Mlflow with Helm and serve Train Model on kubernetes

Part 6: Keep Model in Mlflow remote Cluster

Part 1: Install Mlflow on Local Machine
Part 2: Train example model and keep in Mlflow Local Machine
Part 3: Expose example api on Local Machine
Part 4: API Transform for Model API
Part 5: Install Mlflow on GKE Cluster with helm
Part 6: Keep Model in Mlflow remote Cluster
Part 7: Serve Model API on Cluster

After we have Mlflow Tracking on GKE Cluster then we will train ML and keep it to Mlflow Tracking on GKE Cluster.

• Open Train example model from Part2: Train example model and keep in Mlflow Local Machine
• Code : https://github.com/dounpct/soc-ml-api
• Open mlflow tracking server on GKE

• Add New Experiments: soc-ml-default

• Open Train.py
• In terminal export

export MLFLOW_TRACKING_URI=http://mlflow-tracking.domain.com
export MLFLOW_TRACKING_USERNAME=user 
export MLFLOW_TRACKING_PASSWORD=pwd

• Run Train.py

• Check On Mlflow Tracking Server

• Have fun !!!

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Credit : TrueDigitalGroup

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