent disaster-recovery capabilities, it is not a cheap alternative to the in-house approach. If ones environment is already in the cloud, it may be useful to use a cloud provider as an option to restore data. One can work with their cloud disaster recovery partner to implement the design and set up the disaster recovery infrastructure. Cloud DR partners have access to a wide range of resources, including data centres, cloud servers, storage and network infrastructure, and disaster management tools.

While backing up important data is an integral part of a companys IT strategy, backing up is not the same as having an emergency plan. The last thing one wants to find out is that their backups failed at a time when they lost their data. That is why it is critical for cloud providers to define exactly what their policies are when it comes to the backup process and the disaster recovery.

This article was prepared by collating various publicly available online sources.

Claude Calleja, Executive, eSkills Malta Foundation

Independent journalism costs money. Support Times of Malta for the price of a coffee.

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Mental chess is out of this world – The Guardian

Nice to know Ive been ahead of the trend for cottage gardens since the first one I had with my late husband in 1982 (Whats the buzz? Why the cottagecore garden trend is great for bees and biodiversity, 5 April). None of the regimented, tidy rows with gaps needing weeding; every bit of soil is covered and there is colour from early Januarys first snowdrops right through to the last roses in December. A haven for birds, bees, butterflies, my dog and me. Sharman Finlay Portrush, County Antrim, Northern Ireland

If we dont know whether or not fish can feel pain (Letters, 7 April), surely the right thing to do is err on the side of caution? To think that they might feel pain and continue doing what we do is so obviously wrong. Dave Gunner Copthorne, West Sussex

All this talk of solving imaginary crosswords (Letters, 7 April) reminds me of a visit to the Lake District in the 1970s. I walked past a house that I learned belonged to the astronomer Sir Fred Hoyle. Ah yes, said the barman in the local pub, Sir Fred and a friend of his often sit over there playing mental chess. I was very impressed. Stephen Newbould Birmingham

On 2 November, in the spirit of recent correspondence, shouldnt we celebrate 14,600 days since Steve Bells first If ... cartoon rather than 40 years (Letters, 8 April)? Toby Wood Peterborough

I hope before taking the shot that hit his dad on the 7th at Augusta Rory McIlroy shouted forefather (Report, 8 April). Michael Cunningham Wolverhampton

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Harvard Data Science Initiative Upcoming Events | Department of Biostatistics | Harvard TH Chan School of Public Health – HSPH News

Industry Seminar: Juan M. Lavista Ferres, MicrosoftThursday, April 15, 2021 | 1:30pm to 2:30pm ET |Registration

Everything you always wanted to know about AI and AI For Good* but were afraid to ask

The Microsoft AI for Good Research Lab is a philanthropic team of data scientists and applied researchers dedicated to using AI, Machine Learning, and statistical modeling to tackle some of humanitys most significant challenges. We partner with leading nonprofits, research institutions, NGOs, and governments to accelerate work across the AI for Good program portfolioEarth, Accessibility, Humanitarian Action, Cultural Heritage, Healthas well as other pressing issues such as affordable housing, broadband access, digital skills, justice reform, legal compliance, etc. This talk will describe the type of work we do, the lessons we learned, the impact, and the pitfalls.

Juan M, Lavista Ferres is currently the General Manager and Lab Director of the Microsoft AI For Good Research Lab, where he works with a team of data scientists and researchers in AI, Machine Learning and statistical modeling, working across Microsoft AI For Good efforts. These efforts includes projects in AI For Earth, AI for Humanitarian Action, AI For Accessibility and AI For Health.

Bias^2 Seminar:Yeshimabeit MilnerThursday, April 22, 2021 | 1:30pm to 2:45pm ET |Registration

Yeshimabeit Milner is the Founder & Executive Director of Data for Black Lives. She has worked since she was 17 behind the scenes as a movement builder, technologist and data scientist on a number of campaigns. She started Data for Black Lives because for too long she straddled the worlds of data and organizing and was determined to break down the silos to harness the power of data to make change in the lives of Black people. In two years, Data for Black Lives has raised over $3 million, hosted two sold out conferences at the MIT Media Lab and has changed the conversation around big data & technology across the US and globally.

As the founder of Data for Black Lives, her work has received much acclaim. Yeshimabeit is an Echoing Green Black Male Achievement Fellow, an Ashoka Fellow and joins the founders of Black Lives Matter and Occupy Wall Street in the distinguished inaugural class of Roddenberry Foundation Fellows. In 2020, Yeshimabeit was honored as a Forbes 30 under 30 social entrepreneur.

