Machine Learning: Add video recording of FOSDEM 2024 talk

Author: amotl

docs/domain/ml/index.md

@@ -9,8 +9,7 @@
 :::
 
 Integrate CrateDB with machine learning frameworks and
-tools, for [MLOps] and [Vector database] operations.
-
+tools, for MLOps and vector database operations.
 
 :::::{grid}
 :padding: 0
@@ -28,7 +27,7 @@ operations.
 CrateDB, as a universal SQL database, supports this process through
 adapters to best-of-breed software components for MLOps procedures.
 
-MLOps is a paradigm that aims to deploy and maintain machine learning models
+[MLOps] is a paradigm that aims to deploy and maintain machine learning models
 in production reliably and efficiently, including experiment tracking, and in
 the spirit of continuous development and DevOps.
 ::::
@@ -47,7 +46,7 @@ These feature vectors may be computed from raw data using machine learning
 methods such as feature extraction algorithms, word embeddings, or deep
 learning networks. 
 
-Vector databases can be used for similarity search, multi-modal search,
+[Vector databases][Vector Database] can be used for similarity search, multi-modal search,
 recommendation engines, large language models (LLMs), retrieval-augmented
 generation (RAG), and other applications.
 ::::
@@ -206,6 +205,36 @@ of information, using a technique known as Retrieval Augmented Generation,
 or RAG. RAG is a technique for augmenting LLM knowledge with additional data.
 
 
+:::{rubric} Video Tutorials
+:::
+
+::::{info-card}
+
+:::{grid-item} **How to Use Private Data in Generative AI**
+:columns: auto auto 8 8
+
+In this video recorded at FOSDEM 2024, we explain how to leverage private data
+in generative AI on behalf of an end-to-end Retrieval Augmented Generation (RAG)
+solution.
+
+- [How to Use Private Data in Generative AI] (Video)
+- [End-to-End RAG with CrateDB and LangChain] (Slides)
+:::
+
+:::{grid-item}
+:columns: auto auto 4 4
+
+<iframe width="240" src="https://www.youtube-nocookie.com/embed/icquKckM4o0?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+&nbsp;
+
+{tags-primary}`Fundamentals` \
+{tags-secondary}`Generative AI`
+{tags-secondary}`RAG`
+:::
+
+::::
+
+
 (langchain)=
 ### LangChain
 
@@ -320,7 +349,9 @@ tensorflow
 [automl-classify-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/automl/automl_classification_with_pycaret.ipynb
 [automl-forecasting-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/automl/automl_timeseries_forecasting_with_pycaret.ipynb
 [automl-forecasting-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/automl/automl_timeseries_forecasting_with_pycaret.ipynb
+[End-to-End RAG with CrateDB and LangChain]: https://speakerdeck.com/cratedb/how-to-use-private-data-in-generative-ai-end-to-end-solution-for-rag-with-cratedb-and-langchain
 [How to set up LangChain with CrateDB]: https://community.cratedb.com/t/how-to-set-up-langchain-with-cratedb/1576
+[How to Use Private Data in Generative AI]: https://youtu.be/icquKckM4o0?feature=shared
 [Jupyter Notebook]: https://jupyter.org/
 [LangChain]: https://python.langchain.com/
 [LangChain: Analyzing structured data]: https://python.langchain.com/docs/use_cases/qa_structured/sql