Week 1: Experiment Tracking with MLflow

Overview

Understand experiment tracking by implementing an MLflow REST client in Rust.

Topics

#TypeTitlePlatformDuration
1.1VideoThe Reproducibility CrisisConcept8 min
1.2VideoMLflow Architecture: Tracking, Registry, ProjectsDatabricks10 min
1.3LabCreate Experiments in DatabricksDatabricks30 min
1.4VideoMLflow REST Protocol Deep DiveConcept10 min
1.5LabBuild MLflow Client in RustSovereign40 min
1.6VideoAutologging and Framework IntegrationDatabricks8 min
1.7VideoArtifact Storage: DBFS, S3, Unity CatalogDatabricks8 min
1.8LabCompare: Databricks MLflow vs Rust ClientBoth25 min
1.9QuizExperiment Tracking Fundamentals—15 min

Sovereign AI Stack Components

  • reqwest for HTTP client
  • serde for JSON serialization
  • pacha concepts for artifact storage

Key Concepts

MLflow Tracking

  • Experiments organize related runs
  • Runs contain parameters, metrics, and artifacts
  • Metrics can be logged at each training step

REST API

  • POST /api/2.0/mlflow/experiments/create
  • POST /api/2.0/mlflow/runs/create
  • POST /api/2.0/mlflow/runs/log-metric
  • POST /api/2.0/mlflow/runs/log-batch