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Data scientists and technology working in tandem – TechNative

In this new age of knowledge automation, successful business strategies often depend on using skilled data scientists to enhance the advances provided by new technology

This technology will not replace people. In fact, it will serve to enhance talent by unlocking the potential for the digital workforce of the future.

For data scientists working across asset-intensive industries, and for the manufacturers and operators they serve, its therefore vital that streamlined data processes and increasingly self-sufficient technology are not seen as a threat but rather, as an opportunity to raise the value skilled workers deliver to data science applications and large-scale projects.

Enabling teams to fulfil their potential

As the roll-out of AI and advanced automation accelerates, organisations need to use these technologies both to better define the roles of employees and to add value to them. Asset-intensive manufacturers and operators need to ensure that engineers have clear job functions that play to their strengths and that internal data scientists have the freedom and flexibility to add value to the business.

In line with this, data scientists must be allowed to evolve their role in delivering success to organisations through the use of advanced technology, helping them streamline engineering and maintenance processes alike.

Todays data scientists need to understand that technology can act as an ally. Industrial AI solutions can capture knowledge from engineers and analysts in a way not previously achievable. That is especially key for industries that are digitally transforming while vast amounts of knowledge exit the workforce as workers retire. This knowledge capture will bolster the platform for learning that data science can build on, complementing the role of analysts within the organisation rather than threatening them.

Raising the value of the data scientists role

Ultimately, knowledge automation technologies should enable data scientists to focus on more strategic initiatives, positively enriching the data scientists role. For example, built-in sensors can flag anomalies or potential errors in software with pinpoint accuracy that can provide clarity to data scientists to make large-scale improvements.

Senior managers must change their mindset and start giving data scientists the opportunity to work on projects where they are likely to have a wider, more profound impact. Creating a distinction between the smaller issues that engineers should tackle and the overarching challenges that are the responsibility of data scientists also helps define job roles.

Another change of mindset that can reap significant business benefits is an organisations approach to projects. Implementations based solely on proof of concept or project scale, rarely deliver maximum impact. Larger-scale rollouts often bring faster return on investment, especially when compared with limited in-house data science projects which may not deliver value for months. The time it takes to build, tune, and deploy a data science model is often the biggest challenge for organisations that pursue an in-house approach, and scaling is not easy.

Greater clarity provides greater opportunity

This is where great advantage can be found by working side by side with a packaged outsourced solution delivered by a third-party provider or partner, as it can often bring faster time to value through ease of use, scalability and deployment speed. With greater clarity about their role and responsibilities, data scientists can harness this technology to make enhancements to processes and complex workflows that help to transform the whole organisations working model.

In other words, with technology having built the firm foundation, data scientists have the freedom to be more creative, and explore the full potential of their knowledge and expertise to deliver added value to manufacturers and operators, and to the industry as a whole.

About the Author

Matt Holland is VP EURA at Aspen Technology. AspenTech is a global leader in asset optimization software helping the worlds leading industrial companies run their operations more safely, efficiently and reliably enabling innovation while reducing waste and impact on the environment. AspenTech software accelerates and maximizes value gained from digital transformation initiatives with a holistic approach to the asset lifecycle and supply chain.

Featured image: 2020

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Data Science Institute virtual event April 16 to share data used to predict elections – Vanderbilt University News

How do television networks predict election outcomes? The virtual event A Peek Inside the NBC Decision Desk: Election 2020 scheduled for Friday, April 16, at 2 p.m. CT will provide an overview. Registration is required.

Josh Clinton, Abby and Jon Winkelried Chair and professor of political science, will discuss the data networks use to predict elections and the ways that information is used to determine which candidates will emerge victorious. The discussion is hosted by the Data Science Institute.

While the precise details of the data models are confidential trade secrets, participants will learn about some of the challenges involved in projecting races brought about by the decentralized administration of elections across the United States.

The Vanderbilt Data Science Institute accelerates data-driven research, promotes collaboration and trains future leaders. The institute brings together experts in data science methodologies and leaders in all academic disciplines to spark discoveries and to study the impact of big data on society. The institute is educating students in computational and statistical data science techniques to become future leaders in industry, government, academia and the nonprofit sector. This is the third and final discussion in the spring speaker series.

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Artificial Intelligence and Data Science Are Top of Mind as These Two Grantmakers Join Forces Inside Philanthropy – Inside Philanthropy

For some, artificial intelligence and data science are fantastic technologies that will benefit people and society. For others, theyre terrifying assaults on individual privacy and dire threats to human existence. The point is, these latest innovations are evolving fast in the hotbeds of business, science and government, and it can be difficult for regular citizens and civil society to keep up, particularly nonprofits and others involved in addressing the spectrum of societys needs. What is philanthropys role in the growth, use and regulation of these powerful and protean technologies?

