The machine learning (ML) industry has evolved tremendously in the last couple of years. This has led to the development of a large number of platforms, many of which are open source, that cover different needs and address users with different levels of experience. With such a wealth of options available, choosing the right tools can be challenging – and given the high complexity and requirements of some solutions, tooling can even be a blocker for organisations that are in the early stages of their AI journey and need a lightweight machine learning platform.
To select the right machine learning platform for you, it’s critical to understand and evaluate several key factors:
- These include the scale and maturity of your project,
- Which area of the ML lifecycle you want to cover
- And what kind of cloud environment you will be running on.
If you are in the exploratory phase, when you consider these factors it is highly likely that MLflow will emerge as the ideal platform.
MLflow is among the leading open source machine learning platforms, offering an easy-to-use and lightweight solution that covers several key functions in the ML lifecycle. It was started in 2018 and it has kept growing in popularity ever since. In November 2022, it reached 10 million users and nowadays it is used by important players from the AI world such as DataBricks, Meta, HuggingFaces and Accenture. Learn more about what is MLflow.
MLflow, similarly to any other tool, was built to solve a problem. Experiment tracking is one of the biggest challenges facing data scientists and ML engineers, and it is addressed by MLflow. The open source platform helps users reduce time spent on analysing results and more quickly track the best performant experiments.
However, the key driver behind MLflow’s widespread adoption is its accessibility. In contrast to the majority of ML tools that are difficult to use and designed for advanced users, MLflow offers ease-of-use as a core differentiator. MLflow includes an intuitive user interface (UI) so users do not need to rely exclusively on the command line interface (CLI), making it especially well-suited to beginners.
- Key factors to consider when choosing an MLOps platform
- A detailed view of MLflow: its components, concepts and benefits
- Kubeflow vs MLflow: comparing apples and oranges