Mlops: Machine Learning Operations Training Course: Deploy & Manage Ml Models

Introduction

Streamline your machine learning workflows with our MLOps: Machine Learning Operations Training Course. This program is designed to equip you with the essential skills to deploy, monitor, and manage machine learning models in production, enabling you to build robust and scalable AI systems. In today's data-driven world, mastering MLOps is crucial for ensuring the reliability, efficiency, and maintainability of machine learning deployments. Our MLOps training course offers hands-on experience and expert guidance, empowering you to implement state-of-the-art MLOps practices.

This machine learning operations training delves into the core concepts of MLOps, covering topics such as CI/CD for machine learning, model monitoring, and infrastructure automation. You'll gain expertise in using industry-standard tools and platforms to deploy, monitor, and manage machine learning models in production, meeting the demands of modern enterprise AI projects. Whether you're a machine learning engineer, data scientist, or DevOps professional, this MLOps: Machine Learning Operations course will empower you to build and maintain high-performance ML systems.

Target Audience:

• Machine Learning Engineers
• Data Scientists
• DevOps Engineers
• AI Architects
• Software Developers
• Data Engineers
• Anyone needing MLOps skills

Course Objectives:

• Understand the fundamentals of MLOps: Machine Learning Operations.
• Master CI/CD pipelines for machine learning models.
• Utilize model monitoring and logging for production systems.
• Implement infrastructure automation for ML deployments.
• Design and build scalable and reliable ML systems.
• Optimize machine learning workflows for efficiency and speed.
• Troubleshoot and address common challenges in MLOps.
• Implement model versioning and reproducibility.
• Integrate MLOps practices into real-world projects.
• Understand how to handle security and compliance in ML deployments.
• Explore advanced MLOps techniques (e.g., feature stores, model serving).
• Apply real world use cases for MLOps in various domains.
• Leverage MLOps tools and platforms for efficient deployment.

Duration

10 Days

Course content

Module 1: Introduction to MLOps: Machine Learning Operations

• Fundamentals of MLOps: Machine Learning Operations.
• Overview of CI/CD, model monitoring, and infrastructure automation.
• Setting up an MLOps development environment.
• Introduction to MLOps tools and platforms.
• Best practices for MLOps.

Module 2: CI/CD Pipelines for Machine Learning Models

• Implementing CI/CD pipelines for ML models.
• Utilizing version control and automated testing.
• Designing and building automated deployment pipelines.
• Optimizing CI/CD for ML model updates.
• Best practices for CI/CD.

Module 3: Model Monitoring and Logging

• Implementing model monitoring for production systems.
• Utilizing logging and alerting for model performance.
• Designing and building monitoring dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for model monitoring.

Module 4: Infrastructure Automation for ML Deployments

• Implementing infrastructure automation for ML deployments.
• Utilizing containerization and orchestration (Docker, Kubernetes).
• Designing and building automated deployment scripts.
• Optimizing infrastructure for scalability and reliability.
• Best practices for infrastructure automation.

Module 5: Scalable and Reliable ML Systems

• Designing and building scalable and reliable ML systems.
• Implementing fault tolerance and high availability.
• Utilizing cloud-based ML platforms.
• Optimizing systems for production performance.
• Best practices for scalable systems.

Module 6: Optimization of ML Workflows

• Optimizing machine learning workflows for efficiency and speed.
• Utilizing automation and orchestration tools.
• Implementing parallel processing and distributed computing.
• Designing efficient ML pipelines.
• Best practices for workflow optimization.

Module 7: Troubleshooting MLOps Challenges

• Debugging common challenges in MLOps.
• Analyzing deployment and monitoring issues.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common MLOps challenges.
• Best practices for troubleshooting.

Module 8: Model Versioning and Reproducibility

• Implementing model versioning and reproducibility.
• Utilizing experiment tracking and artifact management.
• Designing and building reproducible ML pipelines.
• Optimizing versioning for model management.
• Best practices for versioning.

Module 9: Integration with Real-World Projects

• Integrating MLOps practices into real-world projects.
• Utilizing APIs and deployment services.
• Implementing real-time ML systems.
• Optimizing integration for business impact.
• Best practices for integration.

Module 10: Security and Compliance in ML Deployments

• Implementing security and compliance in ML deployments.
• Utilizing data encryption and access control.
• Designing and building secure ML systems.
• Optimizing deployments for regulatory compliance.
• Best practices for security.

Module 11: Advanced MLOps Techniques

• Implementing feature stores for feature management.
• Utilizing model serving frameworks (TensorFlow Serving, Seldon Core).
• Designing and building advanced MLOps pipelines.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing MLOps for e-commerce recommendations.
• Utilizing MLOps for fraud detection systems.
• Implementing MLOps for healthcare predictive models.
• Utilizing MLOps for financial trading systems.
• Best practices for real-world applications.

Module 13: MLOps Tools and Platforms Implementation

• Utilizing Kubeflow and MLflow for MLOps.
• Implementing MLOps with cloud-based platforms (AWS SageMaker, Google Cloud AI Platform).
• Designing and building MLOps pipelines with tools.
• Optimizing tool usage for efficient deployment.
• Best practices for tool implementation.

Module 14: Monitoring and Observability for ML Systems

• Implementing monitoring and observability for ML systems.
• Utilizing logging and metrics for performance analysis.
• Designing and building observability dashboards.
• Optimizing monitoring for proactive issue detection.
• Best practices for monitoring.

Module 15: Future Trends in MLOps

• Emerging trends in machine learning operations.
• Utilizing automated MLOps and AI orchestration.
• Implementing federated MLOps for distributed systems.
• Best practices for future MLOps.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.