den Broek - Databricks since 2023 - Focus on Dutch & Belgian public sector and Financial services - 15 years of ‘data experience’ as DE, DS, DA, PO… - linkedin.com/in/victorvdb Solutions Architect @ Databricks
5000+ global employees $1B+ in revenue Inventor and pioneer of the data lakehouse Gartner-recognized Leader Database Management Systems Data Science and Machine Learning Platforms The Lakehouse Company Creator of
and Foundation Models 5 Artificial Intelligence (AI) Multidisciplinary field of computer science that aims to create systems capable emulating human intelligence Machine Learning (ML) Learn from existing data and make predictions without being explicitly programmed Deep Learning (DL) Use artificial neural networks to learn from data Generative AI Subfield of AI focussing on generating new data (images, text, audio, code, ...) LLM Models trained on massive datasets to achieve advanced language processing capabilities Foundation Models (GPT-4, BARD, MPT-7B, …) LLMs which can serve as the base for a wide range or applications
that new Why should I care now? Accuracy and effectiveness has hit a tipping point • Many new use cases are unlocked! • Accessible by all. Readily available data and tooling • Large datasets. • Open-sourced model options. • Requires powerful GPUs, but are available on the cloud.
0 Plain foundational models “Everyone” has done this - go to ChatGPT and ask questions without much engineering. Typical enterprise use cases: - Text summarization - Text classification - Generic coding assistants
Monitoring Data Collection and Preparation DATA PLATFORM UNITY CATALOG Datasets Models Applications Curated AI Models Model Serving optimized for LLMs MLflow AI Gateway Plain LLM Lakehouse Monitoring
1 Prompt engineering Add contextual information in the prompt, to give the model specific information pertaining to the question. Typical enterprise use cases: - Customer service chatbots - Specific coding assistants
Monitoring Data Collection and Preparation DATA PLATFORM UNITY CATALOG Datasets Models Applications Curated AI Models Model Serving optimized for LLMs Lakehouse Monitoring MLflow AI Gateway Feature Serving Mlflow Evaluation Plain LLM Simple prompt engineering
2 Fine tuning Using data you have available, you can fine tune LLMs to fit your use case. Depending on whether they are open-source or closed source, the methodology will differ. Regardless, it will require data specific to your use case, and engineering capabilities - humans and hardware! Typical enterprise use cases: - LLM fine tuned to answer questions in a specialist area (e.g. legal, medical)
Monitoring Data Collection and Preparation DATA PLATFORM UNITY CATALOG Datasets Models Applications Feature Serving Curated AI Models AutoML for LLM training Model Serving optimized for LLMs Lakehouse Monitoring MLflow AI Gateway Mlflow Evaluation Plain LLM Simple prompt engineering Fine tuning
3 Retrieval Augmented Generation Encode all relevant data you have with an LLM to a vector database. Then, retrieve the most relevant data and ingest them into the prompts. Basically prompt engineering on steroids, but requires you to encode all the data you have already, and keep using that LLM to encode questions as well. Typical enterprise use cases: - LLM answering about specifics in documents, such as purchase orders and contracts
Monitoring Data Collection and Preparation DATA PLATFORM UNITY CATALOG Datasets Models Applications Vector Search Feature Serving Curated AI Models AutoML for LLM training Model Serving optimized for LLMs Lakehouse Monitoring MLflow AI Gateway Mlflow Evaluation Plain LLM Simple prompt engineering Fine tuning Retrieval Augmented Generation
4 Training your own model from 0 If all else fails, or you have specific governance / IP / risk requirements, then training a model from scratch becomes an option. However this is both very difficult and very expensive, and there are currently very few enterprise use cases in which this is the solution. If you are one of them, you will know ;-)
Monitoring Data Collection and Preparation DATA PLATFORM UNITY CATALOG Datasets Models Applications Vector Search Feature Serving Curated AI Models AutoML for LLM training Model Serving optimized for LLMs Lakehouse Monitoring MLflow AI Gateway Mlflow Evaluation Plain LLM Simple prompt engineering Fine tuning Retrieval Augmented Generation Training from scratch
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