Food Production Courses

HuggingFace

Food for Diffusion

Explore advanced diffusion techniques with Stable Diffusion co-creator Patrick Esser, covering compositional structures, texture generation, and innovative training methods for next-gen models.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

HuggingFace

Few-Shot Learning in Production

Learn to train and deploy efficient few-shot language models using SetFit, knowledge distillation, and quantization techniques. Explore accelerated inference and deployment strategies with Hugging Face and Intel AI.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

ChariotSolutions

Rust in Production: Case Studies and Value Proposition - Philly ETE 2016

Explore Rust's value proposition and real-world applications through case studies, focusing on its safety, speed, and concurrency features for systems programming.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

ChariotSolutions

Distributed Systems in Production: Tactics and Strategy - Lecture 32

Explore tactics and strategies for implementing distributed systems in production environments, covering key challenges, best practices, and future trends in scalable system architecture.

• YouTube
• 1 hour 1 minute
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Free Video

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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Free Video