Designing and Operationalizing the Full Stack of Generative AI
β±οΈ Length: 4.7 total hours
β 4.61/5 rating
π₯ 69 students
π February 2026 update
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- Course Overview
- Comprehensive examination of the Generative AI lifecycle, moving beyond simple API calls to building robust, production-grade systems.
- In-depth exploration of the Full Stack GenAI architecture, encompassing data engineering, model selection, fine-tuning, and front-end integration.
- Detailed walkthrough of Agentic Workflows, teaching students how to design autonomous systems that can reason, use tools, and execute complex tasks.
- Focus on the transition from Proof of Concept (PoC) to enterprise-scale deployment, addressing scalability, latency, and reliability.
- Strategic analysis of Model Orchestration, including the management of multi-model environments and fallback mechanisms.
- Instruction on Data Flywheels, demonstrating how to capture user feedback to continuously improve model performance in a live environment.
- Evaluation of Infrastructure as Code (IaC) specifically tailored for hosting large-scale language models and diffusion frameworks.
- Exploration of Hybrid Cloud Strategies for GenAI, balancing local edge computing with heavy-duty cloud-based inference.
- Requirements / Prerequisites
- Proficiency in Python Programming, particularly with asynchronous programming and object-oriented design patterns.
- Foundational understanding of Machine Learning (ML) concepts, including supervised learning, loss functions, and gradient descent.
- Familiarity with Cloud Infrastructure services like AWS, Google Cloud, or Azure, specifically regarding compute instances and storage buckets.
- Basic knowledge of Containerization concepts, such as how to build and run basic images for application deployment.
- Experience with API Development using frameworks like FastAPI or Flask to handle request-response cycles.
- Understanding of Data Structures and databases, particularly the difference between relational and non-relational storage systems.
- A working environment with GPU access (local or cloud-based) is recommended for following along with fine-tuning exercises.
- Skills Covered / Tools Used
- Mastery of PyTorch and Hugging Face Transformers for manipulating pre-trained models and adjusting architectural parameters.
- Advanced implementation of Retrieval-Augmented Generation (RAG) using high-performance Vector Databases like Pinecone, Milvus, or Weaviate.
- Utilization of LangChain and LlamaIndex for complex data ingestion pipelines and sophisticated prompt chaining logic.
- Hands-on experience with PEFT (Parameter-Efficient Fine-Tuning) and QLoRA techniques to adapt models with minimal hardware requirements.
- Deployment and scaling using Docker and Kubernetes, optimized for high-throughput inference and low-latency response times.
- Configuration of MLOps Pipelines using tools like Weights & Biases or MLflow for tracking experiments and model versioning.
- Integration of Semantic Search capabilities to enhance the contextual relevance of generative outputs.
- Application of Quantization Techniques (GGUF, AWQ) to compress models for efficient deployment on resource-constrained hardware.
- Implementation of Safety Rails using NeMo Guardrails or similar frameworks to prevent hallucinations and ensure ethical output.
- Benefits / Outcomes
- Ability to architect a Complete GenAI Product from the initial data gathering phase to a user-facing production application.
- Acquisition of Advanced Prompt Engineering skills that involve programmatic optimization and automated evaluation loops.
- Competency in Cost Management for GenAI, learning how to optimize token usage and choose the most cost-effective model for specific tasks.
- Expertise in Model Evaluation, utilizing benchmarks and custom metrics to quantitatively measure the success of a generative system.
- Preparedness for Senior AI Engineering roles by understanding the nuances of model serving, caching, and rate limiting.
- Knowledge of Privacy-First AI, including how to handle sensitive data and implement local LLMs for data sovereignty.
- Capacity to build Multimodal Applications that process and generate text, images, and audio within a unified framework.
- Strategic insight into Future-Proofing AI stacks, ensuring that your architecture can adapt as new models and techniques emerge.
- PROS
- Offers a Holistic Perspective that bridges the gap between theoretical data science and practical software engineering.
- Highly Current Curriculum reflecting the latest 2026 updates in agentic reasoning and efficient model adaptation.
- Focuses on Production-Ready Code rather than just “toy” examples, making the skills immediately applicable to professional projects.
- Includes Real-World Case Studies that illustrate how major tech firms are operationalizing generative models at scale.
- Provides a Deep Dive into the infrastructure layer, which is often overlooked in most high-level generative AI tutorials.
- CONS
- The Steep Learning Curve may be challenging for beginners who do not have a strong background in both software engineering and machine learning.
Learning Tracks: English,IT & Software,Other IT & Software
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