Best Free Machine Learning Software
View:
Compare the Top Free Machine Learning Software as of April 2025
Sort By:
What is Free Machine Learning Software?
Machine learning software enables developers and data scientists to build, train, and deploy models that can learn from data and make predictions or decisions without being explicitly programmed. These tools provide frameworks and algorithms for tasks such as classification, regression, clustering, and natural language processing. They often come with features like data preprocessing, model evaluation, and hyperparameter tuning, which help optimize the performance of machine learning models. With the ability to analyze large datasets and uncover patterns, machine learning software is widely used in industries like healthcare, finance, marketing, and autonomous systems. Overall, this software empowers organizations to leverage data for smarter decision-making and automation. Compare and read user reviews of the best Free Machine Learning software currently available using the table below. This list is updated regularly.
-
1
Vertex AI
Google
Machine Learning in Vertex AI allows businesses to harness the power of data-driven models to make intelligent decisions and automate processes. With a wide range of algorithms, tools, and models, businesses can address diverse challenges such as forecasting, classification, and anomaly detection. Vertex AI makes it easy for companies to create, train, and deploy machine learning models at scale. New customers receive $300 in free credits to explore machine learning features and test models for their unique use cases. By integrating machine learning into their workflows, businesses can unlock the full potential of their data and drive better outcomes.Starting Price: Free ($300 in free credits) -
2
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
3
ClearML
ClearML
ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.Starting Price: $15 -
4
neptune.ai
neptune.ai
Neptune.ai is a machine learning operations (MLOps) platform designed to streamline the tracking, organizing, and sharing of experiments and model-building processes. It provides a comprehensive environment for data scientists and machine learning engineers to log, visualize, and compare model training runs, datasets, hyperparameters, and metrics in real-time. Neptune.ai integrates easily with popular machine learning libraries, enabling teams to efficiently manage both research and production workflows. With features that support collaboration, versioning, and experiment reproducibility, Neptune.ai enhances productivity and helps ensure that machine learning projects are transparent and well-documented across their lifecycle.Starting Price: $49 per month -
5
Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
6
Keepsake
Replicate
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves code and weights to Amazon S3 or Google Cloud Storage. This facilitates the retrieval of code and weights from any checkpoint, aiding in re-training or model deployment. Keepsake supports various machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, by saving files and dictionaries in a straightforward manner. It also offers features such as experiment comparison, enabling users to analyze differences in parameters, metrics, and dependencies across experiments.Starting Price: Free
- Previous
- You're on page 1
- Next