Are GPU credits provided for the lab exercises?

AI & Data EngineeringAdvanced Level•54 Hours•4.94 Rating•3,120 Students Enrolled

Applied AI, LLM Pipelines & Machine Learning Systems

Build production Retrieval-Augmented Generation (RAG) pipelines, fine-tune transformer models, evaluate embeddings, and deploy scalable inference servers.

20 In-Browser CodeLab Exercises8 Cloud Virtual LabsVerifiable Completion Credential

Course Overview

Learn how to take machine learning and generative AI models from toy prototypes to reliable production systems. Master vector database indexing, token economics, semantic chunking, prompt evaluations, and low-latency inference serving.

What You Will Learn

Architect hybrid RAG pipelines with semantic re-ranking and vector databases

Optimize transformer inference with quantization (GGUF/AWQ) and vLLM serving

Implement automated model evaluation benchmarks with synthetic test cases

Build guardrails, output validators, and latency budgets for enterprise LLM apps

Curriculum & Modules

3 Modules • 9 Total Lessons

1.1 Vector Geometry & Embedding Spaces

video30 mins

1.2 Implementing HNSW Indexing in Python

assignment50 mins

1.3 Vector DB Benchmark Lab (Qdrant & Chroma)

lab45 mins

Practical Learning & Cloud Sandboxes

Run GPU-accelerated Jupyter environments and vector stores directly inside your browser.

In-Browser CodeLab

20 automated exercises evaluated against real compilers and standard test matrices in seconds.

Cloud Virtual Labs

8 complete Linux containers launching isolated Docker runtimes, networking, and memory profiling.

Capstone Projects & Deliverables

Project Deliverable 1

Enterprise Technical Documentation Copilot

Develop an end-to-end question answering system with hybrid vector search, source citation grounding, and automated evaluation metrics.

Required Deliverables:

FastAPI backend service
Evaluation dataset with RAGAS metrics
Dockerized deployment setup

Assessments & Grading

Automated test suites grading retrieval accuracy, token efficiency, and response grounding.

6 Knowledge Check Quizzes12 Automated Unit Test Suites

Prerequisites & Requirements

Proficiency in Python
Familiarity with foundational linear algebra and REST APIs

Hardware requirements: Only a modern web browser with Internet access. All code execution runs in the cloud.

Instructor Profile

DTM

Dr. Tariq Mansoor

Head of Applied AI Research • VEducate Intelligence Labs

Published author in transformer architectures with extensive industry experience deploying production LLM solutions across Fortune 500 enterprises.

Course FAQs

Yes. Cloud GPU compute is pre-provisioned inside the browser virtual lab environment at no extra charge.

Enrollment Options

$219

Includes verified certificate & cloud compute

This course includes:

54 Hours on-demand video & reading
20 in-browser CodeLab exercises
8 full Linux cloud virtual labs
12 automated unit test suites
Cryptographic completion diploma

For Colleges & Universities

Available under Campus & Department Licensure

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