The data science and machine learning market is more vibrant than ever before, according to Gartner's new 2021 Magic Quadrant for Data Science and Machine Learning Platforms. "Movement in this market is rapid and multidirectional," said the IT research firm in its new report ranking the 20 world-leading companies in the market. The global COVID-19 pandemic hasn't slowed down the rapid pace of data science and machine learning (DSML) innovation or the bullish growth strategies from vendors. Many organizations are starting DSML initiatives using free or low-cost open-source and public cloud offerings to build up their expertise and explore new opportunities. CRN breaks down the 20 market-leading vendors that made Gartner's 2021 Magic Quadrant for Data Science and Machine Learning Platforms, as well as each company's weaknesses and strengths in the market according to Gartner.

The researcher defines a data science and machine learning platform as "a core product and supporting portfolio of coherently integrated products, …

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.

We are happy to offer complimentary access to the Gartner Magic Quadrant for Data Science and Machine Learning Platforms, 2020 report. This Gartner Magic Quadrant evaluates vendors that offer the essentials for building DSML solutions detailing leading platform overviews, strengths, and areas of caution that will help you determine the vendors that are right for you. Simply fill out the form and download the free complete report today to get the information you need to select the right approaches for success. Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Peter Krensky, Pieter den Hamer, Erick Brethenoux, Jim Hare, Carlie Idoine, Alexander Linden, Svetlana Sicular, Farhan Choudhary, 11 February 2020 Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact.

Back in December, when AWS launched its new machine learning IDE, SageMaker Studio, we wrote up a "hot-off-the-presses" review. At the time, we felt the platform fell short, but we promised to publish an update after working with AWS to get more familiar with the new capabilities. When Amazon launched SageMaker Studio, they made clear the pain points they were aiming to solve: "The machine learning development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling." The machine learning workflow -- from data ingestion, feature engineering, and model selection to debugging, deployment, monitoring, and maintenance, along with all the steps in between -- can be like trying to tame a wild animal. To solve this challenge, big tech companies have built their own machine learning and big data platforms for their data scientists to use: Uber has Michelangelo, Facebook (and likely Instagram and WhatsApp) has FBLearner flow, Google has TFX, and Netflix has both Metaflow and Polynote (the latter has been open sourced).

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Gartner has released its 2020 Data Science and Machine Learning Platforms Magic Quadrant, and we are excited to announce that Databricks has been recognized as a Leader. Gartner evaluated 17 vendors for their completeness of vision and ability to execute. We are confident the following attributes contributed to the company's success: The biggest advantage of Databricks' Unified Data Analytics Platform is its ability to run data processing and machine learning workloads at scale and all in one place. Customers praise Databricks for significantly reducing TCO and accelerating time to value, thanks to its seamless end-to-end integration of everything from ETL to exploratory data science to production machine learning. With Databricks, data teams can build reliable data pipelines with Delta Lake, which adds reliability and performance to existing data lakes.

The employment of Data Science and Machine Learning technologies is at a peak. We can see several software and tools with various innovative features in the market that serve us with the efficiency of new-age data technologies that can potentially increase a business's efficiency and value proposition. With continuous evolution at scale such solutions too, get revamped with time. Now is the era for automated data science and machine learning software that not only enhance the operational proficiency of such tools but also assist data scientists with great potential. They help automate the repetitive and mundane tasks within the ML or data science processes without compromising model performance and productivity. Therefore, here is the list of top 10 automated data science and machine learning software presented by some key players of the respective market.

The employment of Data Science and Machine Learning technologies is at a peak. We can see several software and tools with various innovative features in the market that serve us with the efficiency of new-age data technologies that can potentially increase a business's efficiency and value proposition. With continuous evolution at scale such solutions too, get revamped with time. Now is the era for automated data science and machine learning software that not only enhance the operational proficiency of such tools but also assist data scientists with great potential. They help automate the repetitive and mundane tasks within the ML or data science processes without compromising model performance and productivity. Therefore, here is the list of top 10 automated data science and machine learning software presented by some key players of the respective market.

These providers have recently been named major players in data science and machine learning platforms for 2020 by analyst house Gartner, Inc. Data science and machine learning platforms come in a variety of shapes and sizes to meet the ever-changing needs of organizations and their increasingly complex environments. There are both small and large providers that offer software to help these companies with both niche and common challenges, though choosing the vendor(s) that are right for your specific environment can be a daunting task. The following providers have recently been named leaders in the 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. The report, which highlights and scores the top products in the industry, features these four tools as being cornerstones in the space.