Optimizing, Deploying, and Governing LLMs in the Enterprise

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 hours
Sessions

Self-Paced
Level

Advanced
Subtitles

English

Found in

Part of

LLMs in Enterprise

Overview

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Master strategies for data management, deployment, monitoring, and responsible AI in large language model operations. Stay ahead with insights into emerging trends and multimodal applications in enterprise environments. This course equips learners with advanced skills for managing the full lifecycle of LLMs in production, from crafting effective data strategies and optimizing inferencing to deploying at scale and ensuring robust monitoring. Learners will explore best practices for responsible AI, addressing ethical and regulatory considerations while exploring the latest trends in multimodal LLMs. By the end of the course, learners will be prepared to lead enterprise LLM initiatives with a focus on performance, compliance, and innovation. The course takes learners through real-world case studies, videos, and knowledge checks to gain practical expertise in deploying, optimizing, and governing LLMs. These materials foster a forward-looking perspective, enabling professionals to navigate the evolving landscape of enterprise AI. With a structured approach, you'll master everything from the data blueprint to managing the deployment and monitoring of models in production. Designed for professionals in AI, data science, and enterprise technology, the course is perfect for those who want to gain expertise in deploying LLMs at scale. Ideal for enterprise leaders, AI practitioners, and developers, the course is suitable for learners with some experience in AI or data science. 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. By the end of the course, you will be able to manage LLM lifecycles effectively, deploy models at scale, optimize inferencing, monitor LLMs in production, implement responsible AI practices, and stay ahead of emerging trends.

Syllabus

The Data Blueprint: Crafting Effective Strategies for LLM Development

This module explores the critical role of data in developing and fine-tuning large language models (LLMs). Learners will discover strategies for data sourcing, augmentation, quality control, annotation, and bias mitigation, supported by real-world case studies and practical coding examples. By the end, participants will understand how to craft robust data pipelines that enhance LLM performance and fairness.

Managing Model Deployments in Production

This module explores the practical aspects of deploying large language models (LLMs) in enterprise environments, focusing on efficiency, compliance, and performance optimization. Learners will discover techniques such as model quantization, edge computing, and caching, while also addressing regulatory requirements and performance audits. Real-world examples and hands-on exercises illustrate how to manage and monitor LLM deployments effectively.

Accelerated and Optimized Inferencing Patterns

This module explores practical strategies for accelerating and optimizing large language model (LLM) inference, focusing on memory-efficient formats, deployment engines, and cross-platform solutions. Learners will compare leading frameworks, understand model compilation and quantization, and examine real-world use cases for scalable, low-latency deployments. Emerging trends and advanced optimization techniques are also discussed to prepare learners for cutting-edge AI deployment challenges.

Connected LLMs Pattern

This module explores the design and deployment of interconnected large language model (LLM) systems, highlighting key architectural patterns, enabling technologies, and advanced techniques for knowledge sharing and cost efficiency. Learners will examine real-world examples such as autonomous agents, programmable pipelines, and hybrid symbolic-LLM systems to understand how modern AI solutions achieve scalability, adaptability, and reliability.

Monitoring LLMs in Production

This module explores the essential practices for deploying and maintaining large language models (LLMs) in real-world production environments. Learners will gain insights into monitoring key metrics, ensuring reliability and security, optimizing costs, and scaling architectures for global deployment. Practical strategies and industry insights are provided to help build robust, efficient, and compliant LLM systems.

Responsible AI in LLMs

This module explores the ethical, technical, and regulatory challenges associated with large language models (LLMs). Learners will examine fairness by design, post hoc output filtering, real-time content moderation, and documentation practices to ensure responsible AI deployment. The module also covers strategies for enhancing safety, robustness, and the implementation of constitutional AI principles.

Emerging Trends and Multimodality

This module explores the latest advancements in multimodal artificial intelligence, focusing on how systems integrate and process diverse data types such as text and images. Learners will examine key enabling technologies, including cross-modal attention mechanisms, contrastive learning, and efficient fusion techniques, and see real-world applications in domains like healthcare. By the end, participants will understand both the technical foundations and practical implications of multimodal AI.