DeepLearning.AI x Haystack by deepset Short Course

The world of AI is changing so fast that it can be hard to keep up. Haystack, our open source framework for production-ready LLM applications, is a flexible toolkit for building Compound AI systems. Its wide range of abstractions, integrations, and customization options make it the most robust and powerful framework available.

Thanks to a new DeepLearning.AI course taught by our DevRel lead Tuana Çelik and AI legend Andrew Ng, it is now easier than ever for those with some AI experience to get an in-depth overview of Haystack's capabilities and build advanced applications with it, while deepening their understanding of generative AI.

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Through the one-hour course, participants will get up to speed on all the key concepts in Haystack and learn how to build increasingly complex systems, culminating in an agentic chat pipeline.

The Course

"Building AI Applications With Haystack" is a free course on DeepLearning.AI. While extensive prior knowledge of AI is not required, the course assumes familiarity with the Python programming language and a solid understanding of different language models, including embedding models and generative LLMs. Since the course is self-paced, learners can pause the video, look up things they don't understand, or just play around in the Jupyter notebooks that accompany the tutorial videos.

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All Compound AI frameworks have their own conventions and terminology. The course begins by introducing specific Haystack concepts before moving on to the first project: A retrieval augmented generation (RAG) pipeline. RAG has become the standard technique for building safe and useful architectures with LLMs, and you will see how easy it is to build one in Haystack thanks to the well-designed building blocks. Projects then become increasingly complex, challenging users to build branching pipelines, design custom components, and finally build an agentic chat pipeline with function calling. The latest in AI is packed into this course, made possible by the hard work of our wonderful DevRel and Engineering teams.

DeepLearning.AI and Andrew Ng

Andrew Ng has been a leading voice in AI education and innovation for years. He teaches at Stanford and has led AI teams at Google and Baidu. As an instructor on the Coursera platform (which he co-founded), he has mentored generations of aspiring machine learning engineers and data scientists, providing accessible, free, minimalist courses with bite-sized videos that explain key concepts in AI and are designed for self-paced learning, making them truly accessible to everyone.

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Andrew later founded DeepLearning.AI, which continues the short-video format for busy professionals or students who want to quickly gain practical skills in emerging AI technologies. DeepLearning.AI's Short Courses are designed to provide focused, practical knowledge on specific AI topics in a compact format. When the team approached us about creating a course on Haystack, we were excited about the opportunity to share our wonderful framework with an even wider audience of AI enthusiasts and practitioners.

Haystack by deepset

Building robust, flexible, production-ready applications with language models has been our mission from day one and is manifested in our Haystack framework for building with LLMs. Users tell us they love Haystack for its developer friendliness and excellent documentation.

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With the recent release of Haystack 2.0, we've introduced an even more flexible and customizable LLM framework. It features composable and customizable pipelines, a common data storage interface, and a clear path to production. Haystack powers our enterprise end-to-end AI development platform, deepset Cloud, providing a seamless experience for teams looking to design, build, test, and deploy products built with Compound AI.

Start learning with Haystack and DeepLearning.AI

If you want to create production-ready LLM applications for the real world, or are just curious to get better at building with AI, sign up for the free "Building AI Applications With Haystack" course on DeepLearning.AI.

We'd also love to see you in our Discord community, on our YouTube channel and on Twitter. Connect with other Haystack users and get support. Happy building! 🙂