Course Introduction
Data engineering has become a foundational capability for organizations developing machine learning, generative AI, intelligent automation, and advanced analytical systems. Data Engineering for Machine Learning and AI Systems Training Course provides an advanced framework for designing data platforms and pipelines that deliver reliable, scalable, governed, high-quality, and machine-ready data throughout the complete AI development and operational lifecycle.
This course explores the specialized data engineering requirements created by machine learning and artificial intelligence workloads. Participants examine data ingestion, transformation, feature engineering, labeling, dataset preparation, vectorization, metadata management, data versioning, streaming, retrieval pipelines, and analytical serving architectures. Emphasis is placed on building data foundations that support experimentation while remaining robust enough for production-scale AI applications.
The programme addresses the integration of modern data platforms with machine learning and AI engineering environments. Participants explore lakehouses, warehouses, data lakes, feature stores, vector databases, knowledge graphs, streaming platforms, semantic layers, and model-serving ecosystems. They learn how to create interoperable data pipelines that provide consistent training datasets, real-time features, contextual information, and continuously refreshed data to intelligent applications.
A significant focus is placed on data quality, lineage, observability, governance, and reproducibility for AI systems. Participants examine how data defects, changing schemas, distribution shifts, incomplete lineage, and inconsistent transformations can affect downstream models and intelligent applications. The course introduces engineering approaches for dataset validation, quality gates, provenance, version control, monitoring, drift detection, and automated remediation across AI-oriented data pipelines.
Security, privacy, responsible data usage, and regulatory considerations are integrated into the engineering lifecycle. Participants learn how to protect sensitive training and inference data through access controls, encryption, classification, masking, retention policies, privacy-preserving techniques, and policy automation. The programme also addresses emerging concerns around synthetic data, multimodal datasets, foundation models, retrieval-augmented generation, agentic systems, and the governance of data used by increasingly autonomous AI applications.
By the end of the programme, participants will be equipped to design enterprise data engineering architectures specifically aligned with machine learning and AI requirements. They will be able to develop scalable data pipelines, engineer reliable training and inference datasets, integrate real-time and semantic data services, establish strong governance and observability, and create sustainable data foundations capable of supporting evolving AI workloads across cloud, hybrid, lakehouse, and distributed enterprise environments.
Duration
10 days
Who Should Attend
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Data engineers responsible for building production-grade data pipelines for machine learning and AI workloads.
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Machine learning engineers working with training datasets, features, inference data, and model development pipelines.
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AI engineers developing generative AI, retrieval-augmented generation, and intelligent application architectures.
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Data architects designing enterprise platforms that support analytics, machine learning, and artificial intelligence.
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Cloud architects implementing scalable data and AI infrastructure across public, hybrid, and multi-cloud environments.
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MLOps and DataOps professionals responsible for operationalizing machine learning and data engineering workflows.
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Analytics engineers preparing trusted analytical datasets and reusable data assets for AI applications.
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Data scientists seeking deeper expertise in production data preparation, feature engineering, and data pipeline architecture.
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Data platform engineers developing lakehouses, warehouses, streaming platforms, feature stores, and AI data services.
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Data governance professionals responsible for managing data quality, lineage, privacy, security, and AI-related controls.
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Data quality and observability specialists monitoring the reliability and fitness of datasets used by AI systems.
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Technology managers leading enterprise machine learning, artificial intelligence, and data modernization initiatives.
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Enterprise architects developing target-state data architectures for AI-first organizational transformation.
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Security, privacy, and compliance professionals overseeing sensitive data used in machine learning and AI environments.
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Consultants and technical leaders designing enterprise data engineering strategies for intelligent applications.
Course Objectives
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Explain advanced data engineering principles and architectures required to support machine learning, generative AI, intelligent applications, and analytical workloads.
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Design scalable data pipelines that deliver reliable training, validation, inference, feature, retrieval, and contextual datasets to AI systems.
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Engineer robust ingestion and transformation workflows for structured, semi-structured, unstructured, streaming, and multimodal AI data.
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Apply feature engineering, feature management, dataset versioning, labeling, enrichment, and transformation practices for machine learning workloads.
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Design lakehouse, warehouse, data lake, feature store, vector database, and knowledge graph architectures for integrated AI data ecosystems.
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Establish automated data quality, testing, validation, reconciliation, and observability mechanisms for machine learning and AI data pipelines.
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Implement metadata, lineage, provenance, and reproducibility capabilities that allow AI teams to understand the origin and transformation of datasets.