These are some of the questions Vilas Dhar considers in his role as president of the Patrick J. McGovern Foundation, a relatively young grantmaker whose late founder built a fortune in publishing and industry research, tracking the expanding computer industry in the late 20th and early 21st centuries. McGoverns International Data Group published a number of popular computer industry magazines such as Computerworld, PC World and InfoWorld.

McGovern the person was a believer in technologys potential to improve society and the human condition. He is remembered for some notable philanthropic moves involving science and technology, including a $350 million pledge in 2000 that established the McGovern Institute for Brain Research at MIT.

McGovern the foundationestablished in 2015, a year after the death of its namesakeis also active in the information technology world. The foundation has so far made about $295 million in grants, in areas like tech education, climate change, digital health and pandemic response, as well as data science and AI ethics.

Now, in a move common in business but rare in the philanthropy and nonprofit world, the McGovern Foundation has augmented its powers through a high-profile merger. It recently announced that the Cloudera Foundationa philanthropy created by Silicon Valley data and AI software company Cloudera Inc. to bring data analytics technology to the nonprofit sectorhas merged its $9 million endowment, staff and operations into McGovern.

Dhar says the merger with Cloudera creates an organization thats neither exclusively a philanthropic foundation nor a technology company. Its a hybrid that says were an impact-driven organization that will pull from the private sector when we need to, will pull from technology companies when we need to, and will pull from the long history of philanthropy in this country to build something that actually drives outcomes for people, he said. To me, thats the direction of where the field is already going and should be going.

Most often, grantseekers just require cash to maintain or expand services, pay employees and to keep the lights on. But when it comes to a novel and developing field like data science, it can pay to have a funding partner with the experience to envision potential solutions and the hands-on expertise to design those solutions. Toward that end, the newly expanded McGovern Foundation plans to be something of a technology consulting group for philanthropy and nonprofits.

Claudia Juech, the now-former CEO of Cloudera Foundation, will have a central role in the new hybrid organization, directing activities around data enablement for nonprofits as the head of its new Data and Society program. According to Juech, McGoverns approach will involve resourcing the field as nonprofits seek new ways to apply data science to their work. While creating solutions for specific nonprofits will be part of the job, more central to the mission going forward will be creating tools to let nonprofits everywhere access new technology. We can only work with so many organizations, she said.

McGoverns Data and Society team will create and share a portfolio of solutions to serve as practical examples of whats possible in the field of data and AI for social change, guided by equity principles and the ethical use of data. The bigger question, Juech said, is how can we make this accessible to the broader sector?

What are some possibilities for nonprofits as they delve into these new data and AI applications? As in business, one potential area is predictive tools that let organizations better plan and prepare for future problems and needs. Its evolving, Juech said. A lot of nonprofits are using data science to look backward, to understand what happened. But what is possible these days is to see more of what could happen. For example, the Cloudera Foundation helped Womens World Banking create tools to predict the future of womens financial inclusion and empowerment in emerging markets. Another grantee is using data to forecast malaria outbreaks in West Africa.

Of course, artificial intelligence and data science are hot-button issues these days, with many observers voicing unease about potential dangers, including racial and algorithmic biases or the loss of privacy. This is no theoretical worry. One of the most widespread applications of AI affects nearly everyone in the U.S. and billions around the worldthat is, social media companies use of algorithms to populate individual newsfeeds, which has contributed to political polarization, volatility and even violence in the U.S. and abroad.

Those concerns have not escaped Dhar. Though hes a self-described tech optimist, he nevertheless believes philanthropy must keep potential pitfalls front and center, and that nonprofits and the people they serve must be part of the conversationrather than leaving it all up to tech companies and government. The answer isnt to get rid of one or get rid of the other, he said. Its to let civil society be the ones who are coming into that conversation and promoting all of our best interests.

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Streamlit Introduces New Platform for Data Science Teams – Database Trends and Applications

Streamlit, providers of the framework for machine learning and data science, is introducing Streamlit for Teams, the companys first commercial product, allowing data scientists to instantly deploy and share apps with teammates, clients and other stakeholders.

Streamlit for Teams is a zero-effort cloud platform to securely deploy, manage, debug and collaborate on apps. The product deploys apps directly from private Git repos and runs continuous integration to instantly update apps on commits. It layers on enterprise-grade data security and OAuth2-based authentication as well as advanced collaboration features for both data scientists and their customers.

Streamlit is an open source, powerful, and easy-to-use framework, first introduced in 2019, that lets data scientists quickly build web apps to access and explore machine learning models, advanced algorithms and complex data types.

