scikit-learn Alternatives

If you're looking for a scikit-learn alternative, MLflow is worth a look. It's an open-source, end-to-end MLOps platform that makes it easier to develop, deploy and operate machine learning and generative AI applications. MLflow offers a unified environment for managing the lifetime of ML projects, including experiment tracking, logging and experiment run management, model management for deploying and managing models, and support for popular deep learning and traditional machine learning libraries like PyTorch and scikit-learn.

Another option is PyTorch, an adaptable and powerful end-to-end machine learning framework. It offers fast, flexible experimentation and efficient production, with features like easy toggling between eager and graph modes, distributed training that scales to large numbers of machines, and libraries and tools for tasks like computer vision and natural language processing. PyTorch spans a wide range of use cases from rapid prototyping to large-scale production deployments and has extensive documentation and community resources.

If you're looking for a platform that spans generative and predictive workflows, check out DataRobot AI Platform. It unifies the two workflows so you can quickly build and deploy AI models while governing assets and leveraging domain expertise. DataRobot provides enterprise monitoring and control, complete visibility and oversight, and rapid innovation, with deep ecosystem integration and recognition as a leader in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms.

If you're into deep learning, Keras is a fast-to-code and fast-to-debug deep learning API that's designed to be elegant, maintainable and fast. It can run on multiple backend frameworks like TensorFlow, PyTorch and JAX for flexibility and integration. Keras is highly scalable, suitable for large-scale industrial use, and comes with extensive documentation and code examples to help you get started and experiment. It can be used for a wide range of use cases including computer vision, natural language processing and generative AI.