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Design real-time data engineering architectures that provide continuously updated features, events, context, and operational information to AI systems.
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Integrate data engineering pipelines with MLOps and AI application workflows to support repeatable training, evaluation, deployment, and inference operations.
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Apply security, privacy, governance, compliance, access control, and responsible data management practices throughout AI data engineering lifecycles.
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Evaluate emerging data engineering approaches for generative AI, retrieval-augmented generation, multimodal AI, synthetic data, and agentic applications.
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Develop enterprise implementation roadmaps for building scalable, governed, observable, and continuously optimized data foundations for AI adoption.
Comprehensive Course Outline
Module 1: Foundations of Data Engineering for Machine Learning and AI
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Evolution of enterprise data engineering from analytical workloads toward machine learning and AI-centric data ecosystems.
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Distinct data engineering requirements created by model training, inference, retrieval, evaluation, and intelligent application workloads.
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Architectural relationships among data platforms, machine learning pipelines, AI applications, models, features, and operational systems.
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Principles for building scalable, reproducible, governed, observable, and continuously evolving AI-ready data foundations.
Module 2: AI Data Platform Architecture
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Designing enterprise reference architectures connecting data lakes, lakehouses, warehouses, feature platforms, vector stores, and AI services.
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Establishing architectural layers for ingestion, transformation, storage, metadata, quality, serving, governance, and AI consumption.
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Designing modular AI data platforms that support multiple machine learning frameworks, AI applications, analytical workloads, and model types.
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Evaluating architecture trade-offs involving scalability, interoperability, performance, reliability, governance, portability, and operational complexity.
Module 3: Data Ingestion and Integration for AI Workloads
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Designing ingestion pipelines for databases, APIs, applications, documents, files, events, sensors, SaaS systems, and external information sources.
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Applying batch, incremental, change data capture, streaming, event-driven, and multimodal ingestion patterns for AI data environments.
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Engineering ingestion processes with schema validation, metadata capture, quality checks, error handling, retries, and automated recovery.
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Managing heterogeneous source systems while preserving provenance, context, business meaning, security controls, and downstream AI usability.
Module 4: Data Preparation and Transformation for Machine Learning
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Engineering scalable transformation pipelines for cleaning, normalization, enrichment, aggregation, encoding, and preparation of machine learning datasets.
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Applying reusable transformation frameworks for training, validation, testing, inference, and evaluation datasets across machine learning workflows.
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Managing feature transformations, categorical encoding, numerical normalization, text preparation, temporal processing, and data enrichment.
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Preventing training-serving inconsistencies through standardized transformations, shared logic, validation controls, and reproducible processing pipelines.
Module 5: Feature Engineering and Feature Data Platforms
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Designing feature engineering workflows that convert raw enterprise data into reliable and reusable machine learning features.
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Establishing feature stores and feature serving architectures for consistent offline training and online inference requirements.
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Managing feature definitions, ownership, metadata, versioning, freshness, dependencies, validation, and lifecycle controls.
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Addressing feature leakage, temporal correctness, point-in-time retrieval, feature drift, and consistency across machine learning environments.
Module 6: Training Data, Dataset Management and Reproducibility
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Designing enterprise training dataset architectures that support repeatable experimentation, validation, evaluation, and model development.
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Applying dataset versioning, snapshots, provenance, lineage, labeling, partitioning, sampling, and reproducible transformation techniques.
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Establishing processes for managing training, validation, testing, benchmark, evaluation, and inference datasets throughout their lifecycle.
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Engineering reproducible data pipelines that allow teams to trace model results back to specific datasets, transformations, features, and source records.
Module 7: Data Quality, Validation and AI Reliability
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Establishing quality frameworks for accuracy, completeness, consistency, validity, uniqueness, freshness, representativeness, and contextual correctness.
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Designing automated validation gates for training datasets, features, retrieval sources, inference inputs, and AI application data.
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Detecting anomalies, corrupted records, missing values, distribution changes, unexpected schema modifications, and data pipeline failures.
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Connecting data quality observability with model performance, evaluation outcomes, drift monitoring, reliability engineering, and automated remediation.
Module 8: Streaming, Real-Time Data and Online AI Features
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Designing streaming data architectures that provide continuously updated information and features to machine learning and AI applications.
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Applying event streaming, message brokers, stream processing, real-time enrichment, and event-driven data transformation patterns.
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Engineering low-latency feature pipelines for personalization, fraud detection, recommendation systems, anomaly detection, and operational intelligence.
