Mlops For Data Engineers Training Course: Data Pipelines & Ml Workflows

Introduction

Bridge the gap between data engineering and machine learning with our MLOps for Data Engineers Training Course. This program is designed to equip you with the essential skills to integrate data pipelines with machine learning workflows, enabling you to build and deploy scalable and reliable machine learning systems. In today's data-driven world, mastering MLOps is crucial for organizations seeking to operationalize machine learning models and derive actionable insights from their data. Our MLOps training course offers hands-on experience and expert guidance, empowering you to streamline the machine learning lifecycle and improve model deployment efficiency.

This data pipelines & ML workflows training delves into the core concepts of MLOps, covering topics such as data versioning, model deployment, and continuous integration/continuous delivery (CI/CD). You'll gain expertise in using industry-standard tools and techniques to integrate data pipelines with machine learning workflows, meeting the demands of modern machine learning projects. Whether you're a data engineer, machine learning engineer, or DevOps engineer, this MLOps for Data Engineers course will empower you to build and maintain robust machine learning systems.

Target Audience:

• Data Engineers
• Machine Learning Engineers
• DevOps Engineers
• Data Scientists
• System Administrators
• Software Developers
• Anyone needing MLOps skills

Course Objectives:

• Understand the fundamentals of MLOps for data engineers.
• Master data versioning and feature engineering for machine learning.
• Utilize CI/CD pipelines for machine learning model deployment.
• Implement model monitoring and performance tracking.
• Design and build scalable machine learning workflows.
• Optimize data pipelines for machine learning model training.
• Troubleshoot and address common issues in MLOps deployments.
• Implement model security and access control in MLOps environments.
• Integrate MLOps with various machine learning platforms and tools.
• Understand how to handle model reproducibility and governance.
• Explore advanced MLOps techniques (e.g., model serving, automated retraining).
• Apply real world use cases for MLOps in machine learning projects.
• Leverage MLOps tools and frameworks for efficient model deployment.

Duration

10 Days

Course content

Module 1: Introduction to MLOps for Data Engineers

• Fundamentals of MLOps for data engineers.
• Overview of data versioning, CI/CD, and model monitoring.
• Setting up an MLOps development environment.
• Introduction to MLOps tools and best practices.
• Best practices for MLOps.

Module 2: Data Versioning and Feature Engineering

• Mastering data versioning and feature engineering for machine learning.
• Utilizing data version control systems (e.g., DVC).
• Implementing feature engineering pipelines.
• Designing and building feature stores.
• Best practices for data versioning.

Module 3: CI/CD for Machine Learning Models

• Utilizing CI/CD pipelines for machine learning model deployment.
• Implementing automated model testing and validation.
• Designing and building deployment pipelines.
• Optimizing CI/CD for model reproducibility.
• Best practices for CI/CD.

Module 4: Model Monitoring and Performance Tracking

• Implementing model monitoring and performance tracking.
• Utilizing monitoring tools and metrics.
• Designing and building performance dashboards.
• Optimizing monitoring for model drift detection.
• Best practices for model monitoring.

Module 5: Scalable Machine Learning Workflows

• Designing and building scalable machine learning workflows.
• Utilizing orchestration tools (e.g., Kubeflow, Airflow).
• Implementing distributed model training.
• Optimizing workflows for large-scale data processing.
• Best practices for scalable workflows.

Module 6: Data Pipelines for Model Training

• Optimizing data pipelines for machine learning model training.
• Utilizing data transformation and preprocessing.
• Implementing data validation and quality checks.
• Designing efficient data pipelines.
• Best practices for training pipelines.

Module 7: Troubleshooting MLOps Deployments

• Debugging common issues in MLOps deployments.
• Analyzing model logs and error messages.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common deployment errors.
• Best practices for troubleshooting.

Module 8: Model Security and Access Control

• Implementing model security and access control in MLOps environments.
• Utilizing IAM roles and policies.
• Designing and building secure model deployments.
• Optimizing security for model protection.
• Best practices for model security.

Module 9: Integration with ML Platforms

• Integrating MLOps with various machine learning platforms and tools.
• Utilizing cloud-based ML platforms (e.g., AWS SageMaker, Google Vertex AI).
• Implementing integration with model serving frameworks.
• Optimizing integration for model deployment.
• Best practices for integration.

Module 10: Model Reproducibility and Governance

• Understanding how to handle model reproducibility and governance.
• Utilizing model metadata and lineage tracking.
• Implementing model versioning and audit trails.
• Designing reproducible model deployments.
• Best practices for reproducibility.

Module 11: Advanced MLOps Techniques

• Exploring advanced MLOps techniques (model serving, automated retraining).
• Utilizing model serving frameworks (e.g., TensorFlow Serving, Seldon Core).
• Implementing automated model retraining pipelines.
• Designing and building advanced MLOps solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing MLOps for real-time fraud detection.
• Utilizing MLOps for recommendation systems.
• Implementing MLOps for natural language processing.
• Utilizing MLOps for computer vision applications.
• Best practices for real-world applications.

Module 13: MLOps Tools Implementation

• Utilizing MLOps tools and frameworks (MLflow, Kubeflow Pipelines).
• Implementing model deployment with specific tools.
• Designing and building automated workflows.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Model Performance Monitoring

• Implementing model performance monitoring.
• Utilizing monitoring tools and metrics.
• Designing and building performance dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for monitoring.

Module 15: Future Trends in MLOps

• Emerging trends in MLOps.
• Utilizing AI for MLOps automation.
• Implementing MLOps in cloud-native environments.
• Best practices for future applications.

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.