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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

What people are saying

        The course was great! It was full of great content, a very active cohort, and I feel I learned many methods for putting Large Language Models (LLM) into production. The course was very well-structured, starting with the basics of NLP and building up towards analyzing and describing documents using NLP techniques, all the way to deploying an API
Victor Calderon

Victor Calderon

Senior Machine Learning Engineer
        Amazing! I am leaving this course feeling empowered and equipped with the skills to leverage LLMs to build and deploy applications. I was able to implement the knowledge I learned immediately at work and with personal projects. Hamza is an awesome instructor. He is passionate about this topic and was able to simplify the concepts
Tiffany Teasley

Tiffany Teasley

Data Scientist & Career Coach at Data Sistah
        Prof Hamza Farooq is a far-sighted, application oriented AI Researcher and a great Professor. Working under him was full of learnings and practical knowledge
Darshil Modi

Darshil Modi

Cohort 1
        Hamza provided several excellent projects to learn from, showcasing quite a few ML practices and options in each. Learned a ton that I continually go back to!
Tony Dupre

Tony Dupre

Cohort 1
        Hamza's class was among my favorites of my Master's program! He makes the tools of machine learning accessible and his teaching skills are on point.
Nicole Lovold-Egar

Nicole Lovold-Egar

Beta Cohort
        Hamza was an excellent instructor. He was able to explain various machine learning techniques in ways that were easy to understand and apply them to real world problems
Dan Kellen

Dan Kellen

Beta Cohort

Meet your instructor

Hamza Farooq

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.

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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

|

Bulk purchases

Course syllabus

01

Introduction to Natural Language Processing

  1. Introduction to NLP: covers what NLP is, its history, applications and challenges.
  2. NLP Techniques: covers common techniques such as tokenization, part-of-speech tagging, named entity recognition and sentiment analysis with examples of their use.
  3. 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

  1. Transformers Foundational Knowledge: covers fundamental concepts of transformers, including self-attention, multi-head attention, and positional encoding.
  2. 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

  1. Introduction to Information Retrieval Systems: covers the basic concepts and goals of information retrieval systems and their importance in various applications.
  2. 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

  1. 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
  2. Preprocessing of Hotel Data
  3. 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

  1. 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.
  2. 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

Free resource

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..

Join Waitlist!

Learning is better with cohorts

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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A pattern of wavy dots
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

|

Bulk purchases

$700 USD

9.6

(19)

·

6 Weeks