Data Engineering for AI/ML: Building Scalable Data Pipelines Training Course

Introduction

The success of any Artificial Intelligence (AI) and Machine Learning (ML) initiative hinges on the availability of high-quality, reliable, and scalable data. This Data Engineering for AI/ML: Building Scalable Data Pipelines Training Course is specifically designed for data engineers, data architects, MLOps engineers, and anyone involved in the data infrastructure supporting AI/ML workflows. The course provides an in-depth understanding of the critical role of data engineering in the AI/ML lifecycle, focusing on building robust, automated, and scalable data pipelines.

Participants will gain hands-on expertise in the tools and techniques required to collect, transform, store, and manage vast amounts of data for AI and ML model training and inference. The curriculum covers essential topics such as ETL/ELT processes, data warehousing, data lakes, streaming data technologies, feature stores, and MLOps best practices for data management. By mastering these data engineering principles, you will be equipped to design and implement the foundational data infrastructure that accelerates AI/ML development, ensuring models are built on clean, consistent, and continuously available data.

Target Audience

• Data Engineers and Data Architects.
• MLOps Engineers and DevOps Engineers for ML.
• Cloud Engineers and Infrastructure Engineers supporting AI/ML.
• Data Scientists looking to understand data pipeline complexities.
• Software Engineers with an interest in big data systems for AI.
• Anyone responsible for managing data for AI/ML applications.

Duration

10 days

Course Objectives

1. Understand the critical role of data engineering in the AI/ML lifecycle and its importance for model performance.
2. Master the concepts and implementation of scalable data ingestion and processing techniques.
3. Gain proficiency in designing and managing modern data architectures, including data warehouses and data lakes.
4. Learn to build robust ETL/ELT pipelines for data transformation and preparation for AI/ML.
5. Explore streaming data technologies and their application in real-time AI/ML scenarios.
6. Understand the principles and implementation of feature stores for consistent feature management.
7. Apply MLOps best practices for data versioning, validation, and pipeline automation.
8. Design and implement secure, efficient, and scalable data infrastructure for AI/ML systems.

Course Content

Module 1. Introduction to Data Engineering for AI/ML

• The role of data in the AI/ML lifecycle: From raw data to model deployment
• Data engineering vs. data science vs. ML engineering: Defining roles and responsibilities
• Challenges in data for AI/ML: Volume, velocity, variety, veracity
• Importance of scalable, reliable, and consistent data pipelines
• Overview of the modern data stack for AI/ML

Module 2. Data Storage Solutions for AI/ML

• Relational Databases vs. NoSQL Databases: When to use which for AI/ML data
• Data Warehouses: Concepts, OLAP, and technologies (e.g., Snowflake, BigQuery, Redshift)
• Data Lakes: Concepts, benefits, and technologies (e.g., S3, ADLS)
• Lakehouses: Combining the best of data lakes and warehouses
• Choosing the right storage solution based on data type, access patterns, and scale

Module 3. Data Ingestion Strategies

• Batch data ingestion: Principles, tools (e.g., Apache Nifi, Talend, custom scripts)
• Streaming data ingestion: Concepts, use cases, and challenges
• Message queues and stream processing frameworks (e.g., Apache Kafka, RabbitMQ, Kinesis, Pub/Sub)
• Real-time data processing patterns for immediate AI/ML inference
• Data sources: APIs, databases, files, web scraping, IoT devices

Module 4. ETL/ELT Pipelines for AI/ML

• Understanding ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) paradigms
• Data extraction techniques: Database connectors, API integrations, file readers
• Data transformation: Cleaning, normalization, aggregation, feature engineering
• Data loading into target systems for AI/ML consumption
• Building ETL/ELT pipelines with Python (Pandas, Dask) and SQL

Module 5. Big Data Processing Frameworks

• Introduction to Apache Spark: RDDs, DataFrames, Spark SQL
• Distributed processing concepts: MapReduce, distributed file systems
• Using Spark for large-scale data transformation and aggregation
• PySpark for data engineering workflows
• Other big data tools: Hive, Presto (overview)

Module 6. Data Orchestration and Workflow Management

• The need for workflow orchestration in complex data pipelines
• Introduction to Apache Airflow: DAGs, operators, sensors, scheduling
• Building and managing data workflows with Airflow
• Other orchestration tools: Luigi, Prefect, AWS Step Functions, Azure Data Factory
• Monitoring and logging data pipelines

Module 7. Feature Stores for AI/ML

• The concept of a feature store: Centralized management of ML features
• Benefits of feature stores: Consistency, reusability, freshness, offline/online serving
• Architecture of a feature store: Offline store, online store, feature definition
• Popular feature store solutions (e.g., Feast, Tecton)
• Designing and implementing features for training and inference

Module 8. Streaming Data for Real-time AI/ML

• Real-time vs. batch processing for AI/ML
• Architecting streaming data pipelines with Kafka/Kinesis and Spark Streaming/Flink
• Real-time feature engineering from streaming data
• Online inference and model serving with low latency
• Use cases: Fraud detection, recommendation systems, anomaly detection

Module 9. Data Quality, Validation & Governance

• Importance of data quality for AI/ML model performance
• Data validation techniques: Schema validation, data type checks, range checks
• Data profiling and anomaly detection in data pipelines
• Data governance for AI/ML: Data lineage, access control, compliance (GDPR, HIPAA)
• Implementing data quality checks within pipelines

Module 10. MLOps for Data Pipelines

• Data versioning: Tracking changes to datasets used for ML models
• Data drift and concept drift: Detecting changes in data distribution
• Automated data retraining triggers based on data changes
• Continuous Integration/Continuous Delivery (CI/CD) for data pipelines
• Monitoring data pipelines and model performance in production

Module 11. Cloud-Based Data Engineering for AI/ML

• Leveraging cloud services for data engineering: AWS, Azure, Google Cloud
• AWS: S3, Kinesis, Glue, EMR, Redshift, Athena, SageMaker Data Wrangler
• Azure: Data Lake Storage, Event Hubs, Data Factory, Databricks, Synapse Analytics
• Google Cloud: Cloud Storage, Pub/Sub, Dataflow, Dataproc, BigQuery, Vertex AI Data Labeling
• Cost optimization strategies for cloud data infrastructure

Module 12. Advanced Topics & Future Trends

• Data Mesh and Data Fabric architectures for decentralized data ownership
• Data engineering for multimodal AI and unstructured data (images, video, audio)
• Data governance automation and active metadata management
• Graph databases for relationship analysis in AI/ML data
• The evolving role of the data engineer in the age of advanced AI

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank