MLflow: ML & LLM Experiment Tracking and Evaluation - AI Build & Learn #12

​Welcome to AI Build & Learn a weekly AI engineering stream where we pick a new topic and learn by building together.

​This event is about experiment tracking and model evaluation with MLflow, an open-source platform for managing the end-to-end machine learning lifecycle. MLflow covers experiment tracking, model registry, serving, and evaluation tools for both traditional ML and LLM applications.

​We'll explore MLflow's tracing and evaluation features for LLM workflows, tracking experiments and metrics, and how MLflow compares to other observability tools like Arize Phoenix (from last event).

​Some things to look up to get started:

• ​MLflow GitHub: /mlflow/mlflow

• ​MLflow docs: https://mlflow.org/docs/latest/index.html

​Reources

• ​GitHub: /sagecodes/ai-build-and-learn

• ​Events Calendar: https://luma.com/ai-builders-and-learners

• ​Slack (Discuss during the week): Flyte Slack Group

• ​Hosted by Sage Elliott: /sageelliott

​In this stream

• ​Intro to topic

• ​Community Discussion

• ​Practical examples

​Community challenge (optional)

​Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.

​Note on Flyte / Union

​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​Union: https://www.union.ai/

• ​Flyte: https://flyte.org/

​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).