Building LLM Applications from scratch into Production
9.6
(19 ratings)
6 Weeks
·Cohort-based Course
Gain a thorough understanding of the world of Machine Learning Deployment with a special focus on SOTA Language Models
Hosted by
Hamza Farooq
AI Specialist & Adjunct Professor | 15+ years | Google | Stanford | UCLA
This course is popular
4 people enrolled last week.
Course overview
Develop your production level ML Solutions
After our sold out first three Cohorts [cumulative of 120 students!], we are ready for Cohort 4.
Learning Outcome:
Upon completing this course, students will undergo a transformation in their understanding of machine learning with a focus on production and LLMs. They will gain practical skills in building and deploying machine learning models in a production environment.
Additionally, students will gain a comprehensive understanding of the end-to-end machine learning pipeline, allowing them to construct and deploy robust and effective models in real-world settings using Large Language Models. Overall, students will emerge with greater confidence in their abilities to tackle practical machine learning problems and deliver results in production.
Class begins 2nd December
Who is this course for
01
You are intrigued about LLMs and would like to learn more!
02
You are ready to deploy your own SOTA AI Models and like to see how they work
03
You want to go beyond Jupyter Notebook and develop batch or real-time prediction
What you’ll get out of this course
Collect and preprocess data for large language models
Train and fine-tune pre-trained large language models for specific tasks
Evaluate the performance of large language models and select appropriate metrics
Deploy large language models in real-world applications using APIs and Huggingface
Understand ethical considerations involved in working with large language models, such as avoiding bias and ensuring transparency
Building LLM Applications from scratch into Production
What people are saying
Victor Calderon
Tiffany Teasley
Darshil Modi
Tony Dupre
Nicole Lovold-Egar
Dan Kellen
Meet your instructor
Hamza Farooq
Ex-Google Adjunct Professor @Stanford | @U of Minnesota
I have over 15 years of experience of leading and building ML teams and have been teaching for the past three years.
Join an upcoming cohort
Building LLM Applications from scratch into Production
Cohort 4
$700 USD
Dates
Dec 2—Jan 7, 2024
Application Deadline
Nov 27, 2023
Course syllabus
01
Introduction to Natural Language Processing
- Introduction to NLP: covers what NLP is, its history, applications and challenges.
- NLP Techniques: covers common techniques such as tokenization, part-of-speech tagging, named entity recognition and sentiment analysis with examples of their use.
- NLP Tools: introduces popular NLP tools such as NLTK, spaCy with examples of how to use them
02
Foundational Knowledge of Transformers & ML System Design
- Transformers Foundational Knowledge: covers fundamental concepts of transformers, including self-attention, multi-head attention, and positional encoding.
- Introduction to Fundamental Concepts of ML System Design: covers the basics of designing machine learning systems, including data collection and preprocessing, model selection and training
03
Retrieval Systems
- Introduction to Information Retrieval Systems: covers the basic concepts and goals of information retrieval systems and their importance in various applications.
- FAISS: introduces Facebook's AI Similarity Search (FAISS) library, a popular open-source library for efficient similarity search and clustering of dense vectors, and explains how it works.
04
Building a search engine from bare bones and deploying it on Huggingface using server-less inference
- Deployment on a Serverless Inference: discusses the benefits of serverless computing and how to deploy the semantic search model on a serverless platform like Huggingface
- Preprocessing of Hotel Data
- Evaluation of the Model: discusses the different evaluation metrics used for semantic search models and how to measure the performance of the model
05
Knowledge Graph, Generative AI, Alpaca,Llama and Dolly 2.0 and World of langchain on larger corpus of Data
- Introduction to Knowledge Graph: covers the basics of knowledge graphs, including their architecture, data modeling, and how they are used in real-world applications like search engines and recommendation systems.
- Generative AI: introduces the concept of generative AI and how it is used to generate new data, such as text, images, and music.
06
Fine tuning Models using PeFT and QLoRa
- Understand the difference between prompt engineering and fine tuning
- Introducing bits and bytes
- Fine tuning GPT3
07
Demo Day
Teams get to present their learnings through a capstone project
Course schedule
4-6 hours per week
Sunday: Module Teaching
8am - 10:00am PST
We will go through each module during this class
Fridays
9:00 - 09:30am PST
Office Hours: online
Weekly projects
2-4 hours per week
Students will spend time building projects with their team members or individually
Building LLM Applications from Scratch
this course with a focus on production and LLMs is designed to equip students with practical skills necessary to build and deploy machine learning models in real-world settings. Be part of the first 20 people cohort. More in email link..
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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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Join an upcoming cohort
Building LLM Applications from scratch into Production
Cohort 4
$700 USD
Dates
Dec 2—Jan 7, 2024
Application Deadline
Nov 27, 2023