Gartner Magic Quadrant Artificial Intelligence 2020 – In Gartner’s vendor-first assessment specifically for cloud AI/ML environments, AWS was recognized as an industry leader with the highest scores in both core metrics: performance and visibility’ness.
• Understand Cloud AI developer services in their current capabilities. and develop perspectives that can be shared among IT professionals, business leaders, and cloud developers from startups to large enterprises.
Gartner Magic Quadrant Artificial Intelligence 2020
• Evaluate the strengths and precautions of cloud providers that provide these services. with detailed and updated descriptions for each provider
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• Plan for the future with advice and insights on Cloud AI/ML strategies in development or ready to be deployed.
As the market continues to evolve Use reports as the basis for evaluating Cloud AI/ML providers.
This graphics by Gartner, Inc. was published as part of a larger research study and should be evaluated in the context of the entire article. Gartner documentation is available upon request from AWS. Gartner does not endorse any vendor, product, or service described in it. Company research papers It is not recommended that technology users select only the highest rated vendors or other positions. Gartner research publications are the opinions of the Gartner research organization and should not be construed as facts. Gartner disclaims all warranties. whether express or implied In connection with this research including any warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
Gartner Report: Aws Named A Leader In Cloud Ai Developer Services
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Gartner is usually not one to get too excited about things. That’s not the case in Stamford, Connecticut, but analysts from the reputable firm shed light on a recent report. Noting that the market for data science and machine learning platforms is “strong” and “exciting innovation”.
“Multiple vendors offer a wide range of capabilities with solutions tailored to multiple maturities,” the company wrote in its Magic Quadrant for Data Science and Machine Learning Platforms. continually And this area is very different from when this Magic Quadrant started in 2014.”
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Gartner ranks six vendors in its Leaders Quadrant for the February 2020 report, up from the four vendors last gathered by Gartner in its January 2019 DS and ML report. The Dataiku, Alteryx, Databricks, and MathWorks quadrants have moved. From surrounding area to Leaders quadrant
Previously in Gartner’s Leaders Quadrant, KNIME and RapidMiner found themselves in the Visionaries field alongside vendors such as Google, DataRobot, Domino Data Lab, and H2O.ai. IBM was the only one participating in the Challengers Quadrant, while Anaconda and Altair Acquires DataWatch, Takes Niche Player
Gartner reports that Alteryx is transforming the market perception from a pure-play data preparation provider to a full-fledged data science and ML platform. He will help lead two acquisitions, ClearStory Data and Feature Labs, as part of the transition. “Alteryx’s no-code approach is attractive to organizations that want to use ML but want an easy-to-use platform built for business analysts and citizen data scientists,” write Gartner analysts.
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As Gartner reports, Databricks has been announced to grow and perform strongly. including a good product roadmap The scalability of Databricks’ Apache Spark-based cloud platform is also a plus. The same goes for the availability of customer success engineers. However, if you’re looking for a simple and intuitive interface, Databricks isn’t the best solution. Because the company focuses on “Technical Audience This includes data scientists and machine learning engineers.”
Dataiku has been promoted to the Leaders Quadrant because of its work in enabling collaboration between users. including data scientists and data engineers. As well as citizen data scientists, Data Science Studio software offers a rich user interface for people of all skill levels. But even experts can produce with a laptop-like interface. Data governance and compliance are also important.
MATLAB Developer MathWorks Promoted to Leaders Quadrant Due to Vendor’s Work in Full Data Science Support From data preprocessing and model development to production, Gartner was especially impressed with its integrated simulation capabilities by connecting to the Simulink platform, while pre-built solutions for predictive maintenance. The event is another highlight.
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SAS is a constant contender for any initiative. in advanced analytics and data science, and Gartner was impressed with Cary’s work in North Carolina in this area. such as performance monitoring automatic retraining version control and pedigree
TIBCO Software is back in the lead for DS and ML platforms having successfully integrated all of its acquisitions over the years, including Spotfire, Jaspersoft, Insightful, Statistica, Alpine Data, StreamBase Systems and Orchestra Networks. Gartner said, “TIBCO makes TIBCO Data Science simpler and easier. while the platform supports open, fast-moving ML,” said Gartner.
Domino Data Lab Announces Open Architecture to Help Customers Integrate Data Science Assets and Workloads and automate ML workflows. Gartner noted that customers appreciate Domino’s ability to support collaboration between data science professionals and business users.
Essential Enterprise Ai Companies Landscape
DataRobot has successfully integrated multiple acquisitions (ParalelM, Paxata, Nutonian, and Nexosis) over the past three years to attract a wide range of users, from data scientists and statisticians to business analysts and statisticians. Improve, says Gartner, retain his position.
Google was offered by Gartner because it has one of the “largest ML stacks” in its Cloud AI platform, making it “a great choice for leading data science talent.” Complete end-to-end, as users have to use other Google products such as Cloud Data Fusion, Data Studio, Big Query, etc. Accessibility is also being questioned by citizen data scientists.
KNIME offers a popular open source product (KNIME Analytics Platform) that can be extended with a commercial product (KNIME Server) to enable enterprise data science such as collaboration. automation and utilization Revenue may not grow fast, but Gartner likes KNIME for its “focus on innovation” and its deep connection to the data science community.
Gartner Identifies Four Trends Driving Near Term Artificial Intelligence Innovation
H2O.ai has been recognized by Gartner for its vision of key trends such as open source (H2O-3 and Sparkling Water) and commercial products (Driverless AI), as well as complementary data science and power of explanation. High performance open source software is also highlighted.
Microsoft is a promising vendor in this area through its Azure Machine Learning offering. Gartner likes that Azure ML provides citizen data scientists with a drag-and-drop interface for advanced analytics. But it doesn’t leave out advanced capabilities for data scientists.
RapidMiner brings together data scientists and citizen data scientists with various offerings. This includes core open source products and commercial offerings that bring enterprise-class features. Gartner also likes the new ML model compliance and validation by offering model automation.
Datarobot Is A ‘visionary’ In 2020 Gartner Magic Quadrant For Data Science And Machine Learning Platforms
IBM is moving forward with its flagship product, Watson Studio, which Gartner calls “the next generation of computers.” According to Gartner, the product can be used by multiple user groups. And multi-cloud and hybrid deployments are also a plus for IBM. Like other vendors, IBM offers AutoAI capabilities, but Gartner says that further innovation is needed for AutoML to “become a table that can be used.” used together”
Altair is currently in transition. It acquired Datawatch more than a year ago. Its main offering, Knowledge Studio, has a powerful user interface and is more favored by visual data scientists. Gartner says Altair has a strong user base in the financial services industry. which decision-making structures are popular
Anaconda is announced to offer a freely linked distribution that data scientists can access in open source.
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