In the modern data ecosystem, mastering Data Engineering for Machine Learning Pipelines is a fundamental and mission-critical skill for translating the promise of AI into production-ready reality, as the success of any machine learning model is directly dependent on the quality, reliability, and accessibility of the data used for training and inference. This specialized expertise bridges the gap between raw data and actionable intelligence, enabling data scientists to focus on model development while ensuring a robust, automated, and scalable data foundation. This comprehensive training course is meticulously designed to equip data engineers, machine learning engineers, and data scientists with the advanced knowledge and practical strategies required to build, orchestrate, and manage end-to-end data pipelines that feed machine learning workflows, from feature engineering and validation to deployment and monitoring. Without robust expertise in Data Engineering for Machine Learning Pipelines, organizations risk model degradation, pipeline failures, and a significant barrier to scaling their AI initiatives, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Data Engineers and ETL Developers
• Machine Learning (ML) Engineers
• Data Scientists and ML Researchers
• DevOps Engineers with an interest in MLOps
• Cloud Architects and Systems Administrators
• Data and Analytics Managers
• Software Engineers working on data-driven applications
• Anyone responsible for building and maintaining the data foundation for ML.

Objectives

• Understand the role of data engineering within the machine learning lifecycle.
• Learn about the different stages of a modern ML data pipeline.
• Acquire skills in designing scalable and reliable data ingestion strategies.
• Comprehend techniques for effective feature engineering and transformation.
• Explore strategies for implementing data validation and quality checks.
• Understand the importance of using a feature store for managing features.
• Gain insights into orchestrating and automating ML pipelines using leading tools.
• Develop a practical understanding of building both batch and streaming pipelines.
• Master the principles of CI/CD and MLOps for ML data infrastructure.
• Acquire skills in monitoring data and model performance in production.
• Learn to apply best practices for data governance, security, and reproducibility.
• Comprehend techniques for optimizing data pipelines for performance and cost.
• Explore strategies for managing the full lifecycle of data assets for ML models.
• Understand the importance of collaborative development for data and ML teams.
• Develop the ability to lead and implement production-grade Data Engineering for Machine Learning Pipelines.

Course Content

Module 1: Introduction to the Machine Learning Data Lifecycle

• The MLOps lifecycle: from data to deployment.
• The critical role of data engineering in MLOps.
• Key concepts: features, labels, training data, serving data.
• Differentiating ML pipelines from traditional ETL pipelines.
• The challenges of data for machine learning models.

Module 2: Data Ingestion for ML

• Strategies for data ingestion: batch vs. streaming.
• Connecting to diverse data sources: databases, APIs, IoT sensors.
• Common data formats for ML: Parquet, Avro, TFRecords.
• Using cloud-native ingestion services (e.g., AWS Glue, Azure Data Factory).
• Ingesting semi-structured and unstructured data.

Module 3: Data Storage Architectures for ML

• Data lakes and their role as a raw data repository.
• Data warehouses and their use for structured data.
• The importance of a feature store for serving features.
• Designing a storage strategy for training data and inference data.
• Data governance and organization within storage systems.

Module 4: Data Transformation and Preprocessing

• The importance of data cleaning and imputation.
• Using tools like Apache Spark, Pandas, and dbt for large-scale transformations.
• Standardizing data for machine learning models.
• Building reproducible transformation pipelines.
• Best practices for handling large datasets in memory and on disk.

Module 5: Feature Engineering Fundamentals

• What are features and why are they important?
• Techniques for feature creation: one-hot encoding, binning, normalization.
• Automated feature engineering tools.
• The difference between online and offline feature serving.
• Feature selection strategies to improve model performance.

Module 6: Data Validation and Quality Assurance

• The cost of bad data in machine learning.
• Implementing data validation checks at each pipeline stage.
• Tools for data quality and testing (e.g., Great Expectations).
• Defining data quality metrics and thresholds.
• Setting up alerts for data anomalies and quality failures.

Module 7: Orchestrating Batch ML Pipelines

• The need for workflow orchestration.
• Introduction to Airflow, Dagster, and Prefect.
• Designing directed acyclic graphs (DAGs) for ML workflows.
• Scheduling and monitoring batch jobs.
• Handling dependencies and retries in pipelines.

Module 8: Building Streaming ML Pipelines

• The architecture of a real-time ML pipeline.
• Using streaming platforms like Apache Kafka and Apache Flink.
• Real-time feature engineering and inference.
• Handling late-arriving data and state management.
• Best practices for low-latency data processing.

Module 9: The Role of a Feature Store

• What is a feature store and why is it a key component of MLOps?
• The architecture of a feature store (offline and online).
• Storing, versioning, and serving features consistently.
• Preventing training-serving skew.
• Case studies of implementing a feature store.

Module 10: Model Training and Serving Pipelines

• Automating the model training workflow.
• Using MLOps platforms (e.g., MLflow, Kubeflow) for tracking experiments.
• Building a CI/CD pipeline for model deployment.
• A/B testing and canary deployments.
• Monitoring model performance post-deployment.

Module 11: Monitoring and Observability

• The importance of monitoring data quality and pipeline health.
• Detecting data drift and concept drift.
• Monitoring model predictions and performance.
• Setting up dashboards and alerts for key metrics.
• Logging and auditing for reproducibility and compliance.

Module 12: CI/CD and Version Control for ML Pipelines

• Applying DevOps principles to machine learning.
• Versioning data, code, and models.
• Using Git for code and pipeline management.
• Setting up automated testing and deployment workflows.
• Implementing a CI/CD pipeline for a simple ML project.

Module 13: Data Governance and Security for ML

• Data governance policies for ML data.
• Access control and permissions for sensitive data.
• Data lineage and reproducibility.
• Ethical considerations and bias detection in data.
• Compliance with data privacy regulations (e.g., GDPR).

Module 14: Cost Optimization and Scalability

• Strategies for optimizing compute resources (e.g., spot instances).
• Efficient data storage and partitioning.
• Balancing performance with cost.
• Scaling pipelines for large datasets and high-traffic models.
• Cloud-native services for cost-effective MLOps.

Module 15: Practical Workshop: Building an End-to-End MLOps Pipeline

• Participants work in teams to design and build an end-to-end ML pipeline.
• Exercise: ingest data, perform feature engineering, train a model, and deploy it.
• Automate the pipeline with an orchestration tool.
• Implement data validation and basic monitoring.
• Present the final project and discuss key MLOps challenges.

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.