Machine Learning Operations Training - MLOps Training.pptx

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1. The Power of MLOps: Streamlining AI Pipelines

2. Introduction to MLOps MLOps = Machine Learning + DevOps Framework for managing ML lifecycle efficiently Bridges data science and production operations Enables faster and more reliable model deployment

3. Why MLOps Matters Manual ML workflows are time-consuming and error-prone Lack of standardization leads to inefficiency MLOps automates and monitors every ML stage Improves collaboration between data scientists and engineers

4. Core Components of MLOps Data collection and preprocessing Model training and versioning Continuous Integration/Continuous Deployment (CI/CD) Monitoring, governance, and retraining

5. Benefits of Using MLOps Speeds up model development cycles Ensures scalability and reproducibility Improves accuracy through consistent monitoring Reduces deployment risks

6. Tools and Technologies in MLOps Popular tools: MLflow , Kubeflow , Airflow, TFX, DVC Cloud MLOps : AWS Sagemaker , Azure ML, Google Vertex AI Containerization using Docker and Kubernetes Version control and automation pipelines

7. MLOps in Real-World Applications Real-time fraud detection and recommendations Predictive maintenance and automation Scalable AI in healthcare, finance, and retail Continuous learning from live data

8. Conclusion & Future of MLOps MLOps is essential for production-ready AI systems Promotes collaboration, automation, and trust in ML Future: More automation, governance, and cloud-native pipelines Build smarter AI workflows with MLOps today

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