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Fine-Tuning LLMs
5.0 (6)
4 Weeks
·Cohort-based Course
Achieve optimal performance from your fine-tuned LLM.
Class is in session
Fine-Tuning LLMs
5.0 (6)
4 Weeks
·Cohort-based Course
Achieve optimal performance from your fine-tuned LLM.
Previous Clients Include
Course overview
Fine-tuning that *actually* works
A properly fine-tuned LLM can achieve performance that outshines leading foundation models. However, achieving these results is not as simple as making an API call. In this course, we'll demystify fine-tuning by systematically going step-by-step through the entire process.
Step 1: Curate Data
Step 2: Train Model
Step 3: Evaluate Results
Step 4: Apply Guardrails
Step 5: Deploy Model
Step 6: Improve Results
For each step, I will provide proven strategies and real-world case studies from my experience as an AI consultant.
When it comes to fine-tuning, the devil is in the details. Practitioners must be precise and exacting to reach peak performance. I will provide hands-on help so that students successfully build an end-to-end fine-tuning solution during the course project.
Who is this course for
01
Software engineers and data scientists who have dabbled with LLMs and are considering a career switch to AI.
02
AI and ML engineers who want to enhance their skillset.
03
Startups who are evaluating whether fine-tuning would be beneficial for their company.
What you’ll get out of this course
Curate High-Quality Data
Create a fine-tuning dataset that is ideal for your use case.
Adjust your dataset to handle nuances and edge cases.
Train a Model
Apply the QLoRA algorithm to fine-tune a model (and understand what's actually happening under the hood!).
Conduct hyperparameter tuning to optimize training for your use case.
Evaluate Results
Design a test suite to systematically evaluate model results.
Identify weaknesses in your model and determine how to fix them.
Optimize Costs and Scale
Deploy your model using techniques such as quantization to reduce costs.
Optimize inference parameters for your use case.
Build a Data Flywheel
Analyze the production impact of your model.
Implement a process to continuously improve results.
Explore Open-Source LLMs
Identify the pros and cons of various open-source LLMs and evaluate how their performance compares to the leading proprietary models.
Analyze the current state of LLMs and where they are likely to go in the future.
This course includes
4 interactive live sessions
Lifetime access to course materials
31 in-depth lessons
Direct access to instructor
7 projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Fine-Tuning LLMs
Course syllabus
Week 1
Welcome! Let's Get Started
Never Fine-Tune Before You Do These Things!
Fine-Tuning Basics
Nov
5
Q&A Session 1
Week 2
Train Your Model
Nov
12
Q&A Session 2
Week 3
Evaluation Frameworks
Dataset Improvement Strategies
Nov
19
Q&A Session 3
Week 4
Deploy Your Model
Nov
26
Q&A Session 4
Bonus
Private Coaching Session
5.0 (6 ratings)
What students are saying
7 Cups Increases Engagement 250% Through Fine-Tuning
Discover how 7 Cups fine-tuned an LLM to create an AI therapist that is 250% more engaging than leading proprietary LLMs.
Get the free case study
Meet your instructor
Scott Kramer
I've led AI at startups across every stage, from founding engineer through IPO (3 out of 3 successful exits).
After my last exit, I became a solopreneur and specialize in helping companies fine-tune LLMs.
This course is a culmination of my learnings from successfully fine-tuning LLMs for clients.
Course schedule
4-6 hours per week
Self-Paced Lessons
2 hours per week
1-2 modules per week with lessons walking you through the step-by-step process of fine-tuning an LLM. Each module includes recorded demo videos, coding examples, and additional resources to help you complete the next project.
Course Project
2 hours per week
Students will design and implement a fine-tuned model of their choosing. Each week, students will apply strategies from the lessons to further improve their model.
Q&A with Scott
1 hour per week
Live Q&A calls with Scott and the rest of the cohort to answer questions and receive hands-on help with that week's projects.
Private Coaching Session
1 hour session
Each student will receive a live coaching session with Scott to review their project, receive personalized feedback, and discuss how to improve their AI skills further.
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Frequently Asked Questions
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