Databricks occupies the best place in the Gartner DSML platform report

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On the market for the new learning platform (DSML) for data science? People in Gartner have compiled a document that ranks the best DSML platform providers in the field. This year, Databricks took the number one slot after sharing last year with Microsoft and Google.

For its magic quadrant 2025 for DSML platforms Gartner Afraz Afraz Jaffri, Maryam Hassanla, Tong Zhang, Deepak Seth and Yogesh Bhatt cut and cut into 16 DSML offers that are loaded by generative AI and AI agent capacities.

Intoread Of To Gartner, WHICH SAYS AND REPENT SURVEY FOUND MORE THAN HALF OF DSML USERS AI AI FUNCTIONS TO GAIN AUTOMATED INSIGHTS OR Using Natural Language Queries to Develop AI.

The building and evaluation of AI and ML models remains the main focus for DSML platforms evaluated by Gartner, which must work with structured and impartial data. Other common features include: inclusion of data preparation capabilities; Development environment based; function of cooperation and project management; Possibility of deployment and host; Model Life Cycle Management; and advanced administrative check over the user.

One of the areas where DSML platforms differ is the inclusion of endowment models. Google, Microsoft, Amazon Web Services, Alibaba and IBM create their own endowment models. Databricks, a leader in Magic Quadrant 2025 for DSML Plaforms, has previously developed its own models, but now it is focused on allowing its customers to make the most of the Oser models.

Gartner 2025 Magic Quadrant for Data Science Machine Learning Platform (Source: Gartner)

There is a floor of work that goes without the development of their own endowment models. As analysts note: “Genai is an important catalyst, but a challenge of integrating data, models, code and infrastructure into reliable, scalable Remake products.

Quadrant

Databricks owned the highest place in this magical quadrant on the back of the “understanding” of the platform, a steady lead and continued success on the market. Last year, the company had the highest rating in the vertical “ability to perform” a scale and this year added the highest score along the horizontal ranking of “complete vision”, Microsoft, Google and Dataics. Sometimes the learning curve present, improved competitive offers and insufficient availability of some functions on different cloud platforms were onted press given by Gartner.

Google caught instead of number two with the Vertex AI platform, which is integrated with the Gemini Foundation Models. The Google strengths include the unified vertex AI management, RAG support in Vertex AI search and the history of joint innovation with its customers. The complexity of Vertex AI, support for AI peak outside Google and the existence of other radical solutions are Carsions.

Microsoft Rounded Out The Top Three Finishers With Its Azure ML offering, WHICH GARTNER HALED FOR PROVIDING AND RANGE OF CAPABABLESS FOR DATA SCIENTISTS, AI Engineers, and “Pro-Code Developers ECOSYSTEM, and Flexible Pricing.

Amazon Web Services is also the highest thanks to its Sagemaker and Foundation Models. Gartner Plus includes the launch of the Unified Sagemaker Unified AWS in December, the robust ecosystem of AI AP and the ASA capability. Cations include integration and flexibility, lack of AWS foundation models, and spending and predicting use.

Dataics lead the second level of DSML offers within the leader quadrant, which offer a similar completeness of vision, but ruled the ability to perform the above cloud bigs. The French-American society hated Gartner for the continuing focus on science of data, customer support and market understanding. The cations include precise delivery functions, buyer awareness and differentiation with other DSML suppliers.

Altair continued to build on his offer Rapidminer, which he acquired in 2022. Gartner ranked Altair’s understanding as high, rightly acquisition and integration of the knowledge base of Cambridge Semantics at the beginning of this year and also mentioned its AI center. The concerns included customers’ awareness, integration challenges and uncertainty after winning Altair Siemens in March.

Databricks has the best rated DSML platform on Gartner (Tada Images/Shutstock)

Datarobot received support in the GARTNER ranking thanks to the restoration of his product and Pivot AI in 2024, which “resonates with the needs of businesses”. The analytical company also applauded the acquisition of Agnostq Datarobot and its overall understanding of the market. The cations include the public perception of the datarobot as bad for experts, difficult to manage interactions with low code and pro-call and acquisition of purchases from other partners and sellers.

IBM moved further this year’s magic quadrant on the back of Watsonx, which Gartner stopped for a wide range of tools, open frames and endowment models. Datastax’s acquisition has also strengthened IBM luxury in Stamord in Connecticut, while IBM research provides good things from the laboratory. Concerns are awareness of granite models, exclusion of SPSS tools within Watxonx and lower integration levels when using in AWS, Azure and GCP.

Quadrant

H2O.Ai had the most complete vision of every seller in the visionary quadrant. Gartner identified the “specific expertise” of the company in the use of small language models, fine -tuning, distillation, searching for vector and AI agents. The integration of predicting AI capacitine and access to 24 Kaggle Grandmasters were pluses. Minuses include prices that can be difficult to understand, market awareness and lack of consultants in partner roles.

Snowflake debuted this year in the DSML platform with a cloud offer that is widely accepted across middle and large businesses. Gartner applauded Snowflake for his strong vision for a combination of structured and non -structural data, the AI ​​market market and training programs. The concerns include a complex price system, limited geographical availability of some AI models and the capacity of the mlops that came slowly.

Domino Data Lab in this year’s DSML report slightly shifted to the right. Gartner likes to domino the development of the management of public affairs in order to manage the risk and compliance with the regulations, as well as its industrial focus and integration of finops. The concerns included limited awareness, lack of functions for data engineers and complex integration.

(Kurhan/Shutterstock)

The cloud barely moved in the visionary quadrant with its offer unified open data lakes and data texts. Gartner likes two acquisitions of cloules, verta and octopopi, as well as its support for private AI operating on-Pro or cloud. She also likes to integrate with the face of the face and registration of the NVIDIA NGC. The cations include market awareness, the complexity of frames with an open source code and the less sophisticated AI management that competitors.

Sas debuted in Vistaries Quadrant after he was in the leader of the quadrant last year. The VIYA seller’s offer has been hated for having a composible architecture that provided customers with flexibility and selection, as well as a risk management offer for companies in regulated industries. There were also more SAS partnership with Microsoft. Minuses include the AI ​​agent AI, which lags their peers, lower acceptance of VIYA and the competition of other DSML dealers.

Challenges

Alibaba Cloud is a lonely passenger Challengers Quadrant, which also includes IBM in 2024. The cloud platform of the Chinese cloud giant for AI (or PAI) checks many boxes, if it is a high -performance computing sources and science infrastructure of data, not to mention Alibaba’s investment of $ 53 billion in AI infrastructure. The availability of Qwen Alibaba, solid security and inference accelerators LLM were plus. The cations include low adoption outside Southeast Asia, too much focus on the retail industry and the unclear strategy of science of data.

Quadrant players of the niche

Altyyx is going to rediscover after the start of the Aluyx One platform at the beginning of this year and selling once a public company to private equity companies in 2024. The concerns were the powerful transitions of 2024, not the coast traction between code and generation innovation users for data scientists.

Mathworks is also listed in this year’s MQ thanks to its production Matlab and Simulink, which are widely used by scientists, engineers, other industries such as automotive industry, air automation, telecommunications and medical facilities. AI and simulations are the strengths of MathWorks products, a wide range of talents and understanding cases to use AI AI AI. The concerns included too much focus on data scientists, lack of Genai capacities and no private cloud offer.

This year’s MQ for DSML has been abandoned by three retailers, including Anaconda, Knime and Posity (Forus Rstudio).

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