These apps are everything from advanced analytics dashboards to sales and marketing tools based off of the latest predictive algorithms.

Streamlits unique workflow is 10x faster than other alternatives, making it possible for data scientists to go from idea to deployed app in only a few hours, according to the vendor. Streamlit has more than 14,000 GitHub stars, has been downloaded nearly two million times and is used by hundreds of companies, including 7-Eleven, Apple, Ford and Uber.

Streamlit apps are simple interactive script visualizationsa deceptively powerful idiom that strikes just the right balance between low code, power and customizability. This unique approach enables such fast creation of powerful, useful apps, that Streamlit apps have become an entirely new workflow within companiessimilar to Google Docs and Notion. Streamlit for Teams lets companies instantly bring these apps into the entire company, allowing everyone to make faster, data-informed decisions, said Adrien Treuille, co-founder and CEO of Streamlit.

Additionally, Streamlit also announced $35 million in Series B funding, bringing the total raised to $62 million. The round was led by Sequoia and previous investors Gradient Ventures and GGV Capital also participated. Streamlit will use this money to continue to scale its team, expand its platform and bring its technology to leading enterprises.

For more information about this news, visit http://streamlit.io.

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Data Science and ML Platforms Market Global Production, Growth, Share, Demand and Applications Forecast to 2026 – AlgosOnline

Data Science and ML Platforms market snapshot: past & present business landscape, profitable sections, market drivers, restraints, opportunities, accurate forecasts, and Covid-19 impact.

Executive summary:

The latest business intelligence report on Data Science and ML Platforms market contains a comparative study of the past and present business scenario to deduce the industry performance over 2021-2026. It expounds the size and shares of the market and sub-markets, while discussing the growth determinants, opportunities, and challenges governing the industry dynamics.

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As per views of experts, Data Science and ML Platforms market is expected to record a substantial growth, registering a CAGR of XX% over the forecast period.

Apart from these, the report elaborates on the competitive arena, highlighting the tactics adopted by major contenders to maintain their positions in this vertical. Moreover, it examines the COVID-19 footprint on this domain, along with initial steps taken by the industry and strategies that need to be implemented for ensuring massive profits in the upcoming years.

Market snapshot:

Regional outlook:

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Product landscape outline:

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For More Details On this Report: https://www.marketstudyreport.com/reports/global-data-science-and-ml-platforms-market-growth-status-and-outlook-2020-2025

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Data Science and ML Platforms Segment Market Analysis (by Type)

Data Science and ML Platforms Segment Market Analysis (by Application)

Data Science and ML Platforms Major Manufacturers Analysis

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E Source launches electric vehicle data science consortium, empowering utilities by using data science to predict the future of electric…

Press Releases PRESS RELEASE FROM E SOURCE

To aid utility success in this dramatically shifting landscape, ESource has launched EV4Sight, a consortium to continuously forecast the utility-specific impacts of electric transportation growth. Using our growing data hub and advanced data science, EV4Sight will also provide consortium members with ongoing insights into electric transportation charging patterns, analysis of factors that influence charging behavior, and impacts of load growth at the national, state, service-territory or distribution-feeder level. EV4Sight will address both consumer vehicles as well as key categories of commercial and fleet vehicles.

Electric vehicles promise to be the primary engine for electricity growth in the coming decades as well as the pathway to a low carbon future. Load growth forecasts have been based on widely varying estimates, often extrapolating from local policy goals instead of building on relevant local datauntil now.

Utilities are facing considerable risk exposure if they either under or over build in anticipation of electric vehicle demand, and regulators and policy-makers want hard data to support investments. We also know that electric load growth will be very uneven, with some regions, and even neighborhoods, having vastly different trajectories of adoption, says Bill LeBlanc, ESources Chief Instigation Agent.

EV4Sight consortium members will have access to continuously updated dashboards powered by forecasting models rooted in advanced data science techniques. The data includes charging load shapes, EV owner data records, and proprietary market insights on every household and business in the U.S. ESource, through its partner Rolling Energy Resources, will enroll 100 consumer vehicles in each consortium members service territory to collect detailed real-time information on charging location and speed of charge, miles traveled, and state of battery charge. Members are encouraged to also add their own data, such as charging load shapes/AMI data, rate designs, and customer program participation, to develop granular and accurate load forecasts. As the EV markets rapidly evolve, the projections will too.

