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Unit No.506, 5th Floor Solitaire Business Hub, Survey Number: 27/1, Balewadi high Street, Baner, Pune-411045, India

4904 Kentwood Drive, Marietta, GA 30068, USA

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ML Ops

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ML Ops (Machine Learning Operations)

1. Model Development

• Keep track of evolving datasets using tools like DVC or LakeFS to ensure reproducibility and avoid data drift.

• Use MLFlow, Weights & Biases, or SageMaker Experiments to log parameters, metrics, and artifacts for every model run.

• Jupyter + GitHub integration or tools like Deepnote allow team-based iteration with better version control.

• Define and monitor baseline performance (e.g., accuracy, F1) before promoting models — helps prevent regressions.

2. Model Training & Tuning

• Use Kubeflow Pipelines, Metaflow, or Vertex AI to automate data preprocessing, training, and evaluation workflows.

• Integrate tools like Optuna or Ray Tune for scalable, distributed hyperparameter tuning across compute clusters.

• Leverage frameworks like Horovod or PyTorch DDP to accelerate training over GPUs/TPUs across nodes.

• Dynamically scale compute using Kubernetes, spot instances, or autoscaling clusters for cost-efficiency.

3. Model Deployment

•Apply DevOps principles using tools like GitHub Actions, Jenkins, and ArgoCD to automate testing and deployment.

• Package models using Docker or MLFlow for standardized, repeatable deployment across environments.

• Use tools like KFServing, TorchServe, or Triton Inference Server for scalable, low-latency model serving.

• Test new models safely in production by routing a portion of traffic before full rollout.

4. Monitoring & Governance

•Track drift, accuracy, latency, and throughput using tools like WhyLabs, EvidentlyAI, or Prometheus + Grafana.

• Implement real-time checks to detect shifts in input features or target labels, triggering retraining as needed.

• Maintain full lineage (data, code, model, infra) for compliance, using tools like Pachyderm or Great Expectations.

• Incorporate fairness, explainability (SHAP, LIME), and bias detection into pipelines for ethical AI practices.

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Summary: ML Ops

A discipline that brings together machine learning, DevOps, and data engineering to streamline and automate the entire ML lifecycle. It highlights key functions such as pipeline automation, infrastructure management, monitoring, compliance, and lifecycle governance.

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Unit No.506, 5th Floor Solitaire Business Hub, Survey Number: 27/1, Balewadi high Street, Baner,Pune-411045

4904 Kentwood Drive, Marietta, GA 30068 

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