MLflow is an open-source platform that helps manage machine learning tasks. It started in 2018 and became very popular, reaching 10 million users by November 2022. Before MLflow, people working with AI faced many challenges like tracking experiments, managing models, and ensuring code could be reused. MLflow solves these problems, making life easier for AI professionals and enthusiasts.
One of its advantages is that it is simple and can run on regular computers without expensive hardware. At the same time, it can also work with more advanced tools, making it perfect for scaling up AI projects.
Why use MLflow?
MLflow is one of the most popular platforms for machine learning (ML). It helps users manage important tasks such as:
Reproducing results: ML projects often start simple but can grow into many experiments. Keeping track of all details manually can be difficult, and one small mistake can ruin the results. Not being able to reproduce results or code is a major problem for ML teams. MLflow solves this by making it easier to track experiments and reproduce results.
Easy to get started: MLflow is simple to install and doesn’t need expensive hardware to run. Even beginners can use it to manage their models more effectively. For example, there is a video showing how to set up Charmed MLflow in under 5 minutes.
Environment agnostic: MLflow works with many different libraries and programming languages, including Python, R, and Java. It can be accessed through a REST API or a Command Line Interface (CLI), giving users flexibility in how they use it.
Integrations: While MLflow is powerful on its own, it also integrates well with other popular open-source tools like Spark, Kubeflow, PyTorch, and TensorFlow. This makes it easy to combine MLflow with other tools you may already be using.
Works anywhere: MLflow can run in any environment, including hybrid or multi-cloud setups. It also works on Kubernetes, so it’s flexible and can fit into many different workflows.
MLflow has several important parts that help manage machine learning (ML) projects:
MLflow Tracking
MLflow Tracking helps track and log everything about ML experiments, like parameters, metrics, and results. It stores all experiment data in one place, making it easy to compare different tests and reproduce results. It works well with popular ML libraries, so you can track experiments in many different setups.
MLflow Projects
MLflow Projects lets you package and share ML code in a standard format. It includes everything needed to run the project, like dependencies and entry points, making it easier to reproduce and share. This is useful for collaboration and running the same project on different platforms.
MLflow Models
MLflow Models helps package trained ML models so they can be used in different environments. It makes sure models can be saved and loaded the same way, no matter what tools were used to train them. You can deploy these models through APIs or other methods.
MLflow Model Registry
The Model Registry is a tool that lets you manage ML models. You can register, version, and track models in one place, making it easier to control the deployment and updates of models. It helps teams collaborate and manage model versions properly.
MLflow Pipeline
MLflow Pipeline allows you to create and run multi-step workflows for ML projects. You can define steps like data preparation, model training, and deployment. This makes the entire process consistent and easy to reproduce.
Benefits of MLflow
Easy Experiment Tracking: MLflow makes it simple to track experiments, compare models, and understand how changes affect performance. This improves collaboration and model development.
Better Collaboration: Teams can share experiments and learn from each other’s work. MLflow’s central system promotes teamwork and reduces duplicated efforts.
Simplified Model Deployment: MLflow Models allow you to package and deploy models easily across different platforms without compatibility problems.
Efficient Model Management: With the Model Registry, it’s easy to manage model versions and deployments, ensuring models are tested and tracked correctly.
Reproducible Workflows: MLflow Pipelines ensure that complex workflows, like training and deployment, can be repeated consistently.
Open Source and Flexible: MLflow is open-source and can be customized to fit different needs. It integrates with many other tools, giving users a lot of flexibility.