Food Production Courses

USENIX

Deploying SRE Training Best Practices to Production - What We Learned

Explore best practices for SRE training, including sequential learning, hands-on experiences, and continuous education. Learn from Google's insights on building effective SRE teams and fostering a culture of reliability.

• YouTube
• 35 minutes
• On-Demand
• Conference Talk

0xdade

U2F Zero - Secure Hardware Design, DIY Mass Production, and Amazon Prime

Explore U2F Zero: secure hardware design, DIY production, and Amazon Prime sales. Learn about hardware acceleration, manufacturing challenges, and cost breakdown for this innovative security device.

• YouTube
• 21 minutes
• On-Demand
• Conference Talk

Databricks

Scaling Up AI Research to Production with PyTorch and MLFlow

Explore PyTorch's latest advancements in AI research and production, including distributed training, model optimization, and deployment using MLFlow, with insights on scaling and efficiency.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Databricks

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.

• YouTube
• 38 minutes
• On-Demand
• Free Video

Databricks

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.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Databricks

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.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Databricks

Taking Deep Learning to Production with MLflow and RedisAI

Explore MLflow and RedisAI integration for efficient deep learning model deployment, featuring multi-framework support, auto-batching, and DAGing capabilities in a reliable runtime environment.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Databricks

Using Docker Images in Production - Databricks Case Study

Learn how Condé Nast evolved Spire, their user segmentation service, from notebooks to Docker images on Databricks clusters. Explore the streamlined deployment workflow, challenges faced, and solutions implemented.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Databricks

Testing ML Models in Production - Detecting Data and Concept Drift

Explore robust ML model testing in production using MLflow, SciPy, and statsmodels. Learn key statistical tests, metrics for drift detection, and gain practical insights for maintaining model effectiveness.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Databricks

MLflow Model Serving - From Training to Production

Comprehensive overview of MLflow Model Serving, covering offline and online scoring, deployment options, and integration with Databricks, with practical examples and recent features.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Databricks

DataSketches: A Production Quality Sketching Library for Big Data Analysis

Explore sketching algorithms for efficient big data analysis, enabling fast approximate answers to complex queries and real-time processing of massive datasets in production environments.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Databricks

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.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Databricks

Preventing Common Pitfalls in Production Streaming Jobs

Explore key strategies for optimizing streaming jobs in production, covering input parameters, stateful streaming, output configurations, and job modifications to ensure performance and fault tolerance.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Databricks

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.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Databricks

Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps

Learn to quickly deploy deep learning models using Spark and TensorFlow, simplifying MLOps for data scientists. Explore distributed computing, feature engineering, and leveraging TensorFlow ecosystem libraries for efficient model production.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video