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.
Combining Machine Learning and MLflow with Lakehouse Architecture - Data Brew Episode 5
Explore how Quby leverages ML and MLflow with their lakehouse to extract value from IoT sensor data, moving from batch to real-time streaming for energy optimization and customer insights.
MATS Stack for Cross-System Orchestration of Machine Learning Pipelines
Explore the MATS stack for orchestrating ML pipelines across systems, featuring MLFlow, Airflow, Tensorflow, and Spark. Learn how Avast leverages this approach for efficient phishing detection and streamlined ML workflows.
Spark NLP - State-of-the-Art Natural Language Processing at Scale
Discover scalable NLP techniques using Apache Spark, covering core algorithms, BERT embeddings, and multi-lingual challenges. Learn to build efficient pipelines for various NLP tasks through practical demonstrations.
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.
MLflow at Company Scale - Scaling and Optimizing for High-Volume Machine Learning
Learn how Criteo scaled MLflow to handle massive data volumes, including horizontal scaling, monitoring, SSO integration, and hadoop cluster integration for data scientists.
Data Mesh in Practice - Beyond the Data Lake for Fashion E-Commerce
Explore Zalando's journey from centralized Data Lake to distributed Data Mesh architecture, emphasizing data ownership, quality, and accessibility through decentralized domain-focused approach and Data Products concept.
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.
Taking Machine Learning to Production with MLflow - New Features and Best Practices
Explore MLflow's latest features for productionizing machine learning, including model management, CI/CD, data schemas, and integration with PyTorch for seamless deployment and operation of ML applications.
Introducing MLflow for End-to-End Machine Learning on Databricks
Explore end-to-end machine learning with MLflow on Databricks: from data engineering to model deployment, using health data to predict life expectancy through Spark, hyperopt, and interactive dashboards.
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.
Deep Learning for Price Action Prediction Using Databricks and AWS
Explore deep learning techniques to enhance price action prediction using Databricks and AWS. Learn to optimize technical indicators and train models with Amazon SageMaker for improved financial forecasting.
The Killer Feature Store - Orchestrating Spark ML Pipelines and MLflow for Production
Explore feature store implementation as an orchestration engine for ML pipeline stages using Spark and MLflow, focusing on deployment management, A/B testing, discovery, and governance.
Benchmarks and How-Tos for Convolutional Neural Networks on HorovodRunner-Enabled Apache Spark Clusters
Explore benchmarks and implementation techniques for distributed deep learning using HorovodRunner on Apache Spark clusters, focusing on CNN tasks and scaling efficiency for both GPU and CPU clusters.
Tuning Machine Learning Models - Scaling, Workflows, and Architecture
Automating and scaling ML model tuning using Hyperopt and Apache Spark. Best practices for workflows, architecture, and optimization in hyperparameter tuning for improved performance and accuracy.
Productionalizing Models through CI/CD Design with MLflow
Explore MLflow integration with CI/CD for streamlined model deployment. Learn to design and implement automated pipelines using the model registry, enhancing efficiency in machine learning workflows.
Apache Spark NLP for Healthcare - Building Real-World AI Systems
Explore real-world healthcare AI systems using NLP, covering patient risk prediction, diagnosis automation, and revenue optimization. Learn key considerations for building domain-specific models and scalable ML pipelines.
Productionizing Machine Learning with Microservices Architecture
Learn to deploy ML models as microservices using Spark, enabling auto-scaling, versioning, and security. Discover how to aggregate real-time and historical data for inferencing and create actionable insights.