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.

• RSVP on the Luma to get direct live links and better reminders
• You can always find the live on YouTube: https://www.youtube.com/@sagecodes/streams

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

​​​Resources

• ​​​GitHub: https://github.com/sagecodes/ai-build-and-learn
• ​​​Events Calendar: https://luma.com/ai-builders-and-learners
• ​​​Slack (Discuss during the week): https://slack.flyte.org/
• ​​​Hosted by Sage Elliott: https://www.linkedin.com/in/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).