MLflow

• Documentation

The core components of MLflow are:

Experiment Tracking 📝: A set of APIs to log models, params, and results in ML experiments and compare them using an interactive UI.

Model Packaging 📦: A standard format for packaging a model and its metadata, such as dependency versions, ensuring reliable deployment and strong reproducibility.

Model Registry 💾: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

Serving 🚀: Tools for seamless model deployment to batch and real-time scoring on platforms like Docker, Kubernetes, Azure ML, and AWS SageMaker.

Evaluation 📊: A suite of automated model evaluation tools, seamlessly integrated with experiment tracking to record model performance and visually compare results across multiple models.

Observability 🔍: Tracing integrations with various GenAI libraries and a Python SDK for manual instrumentation, offering smoother debugging experience and supporting online monitoring.

• CMD
  • mlflow ui

Dagshub

• Documentation

• dagshub.init(repo_owner='najmussakib', repo_name='mlflow-dvc-test', mlflow=True)

MLFLOW_TRACKING_URI=https://dagshub.com/najmussakib/mlflow-dvc-test.mlflow
MLFLOW_TRACKING_USERNAME=
MLFLOW_TRACKING_PASSWORD= \

Run this in the terminal to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/najmussakib/mlflow-dvc-test.mlflow

export MLFLOW_TRACKING_USERNAME=

export MLFLOW_TRACKING_PASSWORD=

AWS

• Set Mlflow in AWS:
  • set s3 bucket for artifacts
  • set EC2 instance for tracking server and install mlfow dependencies
  • aws configure for cli access