Databricks

The Databricks Lakehouse for Manufacturing

Building a Streaming Microservice Architecture: with Apache Spark Structured Streaming and Friends

Bucketing in Spark SQL 2 3 with Jacek Laskowski

Using Open Source Tools to Build Privacy-Conscious Data Systems

Unlocking Near Real Time Data Replication with CDC, Apache Spark™ Streaming, and Delta Lake

Creating, Weaponizing, and Detecting Deep Fakes | Hany Farid | Keynote Spark + AI Summit 2020

Apache Spark and Sights at Speed: Streaming, Feature Management, and ExecutionTed Malaska Capital On

Introducing Exactly Once Semantics in Apache Kafka with Matthias J. Sax

Machine Learning with PyCaret

Architect’s Open-Source Guide for a Data Mesh Architecture

Databricks Connect Powered by Spark Connect: Develop and Debug Spark From Any Developer Tool

Apache Spark Performance Troubleshooting at Scale, Challenges, Tools, and Methods with Luca Canali

Apache Spark MLlib's Past Trajectory and New Directions - Joseph Bradley

Building Robust ETL Pipelines with Apache Spark - Xiao Li

Discover the Data Lakehouse

Clean Your Data Swamp by Migrating Off of Hadoop

Introducing the Next Generation Data Science Workspace | Keynote Spark + AI Summit 2020

Building a Unified Data Pipeline with Apache Spark and XGBoost with Nan Zhu

Hybrid Apache Spark Architecture with YARN and Kubernetes

From Idea to Model: Productionizing Data Pipelines with Apache Airflow

A Deep Dive into Spark SQL's Catalyst Optimizer with Yin Huai

Accelerating Data Ingestion with Databricks Autoloader

Managing Data Encryption in Apache Spark™

LLM Module 4: Fine-tuning and Evaluating LLMs | 4.2 Module Overview

LLM Module 4: Fine-tuning and Evaluating LLMs | 4.3 Applying Foundation LLMs

Accelerate Your ML Pipeline with AutoML and MLflow

LLM Module 2 - Embeddings, Vector Databases, and Search | 2.2 Module Overview

Simplifying Change Data Capture using Databricks DeltaDr Ameet Kini Databricks

Tutorial - Getting Started with Databricks Repos | Databricks Academy

LLM Module 4: Fine-tuning and Evaluating LLMs | 4.4 Fine Tuning: Few-shot Learning

LLM Module 2 - Embeddings, Vector Databases, and Search | 2.5 Vector Stores

Scale and Optimize Data Engineering Pipelines with Best Practices: Modularity and Automated Testing

Driving Real-Time Data Capture and Transformation in Delta Lake with Change Data Capture

Scaling Python for Data Science Using Apache Spark (Garren Staubli)

Simplify Data Conversion from Spark to TensorFlow and PyTorch

LLM Module 6: LLMOps | 6.7 Notebook Demo

LLM Module 4: Fine-tuning and Evaluating LLMs | 4.1 Introduction

A Journey to Building an Autonomous Streaming Data Platform—Scaling to Trillion Events Monthly

LLM Module 2 - Embeddings, Vector Databases, and Search | 2.3 How does Vector Search work

LLM Module 2 - Embeddings, Vector Databases, and Search | 2.1 Introduction

LLM Module 4: Fine-tuning and Evaluating LLMs | 4.10 Task specific Evaluations

LLM Module 2 - Embeddings, Vector Databases, and Search | 2.8.2 Notebook Demo Part 2

LLM Module 2 - Embeddings, Vector Databases, and Search | 2.6 Best Practices

Responsible AI: Protecting Privacy and Preserving Confidentiality in ML and Data Analytics

Building a Better Delta Lake with Talend and Databricks

Tangram Distributed Scheduling Framework for Apache Spark at Facebook

Building a Business Logic Translation Engine with Spark Streaming for Communicating -Patrick Bamba

Prompt Engineering is Dead; Build LLM Applications with DSPy Framework

Real-Time Forecasting at Scale using Delta Lake and Delta Caching

LLM Module 3 - Multi-stage Reasoning | 3.7.1 Notebook Demo Part 1

Encoding multi-layered Vega-Lite COVID-19 Geodata visualizations