MLOps Course in Ameerpet - MLOps Training
                     MLOPS
(Machine Learning Operations)
+91-7032290546
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.
+91-7032290546
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.
+91-7032290546
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.
+91-7032290546
Conclusion
• Start small with automated and 
reproducible ML pipelines.
• Leverage containerization, orchestration, 
and modular architecture.
• Integrate fairness, explainability, and 
governance from the start.
+91-7032290546
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: [email protected]
+91-7032290546
THANK YOU
Visit: www.visualpath.in
+91-7032290546