MLOps Course in Ameerpet | MLOps Training

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MLOPS

• (Machine Learning Operations)

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Introduction to MLOps

• MLOps (Machine Learning Operations) bridges ML
  development and operational deployment.
• Combines principles of DevOps, Data Engineering,
  and Machine Learning.
• Focuses on automation, scalability, monitoring,
  and collaboration.
• Critical for deploying reliable, repeatable, and
  auditable ML workflows.

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Why MLOps Matters

• Reduces time from model development to production
  deployment.
• Ensures reproducibility and consistency across
  environments.
• Enables scalable management of ML lifecycle
  stages.
• Enhances collaboration between data scientists,
  ML engineers, and ops teams.

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Key Components of MLOps

• Versioning Tracks datasets, code, and model
  changes.
• CI/CD for ML Automates model testing, training,
  and deployment pipelines.
• Monitoring Tracks model drift, performance, and
  operational metrics.
• Governance Ensures compliance, auditability, and
  access control.

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MLOps Lifecycle

• Data Engineering Data collection, validation,
  transformation pipelines.
• Model Development Experimentation, tuning, and
  training.
• Model Validation Testing against production-like
  scenarios.
• Model Deployment Monitoring Serving, scaling,
  drift detection, and alerting.

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Tools and Technologies

• Pipeline Orchestration Kubeflow, Airflow, MLflow
  Pipelines.
• Model Deployment Seldon Core, KFServing,
  BentoML.
• Monitoring Logging Prometheus, Grafana,
  Evidently AI.
• Version Control DVC, Git, MLflow, Weights
  Biases.

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MLOps in Production

• Automates retraining based on new data or
  performance decay.
• Uses blue-green or canary deployments to minimize
  risk.
• Enables rollback to previous model versions if
  issues arise.
• Incorporates security checks and CI/CD
  validations for safe updates.

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Challenges in MLOps

• Handling data drift and concept drift in
  real-time models.
• Managing complex dependencies and environments.
• Ensuring data and model reproducibility at scale.
• Aligning cross-functional teams around shared
  goals.

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Conclusion

• Start small with automated and reproducible ML
  pipelines.
• Leverage containerization, orchestration, and
  modular architecture.
• Integrate fairness, explainability, and
  governance from the start.

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Contact

• MLOPS
• Address- Flat no 205, 2nd Floor,
• Nilgiri Block, Aditya Enclave,
• Ameerpet, Hyderabad-1 
• Ph. No 91-9989971070 
• Visit WWW.Visualpath.in
• E-Mail online_at_visualpath.in

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THANK YOU
Visit www.visualpath.in
91-7032290546