Working with Data: Engineering, Integration, and MLOps for AI

Master advanced data engineering for AI systems through this comprehensive path. Learn data integration, API development, versioning, and quality monitoring. Then explore knowledge graphs, GraphRAG architectures, and semantic search techniques. Complete your journey with MLOps implementation for orchestrating data pipelines across the AI lifecycle, gaining essential skills to build reliable, production-grade AI applications.

Design data integration strategies for AI applications.
Build advanced retrieval systems that enhance AI.
Orchestrate end-to-end MLOps and data pipeline solutions.

Syllabus

Courses under this program:
Course 1: Data Integration and API Development for AI Applications
-Discover the essential skills and core concepts of data integration and API development for AI applications.

Course 2: Data Versioning, Lineage, and Quality Monitoring for AI
-Explore the principles and best practices of data versioning, lineage tracking, and quality monitoring for AI.

Course 3: Knowledge Graph Data Engineering for Generative AI Use Cases
-Discover a practical framework for implementing neurosymbolic AI solutions, in this course designed for data scientists and engineers.

Course 4: Semantic Search and Information Retrieval using GenAI
-Upskill as a data professional. Learn how to design, implement, and evaluate search solutions that combine knowledge graphs, AI, and traditional search technologies.

Course 5: MLOps and Data Pipeline Orchestration for AI Systems
-Discover the essential skills and core concepts of MLOps and data pipeline orchestration for AI systems.