As artificial intelligence and advanced analytics continue to transform industries, data engineering has emerged as a critical backbone for enabling intelligent systems and decision-making. This 10-day intensive training course on Data Engineering for AI and Analytics Applications is designed to equip professionals with the technical capabilities and architectural understanding to build high-performance, scalable data infrastructure tailored for machine learning, deep learning, and real-time analytics workloads. The course combines hands-on instruction in modern data technologies such as Apache Spark, Kafka, cloud storage, and MLOps frameworks, while emphasizing the optimization of data pipelines, feature engineering, model serving infrastructure, and metadata governance for trustworthy AI applications.

Duration: 10 Days

Target Audience

• Data Engineers
• AI/ML Engineers
• Data Scientists
• Analytics Engineers
• Software Engineers transitioning into AI infrastructure roles
• Cloud Architects
• Data Platform Engineers
• Technical Leads managing AI and analytics projects

Course Objectives

• Understand the unique data engineering needs of AI and analytics workflows
• Learn to design scalable, real-time and batch data pipelines
• Implement feature engineering pipelines for machine learning readiness
• Integrate distributed computing tools for large-scale data processing
• Leverage stream processing frameworks to support real-time AI use cases
• Optimize data formats and storage for model training performance
• Manage data lineage, versioning, and model metadata
• Automate training pipelines using orchestration and CI/CD tools
• Ensure compliance, data security, and fairness in data pipelines
• Enable low-latency model inference infrastructure for real-world AI systems
• Build AI-ready data architecture on cloud-native platforms

Module 1: Foundations of Data Engineering for AI

• Role of data engineers in AI lifecycle
• AI-driven vs. BI-driven pipelines
• Essential characteristics of AI training data
• Challenges in preparing data for deep learning
• Overview of tools and pipeline components

Module 2: Scalable Data Ingestion Pipelines

• Batch ingestion using Apache Spark, Pandas, and Airbyte
• Stream ingestion using Kafka and Pulsar
• Handling API and third-party sources
• File formats: CSV, Parquet, Avro for AI use cases
• Ensuring schema validation and fault tolerance

Module 3: Distributed Data Processing with Apache Spark

• Spark vs. Dask vs. Ray for ML data processing
• Spark SQL and Spark MLlib basics
• Using Spark for large-scale feature engineering
• Partitioning and caching strategies for model training
• Distributed joins, aggregations, and transformations

Module 4: Feature Engineering for ML

• Identifying predictive features
• Encoding techniques for categorical and time-series data
• Scaling, normalization, and outlier treatment
• Real-time vs batch feature computation
• Feature storage and retrieval using Feature Stores

Module 5: Real-Time Stream Processing for AI

• Concepts of stream-first architecture
• Stream enrichment and filtering for ML features
• Stateful stream processing for behavior modeling
• Low-latency pipelines using Flink and Kafka Streams
• Streaming features to real-time model inference systems

Module 6: Data Versioning and Lineage for ML Pipelines

• Version control for datasets using tools like DVC and LakeFS
• Metadata tracking and reproducibility
• Data and model lineage using OpenLineage and MLflow
• Data auditing for fairness and bias detection
• Best practices in versioned storage management

Module 7: Managing Time-Series and Sensor Data

• Data architectures for temporal ML models
• Time-series aggregation, lag features, and rolling windows
• Storing and indexing time-series using InfluxDB and TimescaleDB
• Time alignment and handling missing values
• Predictive maintenance and anomaly detection examples

Module 8: Data Quality Assurance for AI

• Validating training vs inference data consistency
• Anomaly and drift detection in model input data
• Using Great Expectations and Deequ
• Metrics to track data completeness and freshness
• Building automated quality gates in the pipeline

Module 9: Data Governance and Security for AI Applications

• Ensuring compliance with GDPR, HIPAA, etc.
• Encryption, tokenization, and secure data access
• Bias detection and explainability tooling
• Data anonymization for privacy-preserving ML
• Managing access roles and audit logs

Module 10: Cloud-Native Storage and Compute for AI

• Object storage systems: S3, GCS, ADLS for training data
• Distributed training data management
• Leveraging serverless and containerized services
• Best practices in scaling compute for large datasets
• Monitoring and cost optimization of cloud resources

Module 11: Orchestration and Automation with Airflow and Kubeflow

• Designing modular pipelines with DAGs
• Triggering workflows on data or model events
• Pipeline reproducibility and logging
• Handling retries, failures, and notifications
• Kubeflow pipelines for model training automation

Module 12: MLOps and CI/CD for AI Pipelines

• Automating training, validation, and deployment
• Integrating data workflows into DevOps pipelines
• GitOps and version-controlled infrastructure
• Continuous validation and rollout strategies
• Monitoring performance degradation post-deployment

Module 13: Model Serving and Inference Infrastructure

• Batch vs real-time model inference
• Serving models using TensorFlow Serving, TorchServe, or BentoML
• Deploying models on Kubernetes and serverless platforms
• Latency, throughput, and autoscaling considerations
• A/B testing and shadow deployments

Module 14: Analytics Integration and Business Intelligence

• Building dashboards on ML predictions
• Feeding predictions into BI tools like Looker or Tableau
• Integrating reverse ETL for operational analytics
• Combining AI outputs with traditional KPIs
• Enabling real-time analytics on top of model outputs

Module 15: Case Study: Building an End-to-End AI Data Pipeline

• Designing data architecture for a predictive use case
• Ingesting, cleaning, transforming data
• Creating features, training models, deploying them
• Monitoring performance and retraining
• Documentation, handover, and scaling strategy

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.