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Managing event ordering, state, replay, delivery guarantees, checkpointing, schema evolution, fault tolerance, and real-time data observability.
Module 9: Vector Data, Semantic Retrieval and RAG Data Engineering
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Designing vector data pipelines for embeddings, semantic search, similarity retrieval, recommendation systems, and AI knowledge applications.
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Engineering document ingestion, chunking, metadata enrichment, embedding generation, indexing, refresh, and retrieval workflows for enterprise AI.
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Architecting retrieval-augmented generation pipelines that connect language models with governed, current, and contextually relevant enterprise data.
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Managing retrieval quality, source provenance, freshness, access controls, relevance evaluation, indexing performance, and lifecycle management.
Module 10: Knowledge Graphs, Metadata and AI Context Engineering
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Designing knowledge graph architectures that represent enterprise entities, relationships, concepts, dependencies, and business context for AI applications.
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Integrating technical metadata, business glossaries, lineage, ontologies, taxonomies, and semantic relationships into AI data architectures.
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Engineering contextual data services that improve machine understanding, retrieval quality, reasoning support, and intelligent application grounding.
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Applying active metadata and automated enrichment techniques to maintain continuously updated context across rapidly changing enterprise data environments.
Module 11: Data Governance, Security and Privacy for AI
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Designing access control, encryption, classification, masking, tokenization, retention, and privacy architectures for AI-oriented data platforms.
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Establishing governance processes for training datasets, features, embeddings, retrieved content, inference data, and AI application information.
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Applying policy-as-code and automated controls to enforce data usage, residency, retention, consent, privacy, and regulatory requirements.
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Maintaining auditability, provenance, lineage, accountability, and controlled access throughout the machine learning and AI data lifecycle.
Module 12: DataOps, MLOps and Automated AI Data Delivery
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Integrating DataOps and MLOps practices across data ingestion, transformation, feature engineering, dataset creation, model training, and inference workflows.
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Applying CI/CD, infrastructure-as-code, automated testing, deployment promotion, version control, and environment management to AI data pipelines.
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Designing automated workflows for dataset generation, validation, feature publication, pipeline execution, monitoring, and recovery.
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Establishing reliable operational processes that connect data engineering teams with machine learning, AI engineering, platform, and governance functions.
Module 13: Cloud-Native and Distributed AI Data Engineering
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Designing cloud-native data architectures that support elastic processing, distributed storage, managed services, and scalable AI workloads.
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Applying lakehouse and warehouse architectures to unify analytical, machine learning, generative AI, and operational data requirements.
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Engineering distributed processing pipelines capable of handling large-scale datasets, high-throughput transformations, and continuously generated information.
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Designing hybrid and multi-cloud data architectures that address interoperability, portability, data sovereignty, resilience, and enterprise integration.
Module 14: Generative AI, Multimodal Data and Emerging Data Engineering
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Engineering data pipelines for large language models, generative AI, multimodal applications, document intelligence, and enterprise knowledge systems.
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Designing data architectures for text, images, audio, video, documents, structured records, and cross-modal information processing.
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Addressing emerging issues involving synthetic data, model-generated content, data contamination, provenance, copyright-sensitive information, and dataset integrity.
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Evaluating new approaches including context engineering, AI data agents, automated metadata generation, intelligent pipelines, and autonomous data operations.
Module 15: Data Engineering for AI Agents and Intelligent Systems
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Designing data services that allow AI agents to securely discover, retrieve, interpret, transform, and act upon enterprise information.
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Engineering governed APIs, tools, event streams, semantic services, vector retrieval, and knowledge systems for agentic AI applications.
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Establishing data freshness, authorization, provenance, observability, execution controls, and traceability for increasingly autonomous AI workflows.
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Addressing emerging architectural issues involving agent memory, long-running context, dynamic retrieval, multi-agent data access, and automated data operations.
Module 16: Enterprise AI Data Engineering Strategy and Implementation
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Developing enterprise data engineering strategies aligned with machine learning, generative AI, analytics, intelligent automation, and business transformation objectives.
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Creating target-state architectures, capability models, technology standards, implementation roadmaps, migration priorities, and operating models.
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Establishing organizational responsibilities across data engineering, AI engineering, MLOps, governance, security, architecture, platform, and business teams.
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Designing continuous improvement frameworks for AI data quality, reliability, scalability, cost management, governance, technology evolution, and enterprise value.
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: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill 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: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
