MLOps Course in Hyderabad | MLOps Training Online

1. What is MLOps? A Simple Overview Here is where your Career begins

2. Introduction to MLOps MLOps stands for Machine Learning Operations It’s the practice of managing the ML lifecycle Focuses on streamlining model deployment and monitoring Combines data science, DevOps, and IT practices

3. TABLE OF CONTENTS Helps move ML models from development to production faster Ensures models are continuously updated and optimized Reduces manual errors with automation Bridges the gap between data science and IT teams

4. Key Components of MLOps Version Control: Tracks changes in data, code, and models CI/CD Pipelines: Automates model building, testing, and deployment Monitoring: Tracks model performance in real time Collaboration: Encourages teamwork between developers and operations

5. MLOps vs. DevOps MLOps focuses on ML models, DevOps on software Data and model management are unique to MLOps MLOps handles continuous training and retraining of models Both aim to streamline deployment and improve scalability

6. How MLOps Improves ML Projects Automates the deployment of models, saving time Ensures that models adapt to changing data (model drift) Helps scale ML models efficiently across environments Provides transparency and reproducibility across teams

7. MLOps Tools You Can Use Kubeflow: An open-source platform for ML orchestration MLflow: For tracking experiments, models, and deployment TensorFlow Extended (TFX): End-to-end ML pipelines Docker & Kubernetes: Containerization and orchestration tools

8. Conclusion MLOps simplifies managing the ML lifecycle It enables faster, more reliable, and scalable AI solutions Collaboration and automation are key to MLOps success Adopt MLOps to ensure your AI systems stay effective over time

9. CONTAC Machine Learning Operations Training Address:- Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, Ameerpet, Hyderabad-1 Ph. No: +91-9989971070 Visit: WWW.Visualpath.in

10. THANK YOU Visit: www.visualpath.in