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tl;dr Talks about agentic systems, LLMs, MLflow and LangChain in production, RAGs and GenAI workflows.

This meetup focuses on turning machine learning from notebooks into production systems. We explore ML and AI Engineering, MLOps and LLMOps, GenAI, agentic systems, MLflow, Databricks - helping teams ship reliable, scalable AI solutions.

This meetup is for engineers and data scientists who want to move beyond prototypes and build real-world, production-grade machine learning systems.

We focus on the practical side of AI/ML:

  • How to productionize Jupyter notebook workflows
  • How to manage experiments, models, and pipelines with MLflow
  • How to deploy and operate ML systems on platforms like Databricks
  • How to build and scale GenAI applications (LLMs, RAG, agents)
  • How to design reliable, testable, and maintainable ML pipelines

This is NOT a research meetup.
This is about shipping ML to production.

👥 Who is this for?

  • ML Engineers
  • Data Engineers moving into ML and AI
  • Data Scientists who want to productionize their work
  • Platform engineers building ML infrastructure

⚙️ Topics we'll cover

  • MLflow (tracking, registry, deployments)
  • Databricks, Snowflake and other modern Data and AI platforms
  • MLOps/LLMOps best practices
  • Agentic AI
  • CI/CD for ML/AI pipelines
  • Feature engineering & feature stores
  • Model monitoring & observability
  • GenAI systems (RAG, evaluation, orchestration)
  • Reproducibility & experiment management

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