Best Machine Learning Software for Python
View:
Compare the Top Machine Learning Software that integrates with Python as of May 2025
Sort By:
This a list of Machine Learning software that integrates with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
What is Machine Learning Software for Python?
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 Machine Learning software for Python 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
Replicate
Replicate
Machine learning can now do some extraordinary things: it can understand the world, drive cars, write code, make art. But, it's still extremely hard to use. Research is typically published as a PDF, with scraps of code on GitHub and weights on Google Drive (if you’re lucky!). Unless you're an expert, it's impossible to take that work and apply it to a real-world problem. We’re making machine learning accessible to everyone. People creating machine learning models should be able to share them in a way that other people can use, and people who want to use machine learning should be able to do so without getting a PhD. With great power also comes great responsibility. We believe that with better tools and safeguards, we'll make this powerful technology safer and easier to understand.Starting Price: Free -
3
Towhee
Towhee
You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. Towhee provides out-of-the-box integration with your favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data.Starting Price: Free -
4
Mystic
Mystic
With Mystic you can deploy ML in your own Azure/AWS/GCP account or deploy in our shared GPU cluster. All Mystic features are directly in your own cloud. In a few simple steps, you get the most cost-effective and scalable way of running ML inference. Our shared cluster of GPUs is used by 100s of users simultaneously. Low cost but performance will vary depending on real-time GPU availability. Good AI products need good models and infrastructure; we solve the infrastructure part. A fully managed Kubernetes platform that runs in your own cloud. Open-source Python library and API to simplify your entire AI workflow. You get a high-performance platform to serve your AI models. Mystic will automatically scale up and down GPUs depending on the number of API calls your models receive. You can easily view, edit, and monitor your infrastructure from your Mystic dashboard, CLI, and APIs.Starting Price: Free -
5
Tecton
Tecton
Deploy machine learning applications to production in minutes, rather than months. Automate the transformation of raw data, generate training data sets, and serve features for online inference at scale. Save months of work by replacing bespoke data pipelines with robust pipelines that are created, orchestrated and maintained automatically. Increase your team’s efficiency by sharing features across the organization and standardize all of your machine learning data workflows in one platform. Serve features in production at extreme scale with the confidence that systems will always be up and running. Tecton meets strict security and compliance standards. Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure. -
6
Feast
Tecton
Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution.
- Previous
- You're on page 1
- Next