Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

This course provides a deep dive into deploying, evaluating, and scaling production-ready AI systems, focusing on LangChain and LangGraph. You’ll gain hands-on experience in implementing, benchmarking, and mitigating risks to ensure robust AI solutions in enterprise environments. Learners will develop practical skills in evaluating AI systems, covering observability, cost management, and performance optimization. The course emphasizes production readiness and equips you to scale AI applications for real-world tasks while addressing emerging AI trends. What makes this course unique is its focus on enterprise-level deployment, with expert guidance on maintaining AI systems and preparing them for future advancements in generative AI. Ideal for AI developers and engineers, this course requires experience with AI systems and a foundational knowledge of software development. Learners will benefit from practical, real-world scenarios. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Syllabus

Software Development and Data Analysis Agents

This module introduces the use of natural language interfaces for software development and data analysis with large language models (LLMs). Learners will explore practical implementation strategies, benchmarking, security considerations, and agentic approaches for automating coding tasks. Hands-on examples demonstrate how to leverage tools like LangChain and Hugging Face to build and evaluate intelligent code agents.

Evaluation and Testing

This module explores the essential strategies and tools for evaluating and testing large language model (LLM) agents in real-world applications. Learners will discover how to assess agent capabilities, system integration, and output quality using both offline and benchmark-based methods. Practical examples illustrate how to measure correctness, tone, and the overall value delivered to users and stakeholders.

Production-Ready LLM Deployment and Observability

This module guides learners through the essential steps for deploying large language model (LLM) applications into production, emphasizing best practices for scalability, reliability, and observability. Learners will explore deployment frameworks such as Ray Serve, Docker, and Kubernetes, as well as strategies for monitoring, bias detection, and cost management. The module also introduces emerging standards like the Model Context Protocol and specialized platforms for managing LLM workflows.

The Future of Generative Models: Beyond Scaling

This module explores the evolving landscape of generative AI, examining the limitations of scaling models and the emergence of alternative approaches. Learners will investigate the impact of generative AI on industry, workforce dynamics, and broader societal implications. By the end, participants will gain insights into the future directions and challenges of AI development.