Enterprise Data Infrastructure for Generative AI Training Course

 Enterprise Data Infrastructure for Generative AI Training Course 

Introduction 

Enterprise Data Infrastructure for Generative AI Training Course equips professionals with the technical knowledge and practical skills required to design, develop, deploy, and manage scalable data infrastructure for enterprise-grade Generative Artificial Intelligence (GenAI) applications. The course explores modern data architecture, cloud data platforms, distributed computing, data engineering, data lakes, lakehouse architecture, vector databases, Retrieval-Augmented Generation (RAG), knowledge graphs, and enterprise data integration. Participants gain practical insights into building secure, reliable, and high-performance data ecosystems that support Large Language Models (LLMs), intelligent automation, AI agents, and advanced enterprise analytics. 

The training also examines enterprise data governance, data quality management, metadata management, cybersecurity, interoperability, and infrastructure optimization for Generative AI workloads. Through practical exercises and global case studies, participants explore how leading organizations leverage technologies such as Microsoft Azure, Amazon Web Services (AWS), Google Cloud, Databricks, Snowflake, Apache Spark, Kubernetes, and vector search platforms to accelerate AI adoption. The course emphasizes scalable AI infrastructure, responsible AI implementation, cloud-native architecture, operational resilience, and cost-efficient data management to support enterprise digital transformation. 

Course Objectives 

By the end of this course, participants should be able to: 

1. Design scalable enterprise data infrastructure for Generative AI applications.
2. Understand modern cloud-native data architecture and AI ecosystems.
3. Implement data lakes, lakehouses, and enterprise data warehouses.
4. Develop efficient data pipelines for LLM training and inference.
5. Integrate vector databases and semantic search technologies.
6. Implement Retrieval-Augmented Generation (RAG) architectures.
7. Apply enterprise data governance and security frameworks.
8. Optimize distributed data processing and storage systems.
9. Manage metadata, data lineage, and data quality.
10. Integrate structured and unstructured enterprise data.
11. Deploy scalable infrastructure for AI agents and LLM applications.
12. Apply MLOps, DataOps, and infrastructure automation practices.
13. Optimize enterprise AI infrastructure performance and operational costs.

Organizational Benefits
 

1. Accelerates enterprise Generative AI adoption and digital transformation.
2. Improves enterprise data accessibility and interoperability.
3. Strengthens data governance, privacy, and regulatory compliance.
4. Reduces infrastructure costs through cloud optimization.
5. Enhances AI application performance and scalability.
6. Improves data quality and reliability for AI workloads.
7. Strengthens enterprise cybersecurity and risk management.
8. Supports intelligent automation and business process optimization.
9. Improves collaboration between data engineering and AI teams.
10. Builds sustainable infrastructure for enterprise AI innovation.

Target Audiences
 

1. Chief Information Officers and Chief Technology Officers.
2. Enterprise Data Architects and Solution Architects.
3. Data Engineers and Big Data Specialists.
4. Artificial Intelligence and Machine Learning Engineers.
5. Cloud Infrastructure and DevOps Engineers.
6. IT Infrastructure and Systems Administrators.
7. Data Governance and Enterprise Information Managers.
8. Digital Transformation and Technology Consultants.

Course Duration: 5 days

Course Modules

Module 1: Enterprise Data Infrastructure Fundamentals
 

• Enterprise Generative AI infrastructure architecture.
• Modern data ecosystems and cloud-native computing.
• Enterprise data integration and interoperability.
• AI infrastructure requirements and scalability.
• Infrastructure readiness assessment and planning.
• Case study: Microsoft's enterprise cloud and AI infrastructure ecosystem.

Module 2: Cloud Data Platforms and Architecture
 

• AWS, Microsoft Azure, and Google Cloud data services.
• Cloud data warehouses and lakehouse architecture.
• Hybrid cloud and multi-cloud infrastructure.
• Cloud storage, compute, and networking.
• Infrastructure scalability and availability.
• Case study: Netflix's cloud-based data infrastructure.

Module 3: Data Engineering for Generative AI
 

• Enterprise data ingestion and integration pipelines.
• Apache Spark and distributed data processing.
• Batch processing and real-time streaming.
• Data transformation and preparation for LLMs.
• Apache Kafka and event-driven architectures.
• Case study: Uber's large-scale data engineering ecosystem.

Module 4: Vector Databases and Retrieval-Augmented Generation
 

• Vector database architecture and embeddings.
• Semantic search and similarity retrieval.
• RAG infrastructure design and implementation.
• Integration of vector databases with LLMs.
• Indexing, retrieval, and context optimization.
• Case study: Enterprise knowledge retrieval using Pinecone and LangChain.

Module 5: Enterprise Data Governance and Security
 

• Enterprise data governance frameworks.
• Data privacy, encryption, and access controls.
• Metadata management and data lineage.
• Data quality monitoring and validation.
• Responsible AI and regulatory compliance.
• Case study: Microsoft's enterprise data governance practices.

Module 6: Infrastructure for LLMs and AI Applications
 

• Large Language Model infrastructure requirements.
• GPU computing and accelerated AI workloads.
• Model hosting, deployment, and inference infrastructure.
• Kubernetes and containerized AI applications.
• AI model scalability and performance optimization.
• Case study: NVIDIA's accelerated computing infrastructure for Generative AI.

Module 7: DataOps, MLOps and Infrastructure Automation
 

• DataOps and MLOps implementation frameworks.
• CI/CD pipelines for enterprise AI systems.
• Infrastructure as Code and automated provisioning.
• Monitoring, observability, and system reliability.
• AI infrastructure lifecycle management.
• Case study: Google's automated cloud infrastructure and AI operations.

Module 8: Enterprise AI Infrastructure Optimization
 

• Enterprise AI infrastructure performance assessment.
• Storage, compute, and network optimization.
• Disaster recovery and business continuity.
• Infrastructure cost management and FinOps.
• Enterprise AI infrastructure implementation roadmaps.
• Case study: Amazon's scalable infrastructure supporting AI-driven services.

Training Methodology
 

• Expert-led presentations and interactive discussions.
• Practical enterprise data infrastructure demonstrations.
• Hands-on cloud architecture and data engineering exercises.
• Group assignments and enterprise infrastructure design projects.
• Global industry case studies and best-practice analysis.
• Knowledge assessments, technical exercises, and participant presentations.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.