Terraform Templates for Secure Databricks Deployments from Day Zero
Explore Databricks' Security Reference Architecture: Terraform templates for AWS, Azure, and GCP. Learn best practices for secure deployments, from network firewalls to Unity Catalog external locations.
Best Practices for Unit Testing PySpark
Learn effective techniques for unit testing PySpark code, including local and CI testing, code structuring for testability, and integration testing with clusters for enhanced reliability and easier refactoring.
BigQuery Native Integration with Delta Lake - The Latest Announcement
Explore BigQuery's native querying of Delta Lake tables, enhancing data strategy with improved performance and cross-cloud capabilities. Learn about GCP's open platform integrations and interoperability features.
Designing Cloud Storage for LLMs and Data-Intensive Workloads
Optimize storage for LLMs and AI/ML workloads: Learn to select and configure cloud storage solutions for high-performance data pipelines, training, and inference on Google Cloud and Databricks.
Exploring UDTFs and Polymorphism in PySpark
Learn to create and enhance User-Defined Table Functions in PySpark, including polymorphic UDTFs for flexible data processing. Gain practical insights through examples and expert guidance.
Delivering Domain-Specific LLMs with GPU Serving - Case of IFC MALENA
Explore IFC's AI journey: Scaling MALENA platform with Lakehouse, optimizing LLM serving, and leveraging GPU for enhanced performance in addressing global development challenges.
Extending Data Governance for AI Use with Collibra and Databricks
Explore strategies for extending data governance to AI systems, ensuring trust, compliance, and collaboration while integrating Collibra with Databricks Unity Catalog for tailored solutions.
Cloud-Based Generative AI Supporting Preliminary Engineering Design
Optimize engineering design with cloud-based generative AI. Learn how Rolls-Royce and Databricks reduced cGAN training time by 30x using distributed computing, MLflow, and Unity Catalog for aerospace compliance.
Simplify GenAI App Development with Secure, Custom AI Agents
Explore best practices for managing AI model lifecycles, focusing on governance, monitoring, and performance tracking. Learn to optimize model delivery while ensuring compliance and accountability.
Responsible AI on the Databricks Data Intelligence Platform
Explore ethical AI implementation using Databricks' platform. Learn about governance, compliance, security, and tools for safe AI integration in business.
Leveraging AI to Uncover Disparities and Transform Healthcare in Low and Middle Income Countries
Explore how AI and data analytics are revolutionizing global healthcare delivery, focusing on identifying medical deserts and improving access to services in low and middle-income countries.
Efficient MLOps: Developing and Deploying ML Models with Databricks
Explore efficient MLOps framework design for ML model development and deployment, covering environment management, CI/CD pipelines, and tools like Unity Catalog and MLflow for model monitoring and progression.
Methods for Evaluating Your GenAI Application Quality
Explore comprehensive tools for evaluating GenAI applications, including inference tables, Lakehouse Monitoring, and MLflow. Learn best practices for offline and real-time quality assurance in production environments.
Data Discovery at Databricks with Amundsen - Improving Productivity and Trust
Explore how Databricks leverages Amundsen for efficient data discovery, improving productivity by surfacing relevant datasets, SQL dashboards, and metadata programmatically.
Introducing MLflow for End-to-End Machine Learning on Databricks
Learn to predict life expectancy using health data with MLflow. Covers data engineering, exploration, model tuning, and deployment on Databricks' unified analytics platform powered by Apache Spark.
Model Experiments Tracking and Registration Using MLflow on Databricks
Automate ML model experimentation and tracking using MLflow on Databricks. Learn to streamline data preparation, model development, and artifact management for efficient machine learning workflows.
ML Production Pipelines: Building and Deploying a Classification Model
Learn to build and deploy production-ready machine learning pipelines using Python, Databricks, and MLflow. Explore training and prediction workflows, model registration, and deployment options for both batch and on-demand processing.
Implementing a GAN to Remove People from Images - Databricks Lightning Talk
Learn to implement a pix2pix GAN on Databricks for removing people from images, exploring the basics, idea, demo, and analysis of this innovative image editing technique.
Accelerating the ML Lifecycle with Enterprise-Grade Feature Stores
Explore enterprise-grade feature store for ML lifecycle acceleration, addressing data engineering challenges, and empowering data scientists to streamline ML application delivery with centralized data management and instant feature serving.
Productionizing Machine Learning Pipelines with Databricks and Azure ML
Jumpstart ML projects with Databricks and Azure ML. Learn to build, track, and deploy production-ready pipelines using tools like MLflow, TensorFlow, and PyTorch for efficient, reproducible data science workflows.
Azure and Databricks Enabling Personalized Customer Experience at CVS Health
Explore how CVS Health leveraged Azure and Databricks to personalize customer experiences, implement machine learning models, and drive business value through data-driven decision-making and experimentation.