Build production-ready generative AI systems using LLMs, RAG, agents, and full-stack engineering practices
β±οΈ Length: 6.3 total hours
β 4.40/5 rating
π₯ 3,048 students
π January 2026 update
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- Course Overview
- This curriculum represents the definitive transition from experimental AI scripting to industrial-grade AI software engineering tailored for the 2026 technological landscape.
- Students will explore the convergence of traditional DevOps and modern LLM orchestration, focusing on the infrastructure required to support high-availability intelligence.
- The course moves beyond the “black box” approach, encouraging a deep dive into model quantization and local deployment strategies to maintain data sovereignty and reduce third-party dependencies.
- Participants will examine the lifecycle of an AI product, from the initial proof-of-concept (PoC) stage to a hardened, scalable system capable of handling thousands of concurrent requests.
- Special emphasis is placed on the economics of artificial intelligence, teaching engineers how to calculate the unit cost of an inference and optimize the “token budget” without sacrificing logic.
- The syllabus covers the critical shift from stateless interactions to long-term synthetic memory, allowing for applications that evolve alongside the user’s requirements.
- By focusing on hybrid-cloud architectures, the course prepares engineers to deploy models across diverse environments, including edge computing and private data centers.
- Requirements / Prerequisites
- Advanced Python Proficiency: Candidates should be comfortable with asynchronous programming (async/await), decorators, and type hinting to manage complex data flows.
- Web Development Fundamentals: A working knowledge of RESTful API design principles and basic modern frontend frameworks (like React or Next.js) is essential for the full-stack components.
- Environment Management: Familiarity with Docker containerization and virtual environment tools (such as Poetry or Conda) to ensure reproducible builds across different machines.
- Data Handling Skills: Basic understanding of NoSQL and SQL databases, specifically how to structure unstructured data for efficient retrieval and indexing.
- Cloud Literacy: Preliminary experience with AWS, Azure, or GCP services is recommended, as the course involves deploying resources to the cloud.
- Mathematics for AI: While not a math-heavy course, a conceptual grasp of vector spaces and similarity metrics will help in understanding how information is stored and retrieved.
- Skills Covered / Tools Used
- Orchestration Frameworks: Mastery of LangGraph and Haystack for building complex, cyclical workflows that go beyond simple linear chains.
- Vector Databases: Hands-on implementation using Pinecone, Weaviate, and Qdrant to manage high-dimensional data at scale.
- Inference Engines: Utilization of vLLM and Groq for ultra-fast model serving and understanding the trade-offs between different hardware accelerators.
- Validation & Logic: Integration of Pydantic and Instructor to enforce strict schema validation on non-deterministic LLM outputs.
- Security Protocols: Implementation of OWASP Top 10 for LLMs, focusing on prompt injection mitigation and sensitive data masking.
- Observability Stacks: Deployment of Arize Phoenix and Weights & Biases to track trace data, identify bottlenecks, and visualize embedding clusters.
- Container Orchestration: Using Kubernetes or Modal to auto-scale AI workloads based on real-time traffic demands and GPU availability.
- Benefits / Outcomes
- Architectural Fluency: Gain the ability to draft technical design documents for AI systems that satisfy both engineering rigor and business requirements.
- Portfolio of Production Assets: Graduation from the course leaves you with a production-ready repository featuring CI/CD pipelines for AI applications.
- Strategic Decision Making: Develop the expertise to choose between proprietary models (GPT-4o/Claude 3.5) and open-source alternatives (Llama 3/Mistral) based on specific use cases.
- Reduced Technical Debt: Learn to write modular, testable AI code that prevents the common pitfalls of “spaghetti prompting” found in early-stage AI projects.
- Advanced Debugging Techniques: Acquire specialized skills in tracing non-deterministic bugs and implementing fallback mechanisms when a primary model fails.
- Career Positioning: Transition into high-demand roles such as AI Solutions Architect or Lead AI Engineer, commanding higher premiums in the 2026 job market.
- Efficiency Mastery: Drastically reduce development time by using automated evaluation loops rather than manual trial-and-error testing.
- PROS
- Cutting-Edge Relevance: The content is specifically updated for the 2026 tech stack, ensuring you aren’t learning obsolete methods or deprecated libraries.
- Holistic Engineering Approach: It treats AI as a software engineering discipline rather than just a data science experiment, which is what the industry currently demands.
- Practical Resource Optimization: Provides actionable strategies for lowering operational costs, making it highly valuable for startups and enterprise teams alike.
- Rich Ecosystem Integration: Demonstrates how to connect disparate tools into a cohesive ecosystem, rather than teaching them in isolation.
- CONS
- High Cognitive Load: The rapid pace and the requirement to master both backend engineering and machine learning concepts simultaneously may prove challenging for beginners without a strong technical foundation.
Learning Tracks: English,Development,Data Science
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