Mlops: Machine Learning Operations Training Course - Production Ai Mastery

Introduction

Streamline and scale your AI initiatives with our MLOps: Machine Learning Operations Training Course. This program provides a comprehensive guide on how to deploy, monitor, and manage machine learning models in production environments, enabling you to build robust and reliable AI systems. In today's fast-paced data science landscape, mastering MLOps is crucial for bridging the gap between model development and real-world deployment. Our MLOps training course offers hands-on experience and expert guidance, empowering you to create efficient and scalable machine learning pipelines.

This Machine Learning Operations training delves into the core concepts of MLOps, covering topics such as continuous integration, continuous delivery, and model monitoring. You'll gain expertise in using industry-standard tools and techniques to automate and optimize the machine learning lifecycle. Whether you're a machine learning engineer, data scientist, or DevOps professional, this MLOps course will empower you to effectively deploy, monitor, and manage machine learning models in production environments.

Target Audience:

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

Course Objectives:

• Understand the fundamentals of Machine Learning Operations (MLOps).
• Master continuous integration and continuous delivery (CI/CD) for machine learning.
• Utilize containerization and orchestration tools for model deployment.
• Implement model monitoring and performance tracking.
• Automate the machine learning lifecycle for efficient production.
• Deploy and manage machine learning models in cloud environments.
• Troubleshoot and debug production machine learning pipelines.
• Implement data security and access control in MLOps workflows.
• Integrate MLOps with existing data engineering and DevOps practices.
• Understand how to scale and optimize machine learning deployments.
• Implement model versioning and rollback strategies.
• Explore advanced MLOps techniques for complex models.
• Apply real world use cases for MLOps.

Duration

10 Days

Course content

Module 1: Introduction to Machine Learning Operations (MLOps)

• Fundamentals of MLOps and its importance.
• Overview of the machine learning lifecycle.
• Setting up an MLOps development environment.
• Introduction to MLOps tools and frameworks.
• Best practices for MLOps.

Module 2: Continuous Integration (CI) for Machine Learning

• Implementing automated testing for machine learning models.
• Utilizing version control for model artifacts and code.
• Building automated model training pipelines.
• Integrating CI with machine learning frameworks.
• Best practices for CI in machine learning.

Module 3: Continuous Delivery (CD) for Machine Learning

• Automating model deployment and release processes.
• Utilizing containerization tools (Docker) for model packaging.
• Implementing infrastructure as code (IaC) for deployment.
• Deploying models to staging and production environments.
• Best practices for CD in machine learning.

Module 4: Containerization and Orchestration for Model Deployment

• Utilizing Docker for containerizing machine learning models.
• Implementing Kubernetes for model orchestration.
• Deploying models as microservices.
• Managing containerized environments.
• Best practices for containerization.

Module 5: Model Monitoring and Performance Tracking

• Implementing real-time model monitoring.
• Tracking model performance metrics.
• Setting up alerts for model drift and degradation.
• Utilizing monitoring tools and dashboards.
• Best practices for model monitoring.

Module 6: Automation of the Machine Learning Lifecycle

• Automating data preprocessing and feature engineering.
• Implementing automated model retraining and updating.
• Utilizing workflow management tools (Airflow, Kubeflow).
• Building end-to-end machine learning pipelines.
• Best practices for automation.

Module 7: Model Deployment in Cloud Environments

• Deploying machine learning models on AWS, Azure, and GCP.
• Utilizing cloud-based MLOps services.
• Managing cloud resources for machine learning.
• Best practices for cloud deployment.

Module 8: Troubleshooting and Debugging Production Pipelines

• Debugging production machine learning pipelines.
• Analyzing model errors and performance issues.
• Utilizing debugging tools and techniques.
• Identifying and resolving pipeline bottlenecks.
• Best practices for troubleshooting.

Module 9: Data Security and Access Control in MLOps

• Implementing data security in MLOps workflows.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Auditing and compliance in MLOps.
• Best practices for data security.

Module 10: Integrating MLOps with Data Engineering and DevOps

• Integrating MLOps with data engineering pipelines.
• Utilizing DevOps practices for machine learning.
• Building collaborative MLOps environments.
• Best practices for integration.

Module 11: Scaling and Optimizing Machine Learning Deployments

• Scaling machine learning models for high-throughput applications.
• Optimizing model performance and resource utilization.
• Implementing load balancing and autoscaling.
• Best practices for scaling.

Module 12: Model Versioning and Rollback

• Implementing model versioning and tracking.
• Utilizing model registry and metadata management.
• Implementing rollback strategies for model updates.
• Best practices for model versioning.

Module 13: Advanced MLOps Techniques

• Implementing feature stores for feature management.
• Utilizing model explainability and interpretability tools.
• Implementing federated learning and privacy-preserving MLOps.
• Advanced techniques for large-scale MLOps.

Module 14: MLOps and Data Governance

• Implementing data governance policies in MLOps.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in MLOps

• Emerging trends in MLOps research and applications.
• Utilizing AI and automation in MLOps workflows.
• Implementing continuous training and adaptive models.
• 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.