Build production-ready LLM apps using LangChain, RAG, agents, multimodal AI, deployment, and real-world systems
β±οΈ Length: 17.6 total hours
π₯ 384 students
π February 2026 update
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- Course Overview
- Dive deep into the practical realities of deploying Large Language Models (LLMs) beyond simple experimentation.
- This course bridges the gap between theoretical LLM capabilities and the robust infrastructure required for enterprise-grade applications.
- Explore the complete lifecycle of building and maintaining LLM-powered systems, from initial design to ongoing optimization and scalability.
- Gain a comprehensive understanding of the architectural patterns and best practices that underpin reliable and efficient AI solutions.
- Demystify the complexities of integrating LLMs into existing software stacks and workflows.
- Learn how to leverage advanced LLM techniques to solve complex business problems and create innovative user experiences.
- Understand the critical considerations for security, performance, and cost-effectiveness in production LLM deployments.
- This is not just about prompting; it’s about engineering.
- Core Competencies Developed
- System Design for LLMs: Architecting scalable, fault-tolerant LLM-powered applications.
- Integration Strategies: Seamlessly embedding LLMs into diverse technology landscapes.
- Performance Optimization: Techniques for maximizing LLM inference speed and resource utilization.
- Reliability Engineering: Building resilient systems that handle errors and edge cases gracefully.
- Observability and Monitoring: Implementing effective strategies for tracking LLM behavior and system health in production.
- Deployment Pipelines: Automating the release and management of LLM applications.
- Cost Management: Strategies for controlling LLM operational expenses.
- Ethical AI Deployment: Considerations for responsible and fair LLM implementation.
- Key Learning Modules & Concepts
- Advanced LangChain Patterns: Moving beyond basic chains to build sophisticated workflows and orchestration logic.
- Retrieval-Augmented Generation (RAG) Mastery: Designing and implementing highly effective RAG pipelines for domain-specific knowledge.
- Intelligent Agents: Creating autonomous agents capable of planning, executing tasks, and interacting with tools.
- Multimodal AI Integration: Incorporating visual, auditory, and other data types alongside text for richer applications.
- Production Deployment Patterns: Exploring various deployment strategies, including containerization, serverless, and managed services.
- Real-World System Architectures: Case studies and blueprints for successful LLM deployments in various industries.
- API Design & Management: Building robust APIs for LLM services.
- Data Management for LLMs: Effective strategies for handling training, fine-tuning, and inference data.
- Evaluation & Testing Frameworks: Developing comprehensive testing suites for LLM-driven applications.
- Security Best Practices for LLMs: Mitigating risks associated with LLM vulnerabilities.
- Tools and Technologies You’ll Master
- LangChain: The definitive framework for LLM application development.
- Vector Databases: Essential for efficient RAG implementations (e.g., Chroma, Pinecone, Weaviate).
- LLM Orchestration Tools: Advanced features and custom solutions.
- Cloud Deployment Platforms: AWS, Azure, GCP for scalable infrastructure.
- Containerization: Docker for consistent and reproducible environments.
- Orchestration Tools: Kubernetes for managing containerized applications.
- Monitoring & Logging Tools: Prometheus, Grafana, ELK Stack for system health.
- API Gateway Services: For secure and efficient API management.
- MLOps Principles & Tools: Applying best practices for the machine learning lifecycle.
- Target Audience & Benefits
- For Developers & Engineers: Equip yourself with the skills to build production-grade AI features into your applications.
- For AI/ML Practitioners: Transition from experimentation to deployment with confidence and practical know-how.
- For Technical Leads & Architects: Design and implement scalable, reliable LLM solutions for your organization.
- For Product Managers: Understand the technical feasibility and implementation challenges of LLM-powered products.
- Outcome: Become a sought-after professional capable of delivering impactful AI solutions in the real world.
- Outcome: Enhance your career prospects in the rapidly growing field of AI engineering.
- Outcome: Gain the ability to tackle complex business challenges with cutting-edge LLM technology.
- Requirements / Prerequisites
- Foundational Python Programming: Strong proficiency in Python is essential.
- Basic Understanding of Machine Learning Concepts: Familiarity with core ML principles.
- Familiarity with APIs and Web Services: Understanding of how systems communicate.
- Comfort with Command-Line Interfaces: Ability to navigate and interact with the terminal.
- A Laptop with Sufficient Resources: Capable of running development environments and potentially local LLM models.
- No Prior LLM Experience Required (but a plus): The course is designed to build upon fundamental knowledge.
- PROS
- Highly practical focus: Emphasizes hands-on application of LLM technologies.
- Comprehensive coverage: Addresses the full lifecycle from development to deployment.
- Expert-led curriculum: Likely to be taught by industry practitioners.
- Future-proof skills: Equips learners with in-demand LLM engineering expertise.
- CONS
- Technical depth may require significant effort: Mastering production-ready systems demands dedicated study and practice.
Learning Tracks: English,Development,Data Science
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