Learn to Align LLMs Through Post-Training in This New Course with AMD!

Why It Matters

Post‑training bridges the gap between raw LLM capabilities and production‑grade performance, making AI solutions safer and more commercially viable. Mastering these techniques is becoming essential for any organization deploying AI‑driven products.

Key Takeaways

• Post‑training tailors LLMs for production use
• Course covers fine‑tuning, RLHF, LoRA, and evaluation
• Taught by AMD VP Sharon Zhou, DeepLearning.AI instructor
• Includes pipelines for deploying safe, reliable models
• Targets developers, platform teams, AI product builders

Pulse Analysis

Post‑training has emerged as the critical final step in the LLM lifecycle, transforming generic, pre‑trained models into task‑specific, trustworthy assistants. While raw models excel at language generation, they often lack the instruction‑following behavior, reasoning depth, and safety safeguards required for enterprise use. By applying fine‑tuning, reinforcement learning from human feedback (RLHF), and parameter‑efficient methods like LoRA, organizations can dramatically reduce hallucinations, align outputs with business policies, and accelerate time‑to‑market for AI‑powered services.

The AMD‑partnered course addresses a growing skills gap among developers and AI platform teams. Sharon Zhou, AMD’s VP of AI and a veteran DeepLearning.AI instructor, brings both industry insight and academic rigor to the curriculum. Participants learn to design evaluation frameworks, detect reward‑hacking, and conduct red‑team analyses—practices that are increasingly mandated by regulatory bodies and corporate governance standards. The hands‑on modules also cover synthetic data generation and reward modeling, enabling teams to build robust pipelines without massive data collection costs.

Beyond technical mastery, the program emphasizes production readiness. Learners explore end‑to‑end deployment workflows, including go/no‑go criteria, continuous feedback loops from live logs, and monitoring strategies to maintain model reliability at scale. As enterprises integrate LLMs into developer copilots, customer support bots, and internal assistants, the ability to safely and efficiently post‑train models becomes a competitive differentiator. This course equips professionals with the practical toolkit to turn cutting‑edge research into reliable, revenue‑generating AI products.

Original Description

Learn more: https://bit.ly/47ict9O

Learn to align and optimize LLMs for real-world applications through post-training. In this course, created in partnership with AMD, you’ll learn how to apply fine-tuning and reinforcement learning techniques to shape model behavior, improve reasoning, and make LLMs safer and more reliable.

Large language models are powerful, but raw pretrained models aren’t ready for production applications. Post-training is what adapts an LLM to follow instructions, show reasoning, and behave more safely.

Many developers still assume “LLMs inherently hallucinate,” or “only experts can tune models.” Recent advances have changed what’s feasible. If you ship LLM features (e.g., developer copilots, customer support agents, internal assistants) or work on ML/AI platform teams, understanding post-training is becoming a must-have skill.

This course, consisting of 5 modules and taught by Sharon Zhou (VP of AI at AMD and instructor to popular DeepLearning.AI courses), will guide you through various aspects of post-training:

- Post-training in the LLM lifecycle: Learn where post-training fits, key ideas in fine-tuning and RL, how models gain reasoning, and how these methods power products.

- Core techniques: Understand fine-tuning, RLHF, reward modeling, and RL algorithms (PPO, GRPO). Use LoRA for efficient fine-tuning.

- Evaluation and error analysis: Design evals, detect reward hacking, diagnose failures, and red team to test model robustness.

- Data for post-training: Prepare fine-tuning/LoRA datasets, combine fine-tuning + RLHF, create synthetic data, and balance data and rewards.

- From post-training to production: Learn industry-leading production pipelines, set go/no-go rules, and run data feeedback loops from your logs.

Enroll now: https://bit.ly/47ict9O