Skip to content

Latest commit

 

History

History
56 lines (36 loc) · 2.44 KB

README.md

File metadata and controls

56 lines (36 loc) · 2.44 KB
Raw

MlFlow_Foundational_Training

Foundational Training on MlFlow and Data Science on Databricks.

Owner: Anastasia Prokaieva, Sr SSE (Data Science, MlOps) Databricks, 2022

Demo includes:

  • Delta Lake
  • Databricks Feature store
    • offline store
    • example of an online store publication
  • MlFlow
    • tracking
    • models
    • serving
  • Databricks AutoML

End-to-End MLOps demo with MLFlow for TelcoChurn use case

Challenges moving ML project into production

Moving ML project from a standalone notebook to a production-grade data pipeline is complex and require multiple competencies.

Having a model up and running in a notebook isn't enough. We need to cover the end to end ML Project life cycle and solve the following challenges:

  • Update data over time (production-grade ingestion pipeline)
  • How to save, share and re-use ML features in the organization
  • How to ensure a new model version respect quality standard and won't break the pipeline
  • Model governance: what is deployed, how is it trained, by who, which data?
  • How to monitor and re-train the model...

In addition, these project typically invole multiple teams, creating friction and potential silos

  • Data Engineers, in charge of ingesting, preparing and exposing the data
  • Data Scientist, expert in data analysis, building ML model
  • ML engineers, setuping the ML infrastructure pipelines (similar to devops)

This has a real impact on the business, slowing down projects and preventing them from being deployed in production and bringing ROI.

What's MLOps ?

MLOps is is a set of standards, tools, processes and methodology that aims to optimize time, efficiency and quality while ensuring governance in ML projects.

MLOps orchestrate a project life-cycle and adds the glue required between the component and teams to smoothly implement such ML pipelines.

Databricks is uniquely positioned to solve this challenge with the Lakehouse pattern. Not only we bring Data Engineers, Data Scientists and ML Engineers together in a unique platform, but we also provide tools to orchestrate ML project and accelerate the go to production.

MLOps pipeline we'll implement

In this demo, we'll implement a full MLOps pipeline, step by step:

Teh original data source is coming from : https://github.com/IBM/telco-customer-churn-on-icp4d/blob/master/data/Telco-Customer-Churn.csv