What you will learn

How to apply LLMs to real-world problems in natural language processing using popular libraries, such as Hugging Face and LangChain.
How to build a custom chat model leveraging open-source LLMs.
Understand the theory behind foundation models, how to fine-tune foundation models on custom datasets, and the innovations that led to GPT-4 and ChatGPT.
How to implement LLMOps and multi-step reasoning best practices.
How to evaluate the efficacy and bias of LLMs using different methods.

As Large Language Model (LLM) applications disrupt countless industries, breakthroughs such as ChatGPT are becoming household terms. The demand for LLM-based applications is skyrocketing, and this program will provide you with the skills and knowledge needed to be at the forefront of this exciting field. You will learn how you can build your own production-ready LLM-based applications, leveraging the latest and most popular natural language processing (NLP) frameworks.

Through dynamic lectures, demos, and hands-on labs taught by industry leaders and renowned researchers—such as Matei Zaharia, co-founder and chief technologist at Databricks and computer science professor at UC Berkeley—students will learn how to develop and productionize LLM applications. Brought to you by the big data company that created the popular open-source projects Apache Spark, MLflow, Delta, and Dolly, the instructors bring a unique perspective from working with F500 companies, startups, and academia.

The first course takes you through a practical tour of how to get started quickly with LLMs for common applications, including fine-tuning open-source LLMs to build your own custom chat model. You will also learn how to apply LLMOps best practices for deploying models at scale, as well as evaluate the efficacy and bias of LLMs.

The second course dives into the details of language foundation models. You will learn the innovations that led from LSTMs to Transformers, including BERT, GPT, and T5, and the key breakthroughs that led to the model powering ChatGPT.

By the end of the program, you will have built your own end-to-end LLM workflows that are ready for production. Upon completion, you will be well-equipped to pursue careers as LLM developers, data scientists, and engineers, and to build innovative solutions to complex natural language processing problems.

If you’d like to audit the program, you’ll need to navigate to the individual course page to audit each course.

Expert instruction

2 skill-building courses

Self-paced

Progress at your own speed

3 months

4 - 9 hours per week

$178.20

~~$198~~USD

For the full program experience

Courses in this program

Databricks' Large Language Models Professional Certificate
4–10 hours per week, for 6 weeks
This course is aimed at developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks. By the end of this course, you will have built an end-to-end LLM workflow that is ready for production!
View the course
4–8 hours per week, for 4 weeks
This course dives into the details of foundation models in large language models (LLMs). You will learn the innovations that led to the proliferation of transformer-based models, including BERT, GPT, and T5, and the key breakthroughs that led to applications such as ChatGPT. Additionally, you will gain understanding about the latest advances that continue to improve LLM functionality including Flash Attention, LoRa, AliBi, and PEFT methods.
View the course
- 90% of enterprise applications will be AI-augmented by 2025 (Source: IDC). Developers with experience in LLMs are highly sought after by companies in industries such as healthcare, finance, e-commerce, and customer service.
- Median salary for NLP engineers with expertise in LLMs is around $142,000 per year. (Source: Glassdoor)
- There has been a 105% increase in job postings that require both NLP and deep learning skills in the last three years. (Source: Burning Glass, a job market analytics firm)
- Graduates of the program may pursue careers as NLP/LLM engineers, data scientists, machine learning engineers, software developers, and research analysts, among others.