Today, we just dont know what the real influences and levers are of EV adoption. Similarly, we arent aware of what affects charging habits the most. explains Ted Schultz, President of ESource Data Science. For consumer EV adoption, is it gas prices, number of public charging stations, model availability, EV range, or something that is hidden from us? For commercial vehicles, is rate design, range, managed charging, vehicle cost or other factors that are the biggest influencers? Thats where data science will provide consortium members the tools you need in an ongoing basis to help manage the grid of the future.

With dozens of utilities all pooling their data along with ESources proprietary data sets, advanced data science within EV4Sight will enable us to discern which utility programs, rate designs, policies, and activities work best with various target groups of customers under varying driving conditions.

ESources long-standing role of bringing together utilities to solve big problems will allow each EV4Sight consortium member to learn from all the other utilities programs and approaches and EV data without having to conduct those experiments themselves, explains LeBlanc. We are just at the beginning of this EV growth curve, which means markets will be shifting dramatically over the coming decade, necessitating real time analysis of this complex addition to our electric grid throughout the coming decade.

Dont risk unplanned EV load growth! Become an EV4Sight consortium member today by visitingwww.esource.com/ev4sight, call 1-800-ESOURCE, or email[emailprotected].

About ESource:ESource is a leading partner to more than 500 electric, gas, and water utilities and municipalities, and their partners, across theUSand Canada. We provide data science, market research, benchmarking, and consulting services. Our 35 years of technology validation, market assessment, program design, and customer experience expertise help clients make informed, data-driven decisions; plan for tomorrows infrastructure needs; strengthen customer relationships; and meet critical business objectives while becoming more innovative and responsive in the rapidly evolving market.

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Automated Data Science and Machine Learning Platforms Market 2021 Recent Trends and Growth Forecast by 2026 |Palantier, MathWorks, Alteryx, SAS,…

Automated-Data-Science-and-Machine-Learning-Platforms-Market

Latest research on Global Automated Data Science and Machine Learning Platforms Market report covers forecast and analysis on a worldwide, regional and country level. The study provides historical information of 2016-2021 together with a forecast from 2021 to 2026 supported by both volume and revenue (USD million). The entire study covers the key drivers and restraints for the Automated Data Science and Machine Learning Platforms market. this report included a special section on the Impact of COVID19. Also, Automated Data Science and Machine Learning Platforms Market (By major Key Players, By Types, By Applications, and Leading Regions) Segments outlook, Business assessment, Competition scenario and Trends .The report also gives 360-degree overview of the competitive landscape of the industries.

Moreover, it offers highly accurate estimations on the CAGR, market share, and market size of key regions and countries. Players can use this study to explore untapped Automated Data Science and Machine Learning Platforms markets to extend their reach and create sales opportunities.

Some of the key manufacturers operating in this market include: Palantier, MathWorks, Alteryx, SAS, Databricks, TIBCO Software, Dataiku, H2O.ai, IBM, Microsoft, Google, KNIME, DataRobot, RapidMiner, Anaconda, Domino, Altair and More

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Our Research Analyst implemented a Free PDF Sample Report copy as per your Research Requirement, also including impact analysisofCOVID-19 on Automated Data Science and Machine Learning Platforms Market Size

Automated Data Science and Machine Learning Platforms market competitive landscape offers data information and details by companies. Its provides a complete analysis and precise statistics on revenue by the major players participants for the period 2021-2026. The report also illustrates minute details in the Automated Data Science and Machine Learning Platforms market governing micro and macroeconomic factors that seem to have a dominant and long-term impact, directing the course of popular trends in the global Automated Data Science and Machine Learning Platforms market.

Market Segment by Type, covers:Cloud-basedOn-premisesMarket Segment by Applications, can be divided into:Small and Medium Enterprises (SMEs)Large Enterprises

Regions Covered in the Global Automated Data Science and Machine Learning Platforms Market:1. South America Automated Data Science and Machine Learning Platforms Market Covers Colombia, Brazil, and Argentina.2. North America Automated Data Science and Machine Learning Platforms Market Covers Canada, United States, and Mexico.3. Europe Automated Data Science and Machine Learning Platforms Market Covers UK, France, Italy, Germany, and Russia.4. The Middle East and Africa Automated Data Science and Machine Learning Platforms Market Covers UAE, Saudi Arabia, Egypt, Nigeria, and South Africa.5. Asia Pacific Automated Data Science and Machine Learning Platforms Market Covers Korea, Japan, China, Southeast Asia, and India.

Years Considered to Estimate the Market Size:History Year: 2015-2021Base Year: 2021Estimated Year: 2021Forecast Year: 2021-2026

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Automated Data Science and Machine Learning Platforms Market 2021 Recent Trends and Growth Forecast by 2026 |Palantier, MathWorks, Alteryx, SAS,...

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