Find out about 10 data science and machine learning tools solutions and service providers need to know.
The field of data science and machine learning technology is fueled primarily by waves of generative AI, and last year has been fuelled by Zentec AI systems and large-scale language models that enhance them.
If data science and machine learning tools are targeted at the development and support of data analysis and predictive analytics systems, these tools are now increasingly supporting the development of AI agent systems, according to the 2025 Gartner Magic Quadrant of the Data Science and Machine Learning Platform.
Another major trend, according to the report, is the migration from point-specific tools to “full-stack data and AI platforms,” including model development and lifecycle management, data science tasks, data pipelines and other chores. Furthermore, these platforms themselves incorporate LLMS and Genai assistants “to enhance your data science workflow.”
As part of CRN's 2025 series so far, check out some of the hottest data science and machine learning tools used today. Some of the tools below are relatively new in the market, but others have been around for a while and have been updated recently. This list also includes both commercial products and open source software.
AI agent with dataiku
Dataiku's flagship Universal AI platform is one of the industry's leading AI and machine learning platforms for data scientists. In April, Dataiku debuted AI agents using DataIKU, a new feature set within the platform for creating and controlling AI agents at scale.
The platform supports the central creation of code agents for data scientists and developers, as well as agents using the Visual Agent No Code option for non-technical business users. Features include managed agent tools to maintain the quality and validation of the tools used by agents, and a Genai registry for strategic monitoring of agent use cases.
A key technology within an AI agent with DataIKU is the DataIKU LLM mesh architecture. According to the company, it manages model access across large language models of all its own open source and cloud services. Dataiku Safe Guard defines and applies GuardRails, and Agent Conneg centralizes agent access across your organization from a single interface.
For agent observability and performance monitoring, AI agents with DataIKU provide visibility into agent decisions, quality guard to continuously evaluate and monitor agent performance, cost guard trace explorer for real-time usage tracking, budget enforcement, and internal cost allocation.
Anaconda AI Platform
Anaconda is well known for its data science and AI platform for developers using the popular Python programming language.
The new Anaconda AI platform, unveiled in May, is an integrated open source platform that provides Anaconda with a comprehensive system for streamlining machine learning workflows and building, training and deploying machine learning models.
According to the company, the platform provides simplified development and governance control to increase productivity for practitioners and reduce organizational risks associated with open source AI development.
Features and features include Anaconda AI Navigator for AI application development and Anaconda assistant chatbots with AI to help experiment with large-scale language models and help with coding, debugging and data visualization. It also includes a Conda package manager for managing packages and dependencies, a curated “required” ML library, and an MLOP for automating the deployment and management of models.
Datarobot syftr
In May, Agentic Workforce Platform developer Datarobot debuted SYFTR, an open source framework designed to allow AI developers to evaluate and identify agent workflows for commercial use of performance agent workflows.
According to Datarobot, SYFTR “ensures AI practitioners programmatically discover and implement the best combination of components, parameters, tools and strategies for agent use cases” and optimize them for accuracy, speed of processing and cost.
SYFTR features include a premature stop mechanism for multipurpose search and Bayesian optimization.
SYFTR software is currently available as an open source project “authorized licensed”; The SYFTR Enterprise Edition will be available this fall.
Domino Enterprise AI Platform
Domino Data Lab's flagship Domino Enterprise AI platform is a machine learning operation (MLOPS) system that helps organizations build and run AI at scale. The platform provides a central hub for data science teams, from initial research to model deployment and monitoring, with tools and infrastructure that provide tools and infrastructure for managing the entire data science lifecycle, according to the company.
In June, Domino Data Lab launched a new feature with a unified system for productivity, governance and delivery, and a new feature with the new “turning fragmented initiatives into AI factories.”
The release also included new ground-to-AI services to catalyze proven AI cultural change within the organization.
Hex Technology
Hex provides a collaborative data science and analytics workspace where data teams and business users can share their analytics results. The platform combines traditional data science notebook capabilities with integrated AI aid, data applications and reporting, and advanced collaboration capabilities.
In January, the company introduced HEX embedding analysis. This allows developers to build HEX technology in data products such as applications that require customer analytics.
In May, HEX raised an impressive $70 million in Series C funding.
mlflow 3.0
According to the MLFLOW.org website, MLFLOW is an open source MLOPS platform for managing workflows and artifacts throughout the machine learning lifecycle, helping machine learning practitioners and development teams handle the complexity of machine learning processes.
Introducing MLFLOW 3.0 on June 11th, “it's not just a feature update,” according to the announcement of the 3.0 release, but “expands what's essentially possible” with ML tools, addressing observability and quality challenges with Genai deployments.
The new edition provides LoggedModel1 entities to allow better organization and comparison of the entire experimental generation AI agents, deep learning checkpoints, and model variants. It also enhances model tracking for new Genai evaluation suites and systematic support.
The MLFLOW project was originally created by data management platform giant DataBic and contributed to the Linux Foundation in 2020. DataBricks offers fully managed MLFLOW services on its own platform.
According to DataBricks, MLFLOW has over 30 million monthly downloads and contributions from over 850 developers around the world.
Pytorch 2.7.1
Pytorch is a widely used open source machine learning library and framework for developing and training deep neural networks. According to the pytorch.org website, it is known for its flexibility and ease of use for building and debugging more intuitive models, along with more intuitive model construction and dynamic computational graphing capabilities.
According to GitHub, the latest edition is Pytorch 2.7.1, released on June 4th. The new version includes support for Python 3.12 and optimizations for AoTinductor.
Pytorch 2.7.1 is part of the Pytorch 2 series, focusing on improving Pytorch performance and enhancing user experience with compiler-level changes.
Snowflake Data Science Agent
In early June, Snowflake Summit in 2025, Snowflake announced its Data Science Agent. This is an “agent companion” that increases productivity for data scientists by automating everyday machine learning model development tasks.
According to Snowflake, data science agents simplify AI and ML workflows, democratize users' access to data across their business, and eliminate technical overhead.
According to Snowflake, Data Science Agent, which will soon be appearing in private preview, will use Anthropic's Claude Large Language Models to break down issues related to ML workflows into different steps for a private preview soon. This product uses advanced techniques such as multi-step inference, context understanding, and action execution to create a fully functional pipeline.
Tecton 1.1
Tecton has begun developing a functional platform that streamlines the process of building, deploying and managing machine learning capabilities. The company expanded in September 2024, beyond its machine learning roots, with a new release of a platform that delivers contextual data to large-scale language models that power generative AI systems.
In February, the company debuted its latest update to its platform, Tecton 1.1, adding features that make it easier for AI teams to build more sophisticated features, optimize infrastructure and improve model performance.
This release includes new API resources for accessing third-party data sources in real time, new features to perform more efficiently the calculations needed to speed up the real-time feature view to speed up conversions during online acquisition queries, and many performance enhancements on the core techton platform.
Tensorflow 2.19
Tensorflow is a popular open source machine learning platform and software library for developing and deploying machine learning models for AI, especially sophisticated, deep learning models and neural networks.
Tensorflow 2.19 was released in March and has received many technical improvements, including changes to Litert's C++ API and support for BFLOAT16 for Tflite cast.
Pytorch is generally considered an alternative platform for small machine learning development projects where model experimentation and rapid editing is a priority, but according to the Tensorflow.org website, Tensorflow is generally considered ideal for large-scale projects and production environments that require performance and scalability.
Tensorflow is available under Apache license 2.0.
