Enable Production ML with Databricks Feature Store
Simplify production ML with Databricks Feature Store. Learn to overcome data challenges, enable real-time inference, and streamline ML projects using a unified data lakehouse platform integrated with Apache Spark and MLflow.
AutoML Toolkit Deep Dive - Automating Feature Engineering and Model Optimization
Explore Databricks Labs' AutoML Toolkit for automating feature engineering, model selection, tuning, and deployment. Learn to accelerate machine learning workflows and enhance productivity.
Managing the Complete Machine Learning Lifecycle with MLflow - Introduction to MLflow Tracking
Introduction to MLflow: tracking experiments, packaging projects, deploying models, and managing lifecycle. Learn to record, reproduce, and compare ML runs using MLflow's components and UI.
State of the Industry - Machine Learning Platforms and MLOps
Explore the state of MLOps and ML platforms with industry leaders, focusing on challenges, common components, and Databricks' approach to unified data analytics and machine learning.
How to Build a Cloud Data Platform - Part 1: Architecture
Learn to build a unified cloud data platform using Delta Lake, combining data warehouses and lakes for improved reliability, performance, and operations.
Implementing End-to-End Demand Forecasting with Databricks and MLflow
Discover how to implement a scalable demand forecasting solution using Databricks and MLflow, improving retail efficiency and reducing food waste through accurate production and delivery planning.
Koalas - Scaling Pandas API on Apache Spark
Learn to scale pandas operations to big data using Koalas, an open-source project implementing the pandas API on Apache Spark. Transition seamlessly from single-machine to distributed environments for large-scale data science.
Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production
Explore scaling data and ML with Apache Spark and Feast, focusing on feature engineering challenges and solutions for big data-driven machine learning in production environments.
Generative Hyperloop Design - Managing Massively Scaled Simulations for Demand Modeling
Explore large-scale simulation framework for Hyperloop design, utilizing cloud computing, data pipelines, and machine learning to address key technical and business questions for safe and efficient mass transportation.
Productionizing Machine Learning with Apache Spark, MLflow and ONNX - Cloud Deployment Using SQL Server
Explore productionizing machine learning using Apache Spark, MLflow, and ONNX with SQL Server. Learn model management, lifecycle orchestration, and leveraging SQL Server for ML model storage and deployment.
End to End MLOPS Data Science Project Implementation With Deployment
Comprehensive MLOps project implementation covering data ingestion, validation, transformation, model training, and deployment on AWS EC2 using MLflow and GitHub Actions.
Train and Track ML Models with MLflow in Microsoft Fabric
Explore MLflow integration in Microsoft Fabric for efficient machine learning model experimentation, tracking, and management using familiar notebook environments.
Key Insights from a Fluffy AI Vision to a Complete ML-Product
Explore the journey from AI concept to ML product, focusing on technical solutions and stakeholder engagement. Learn effective strategies for implementing machine learning workflows and communicating their value.
Databricks for MLOps - Experiment Tracking, Model Packaging, and Deployment
Master MLflow in Databricks: Experiment tracking, model packaging, and deployment for efficient machine learning operations.
MLOps: Comparing Microsoft Phi3 Mini 128k in GGUF, MLFlow, and ONNX Formats
Explore Microsoft Phi3 Mini 128k model: compare inference time and precision across MLFlow, GGUF, and ONNX formats. Learn quantization techniques and performance optimization for various platforms.
MLOps with MLFlow: Working with Spacy NLP and Training NER Models
Explore Spacy NLP models in MLflow and learn to train a custom NER component using your own data for advanced natural language processing tasks.
Turbo Charge Your Data Science Workflow with Microsoft Fabric
Discover how Microsoft Fabric's SaaS platform enhances data science workflows, leveraging MLflow and Semantic Link for advanced machine learning model development.
Auto-Train a Time-Series Forecast Model with AML & ADB
Learn to auto-train time-series forecast models using Azure Machine Learning and Azure Databricks for large-scale enterprise solutions in supply chain, healthcare, insurance, and finance.
Auto-Train a Time-Series Forecast Model with Azure Machine Learning and Azure Databricks
Discover how to efficiently build accurate forecasting models at scale using Azure Machine Learning and Azure Databricks, enhancing enterprise AI/ML capabilities for various industries.
Bosch DataOps: NLP Development and Pipeline Orchestration on Kubernetes
Discover how to scale NLP development using Kubernetes, featuring DataOps practices, pipeline orchestration, and MLOps tools integration within AWS cloud for production-ready systems.
Scaling Open Source ML: How Wolt Uses Kubernetes to Deliver Great Food to Millions
Discover how Wolt leverages Kubernetes and open-source frameworks to scale machine learning operations, addressing challenges in forecasting, recommendations, and delivery time predictions for millions of users.