MLflow is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring that each phase is manageable, traceable, and reproducible.
In each of the sections below, you will find overviews, guides, and step-by-step tutorials to walk you through the features of MLflow and how they can be leveraged to solve real-world MLOps problems.
If this is your first time exploring MLflow, the tutorials and guides here are a great place to start. The emphasis in each of these is getting you up to speed as quickly as possible with the basic functionality, terms, APIs, and general best practices of using MLflow in order to enhance your learning in area-specific guides and tutorials.
A great place to start to learn the fundamentals of MLflow Tracking! Learn in 5 minutes how to log, register, and load a model for inference.
Learn how to get started with the basics of MLflow in a step-by-step instructional tutorial that shows the critical path to logging your first model
Short on time? This is a no-frills quickstart that shows how to leverage autologging during training and how to load a model for inference
Explore the comprehensive LLM-focused native support in MLflow. From MLflow AI Gateway to the Prompt Engineering UI and native LLM-focused MLflow flavors like open-ai, transformers, and sentence-transformers, the tutorials and guides here will help to get you started in leveraging the benefits of these powerful natural language deep learning models. You’ll learn how MLflow simplifies both using LLMs and developing solutions that leverage LLMs. Important tasks such as prompt development, evaluation of prompts, comparison of foundation models, fine-tuning and logging LLMs, and setting up production-grade interface servers are all covered by MLflow.
Explore the guides and tutorials below to start your journey!
Explore the features and functions of MLflow's Prompt Engineering UI for development, testing, evaluation, and deployment of validated prompts for your LLM use cases.
Learn how to configure, setup, deploy, and use the MLflow AI Gateway for testing and production use cases of both SaaS and custom open-source LLMs.
Dive into the intricacies of MLflow's LLM Tracking system. From capturing prompts to monitoring generated outputs, discover how MLflow provides a holistic solution for managing LLM interactions.
Learn how to evaluate LLMs and LLM-powered solutions with MLflow Evaluate.
Explore the nuances of packaging and deploying advanced LLMs in MLflow using custom PyFuncs. This guide delves deep into managing intricate model behaviors, ensuring seamless and efficient LLM deployments.
Dive into MLflow’s robust framework for evaluating the performance of your ML models.
With support for traditional ML evaluation (classification and regression tasks), as well as support for evaluating large language models (LLMs), this suite of APIs offers a simple but powerful automated approach to evaluating the quality of the model development work that you’re doing.
In particular, for LLM evaluation, the mlflow.evaluate() API allows you to validate not only models, but providers and prompts. By leveraging your own datasets and using the provided default evaluation criteria for tasks such as text summarization and question answering, you can get reliable metrics that allow you to focus on improving the quality of your solution, rather than spending time writing scoring code.
Visual insights are also available through the MLflow UI, showcasing logged outputs, auto-generated plots, and model comparison artifacts.
See how MLflow can help manage the full lifecycle of your Deep Learning projects. Whether you’re using frameworks like TensorFlow (tensorflow), Keras (keras), PyTorch (torch), Fastai (fastai), or spaCy (spacy), MLflow offers first-class support, ensuring seamless integration and deployment. Additionally, generic packaging frameworks that have native MLflow integration such as ONNX (onnx) grealy help to simplify the deployment of deep learning models to a wide variety of deployment providers and environments.
Paired with MLflow’s streamlined APIs and comparative UI, you are equipped with everything needed to manage, track, and optimize your deep learning workflows.
Leverage the power of MLflow for all your Traditional Machine Learning needs. Whether you’re working with supervised, unsupervised, statistical, or time series data, MLflow streamlines the process by providing an integrated environment that supports a large array of widely-used libraries like Scikit-learn (sklearn), SparkML (spark), XGBoost (xgboost), LightGBM (lightgbm), CatBoost (catboost), Statsmodels, Prophet, and Pmdarima. With MLflow, you not only get APIs tailored for these libraries but also a user-friendly UI to compare various runs, ensuring that your model tuning and evaluation phases are both efficient and insightful.
This in-depth guide will show you how to leverage some core functionality in MLflow to keep your tuning iterations organized and searchable, all while covering a number of features within MLflow that cater to the needs of this common activity.
Dive into the foundational aspects of MLflow's custom `pyfunc` to encapsulate, manage, and invoke models from any framework. This guide elucidates the versatility of `pyfunc`, highlighting how it bridges the gap between supported named flavors and bespoke model requirements.
In today’s ML-driven landscape, the ability to deploy models seamlessly and reliably is crucial. MLflow offers a robust suite tailored for this very purpose, ensuring that models transition from development to production without a hitch. Whether you’re aiming for real-time predictions, batch analyses, or interactive insights, MLflow’s deployment capabilities have got you covered. From managing dependencies and packaging models with their associated code to offering a large ecosystem of deployment avenues like local servers, cloud platforms, or Kubernetes clusters, MLflow ensures your models are not just artifacts but actionable, decision-making tools.
Dive deep into the platform’s offerings, explore the tutorials, and harness the power of MLflow for efficient model serving.
This guide showcases the seamless end-to-end process of training a linear regression model, packaging it in a reproducible format, and deploying to a Kubernetes cluster using MLflow. Explore how MLflow simplifies model deployment to production environments.