MLOps Training Course in Chennai | MLOps Training

1. MLOps vs DevOps: Key Differences Understanding the distinctions between Machine Learning Operations and Development Operations is crucial for modern software and data teams. This presentation will highlight the unique aspects of each discipline. +91-7032290546

2. Introduction to MLOps & DevOps MLOps Defined DevOps Defined Practices combining Machine Learning (ML) and DevOps principles. A culture to automate and integrate software development and IT operations. Shared Goal Distinct Focus Both aim for faster and more reliable deployments. MLOps: ML model deployment, monitoring, and management. DevOps: Continuous delivery and integration of software. +91-7032290546

3. Key Differences in Goals MLOps Goals DevOps Goals Improve collaboration for ML model deployment. Enhance collaboration for software delivery. Deals with data pipelines, model monitoring. Focuses on CI/CD pipelines, automation. Ensures ML model reproducibility in production. Prioritizes system reliability. +91-7032290546

4. Data Handling & Model Lifecycle (MLOps) Data Versioning Model Training Emphasizes versioning and managing datasets rigorously. Models are continuously trained and tested for improvement. Feedback Loops Model Monitoring Continuous improvement requires data feedback loops. Models need performance evaluation after deployment.

5. Code vs Model in DevOps and MLOps Code Model DevOps MLOps Focuses on software code updates and bug fixes. Handles machine learning model lifecycle (training, tuning, retraining). DevOps tools manage source code. MLOps tools manage datasets, models, and model versioning. Model performance and drift monitoring are crucial in MLOps for ongoing accuracy. +91-7032290546

6. Automation in MLOps vs DevOps DevOps Automation Automates software deployment and infrastructure management. MLOps Automation Automates the entire ML pipeline, from data to deployment. Shared CI/CD Both use CI/CD, but MLOps adds model validation. +91-7032290546

7. Collaboration & Roles in MLOps vs DevOps DevOps Teams Developers, IT operations, and quality assurance work together. MLOps Teams Data scientists, ML engineers, and software developers collaborate. Integration Goals DevOps integrates development and operations workflows. MLOps integrates data science with IT infrastructure. +91-7032290546

8. Testing & Validation in MLOps DevOps Testing Focuses on automated unit, integration, and system tests for code reliability. MLOps Validation Requires model validation, performance testing, and A/B testing for accuracy. Testing data for ML models is crucial for generalization. Post-deployment model validation is important for MLOps to ensure ongoing accuracy. +91-7032290546

9. Challenges in MLOps vs DevOps Model Versioning 1 Ensuring consistent model versions across environments. Reproducibility 2 Recreating ML experiments and results reliably. Data Drift 3 Handling changes in data distribution over time. Model Bias 4 Ensuring fairness and avoiding unintended biases in models. DevOps primarily handles software deployment and scaling. MLOps also faces challenges with model retraining, monitoring performance, and scalability for ML-specific workflows. +91-7032290546

10. Tools in MLOps vs DevOps CI/CD Jenkins, GitLab CI Kubeflow, MLflow Orchestration Kubernetes, Docker Kubeflow, Argo Infrastructure Terraform, Ansible SageMaker, Vertex AI Experiment Tracking N/A MLflow, Weights & Biases DevOps tools focus on CI/CD and infrastructure automation. MLOps tools integrate with ML workflows (data ingestion, model training, deployment). Some tools like Kubernetes are common, but with specific use cases in each domain. +91-7032290546

11. Contact Us MLOps Team Address: Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, Ameerpet, Hyderabad-1 Phone:+91-7032290546 Website:WWW.VISUALPATH.IN Email:online@visualpath.in +91-7032290546

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