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Free course “LangChain and vector databases in production”

The LangChain & Vector Databases in Production course is a collaborative effort between Activeloop, Towards AI and the Intel Disruptor Initiative to equip professionals with the tools to master Large Language Models (LLMs) from training to release into production.

It is free and a self-study course. And the LangChain & Vector Databases in Production certification is the first of three that together lead to the Gen AI 360 Foundational Model Certification.

The first part of the course offers a comprehensive overview of the fundamental model theory and its application to practical projects using LangChain and Deep Lake.

It considers:

LangChain is an open source framework that makes it easy to apply LLM to your own data so that you can work with it in natural language like ChatGPT chat, but on your own data, not the one that ChatGPT was trained on default. LangChain allows you to do this.

The data format that LangChain works with is called vector representations (vector embedding) – this is a data type that carries the semantic information necessary for AI to understand the context. Of course, the best storage for such data is a vector database, such as Data Lake, which is optimized for this purpose and allows you to query this data.

This course will show you how to use LangChain with the Deep Lake multimodal vector database using over 10 case studies. In total, you can expect 40 hours of study material. Please note that the course is mostly text-based and contains only 4 videos for the entire course.

The initial training requirements are low – only some familiarity with Python and programming is required to complete projects. In addition, the course is interdisciplinary, which means that professionals from various industries, and not just software developers, can take it.

The course includes hands-on projects and exercises that will require the use of various API keys. Basically it is the key of OpenAI and Deep Lake. The total cost of completing all the lessons in this course, as well as some of the experiments, will be less than $3. But you can also study this course locally without paying a dime with GPT4All.

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