RAG Systems and Production Operations

This course is part of Vector Databases for Machine Learning: A Comprehensive Guide Specialization

Instructor: Professionals from the Industry

Access provided by Interbank

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Build and evaluate advanced RAG systems with Self-RAG and Corrective RAG patterns
Implement secure, scalable vector database deployments with TLS and authentication
Design production-ready APIs with monitoring, rate limiting, and performance optimization
Execute cross-platform vector database migrations with data integrity checks

Details to know

Shareable certificate

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Assessments

15 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

This course is part of the Vector Databases for Machine Learning: A Comprehensive Guide Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

This advanced course transforms you into an enterprise-level ML engineer capable of designing, implementing, and operating sophisticated retrieval-augmented generation (RAG) systems. You'll progress from foundational RAG architecture to cutting-edge patterns like Self-RAG and Corrective RAG, then dive deep into production operations including secure deployment, performance optimization, and cross-platform migration.

By combining hands-on projects with real-world enterprise requirements, you'll learn to build AI systems that deliver accurate, grounded responses at scale. Each module builds practical skills used by senior ML engineers in high-stakes domains like legal tech, healthcare, and finance. Who this is for: Experienced software engineers and data scientists ready to build production-grade AI applications. Strong Python programming and basic machine learning knowledge required.

This foundational module demystifies Retrieval-Augmented Generation. You will learn why RAG is essential for creating reliable AI systems and explore the role and function of each component in its architecture. You will finish by sketching a RAG data flow diagram to solidify your theoretical understanding.

What's included

3 videos2 readings4 assignments

3 videosTotal 14 minutes

How-To: Diagram the RAG Data Flow5 minutes
Why Code Matters: From Diagram to Reality4 minutes
How-To: Build a Vector Store with Python6 minutes

2 readingsTotal 15 minutes

The Components of a RAG System: Retriever and Generator7 minutes
Choosing Your Tools: Vector Stores and LLMs8 minutes

4 assignmentsTotal 70 minutes

Build and Submit a RAG Pipeline Report30 minutes
Hands-On Learning: Sketch a RAG Architecture Diagram15 minutes
Knowledge Check: RAG Components10 minutes
Hands-On Learning: Practice Run: Retrieve Context15 minutes

Go beyond basic RAG to build robust, self-correcting AI systems. This 2-hour course teaches intermediate developers to implement Corrective, Self, and Agentic RAG patterns. Through hands-on A/B testing and performance analysis, you’ll learn to architect, evaluate, and defend trustworthy, production-ready pipelines that solve complex, multi-hop queries with precision.

What's included

3 videos2 readings4 assignments

3 videosTotal 22 minutes

How to Implement a Self-RAG and a Corrective RAG Loop9 minutes
Why Your RAG Bot Needs to Think, Not Just Retrieve6 minutes
How-To: Build an Agent and Its Evaluation Harness8 minutes

2 readingsTotal 10 minutes

The Theory of Self-Correction: Corrective vs. Self-RAG5 minutes
The Cost of Intelligence: Choosing an Embedding Service5 minutes

4 assignmentsTotal 70 minutes

A/B Test RAG Patterns for Production30 minutes
Hands-On Learning: Add a Validation Step to a RAG Pipeline15 minutes
Knowledge Check: Matching Patterns to Problems10 minutes
Hands-On Learning: Build and Test a Basic RAG Agent15 minutes

Move AI from local to production with this hands-on course. Master essential "last-mile" skills: containerize databases with Docker, implement TLS and RBAC security, and monitor health via Grafana. Learn to analyze performance for autoscaling, ensuring your enterprise-grade vector database deployments are secure, scalable, and production-ready.

What's included

3 videos2 readings2 assignments1 ungraded lab

3 videosTotal 18 minutes

How-To: Secure Weaviate in Docker7 minutes
Why We Monitor: Surviving a Traffic Spike4 minutes
How-To: Build a Grafana Dashboard from Scratch6 minutes

2 readingsTotal 13 minutes

The Production Security Model: TLS and RBAC7 minutes
Key Metrics for Vector Database Health6 minutes

2 assignmentsTotal 42 minutes

Deploy, Monitor, and Propose a Scaling Plan30 minutes
Knowledge Check: Core Security Functions12 minutes

1 ungraded labTotal 60 minutes

Hands-On Learning: Add Authentication and TLS to a Dockerized DB60 minutes

Optimize and Migrate Vectors is a 90‑minute, hands‑on intermediate course for ML engineers to master vector‑database operations. Learn performance tuning to cut latency up to 40 % and script zero‑loss migrations of 100k+ vectors from Chroma to Weaviate using Python and Docker.

What's included

3 videos2 readings4 assignments

3 videosTotal 17 minutes

How To Tune Parameters and Measure Impact6 minutes
When Your Database Holds You Back5 minutes
How-To: Script a Cross-Platform Migration6 minutes

2 readingsTotal 7 minutes

The Speed vs. Accuracy Trade-Off4 minutes
The Anatomy of a Migration Plan3 minutes

4 assignmentsTotal 53 minutes

Migrate and Verify 100k Vectors30 minutes
Hands-On Learning: Configure an Index for Speed8 minutes
Knowledge Check: Optimization Scenarios5 minutes
Hands-On Learning: Draft a High-Level Migration Plan10 minutes

In this project, you'll build a production-grade RAG system that synthesizes everything learned throughout the program: vector database deployment, advanced RAG patterns, security, monitoring, and performance optimization. This comprehensive project simulates enterprise requirements and produces strong portfolio evidence of end-to-end ML engineering capability.