Bringing an AI System from Proof of Concept to Deployment
Learn to create production-grade AI systems: from MVP to scaling, performance tuning, and MLOps. Gain insights on hardware sizing, latency optimization, and avoiding common pitfalls.
Fine-Tuning Language Models with Declarative ML Orchestration
Gain hands-on experience using Flyte to declaratively specify infrastructure for fine-tuning language models on your own data, overcoming compute resource challenges.
Automating Knowledge Work with Generative AI
Explore generative AI's impact on knowledge work, learn to apply models for business solutions, and gain hands-on experience in automation techniques.
Building a Fraud Detection Model with Feature Stores - Includes Shopify Case Study
Develop a real-time fraud detection system using ML data pipeline tools. Learn to build, deploy, and serve data pipelines with Feast and Tecton. Explore architectural patterns and model stages.
An Introduction to Drift Detection
Explore drift detection in machine learning, covering types, detection methods, and practical applications using Alibi Detect. Gain hands-on experience with real-world examples and state-of-the-art techniques.
Leveraging ML Recommendation Models to Improve Customer Engagement
Explore personalization in retail using Amazon Personalize. Learn about ML-powered recommendation systems, real-time personalized experiences, and their impact on customer engagement.
Responsible AI Toolbox: Practical Approaches and Tools for Ethical Machine Learning
Explore practical approaches to responsible AI and learn how open-source and cloud-integrated capabilities empower data scientists to understand and improve ML models ethically.
D-SOS: Dataset Shift with Outlier Scores
Explore a scalable two-sample test for dataset shift using outlier scores, focusing on low-density regions and comparing contamination rates across samples.
Building Data-Centric Applications with Federated Learning
Explore federated learning to overcome data silos and enhance machine learning models. Learn benefits, challenges, and practical applications in a hands-on environment.
10X Faster Machine Learning: From R&D to Production
Supercharge ML experimentation with PyTorch Lightning and DVC, and streamline production using Kubeflow. Learn to overcome challenges in complex ML model development and deployment.
How You Can Start Designing AI with Purpose
Explore ethical AI design principles and learn to create purposeful, responsible machine learning systems for real-world applications.
Deep AutoViML for TensorFlow Models and MLOps Workflows
Explore Deep AutoViML for building powerful TensorFlow models and MLOps workflows. Learn to fast prototype data pipelines and models using TF 2.4+ and Keras preprocessing layers.
Federated Learning: Unlocking the Value of Distributed Data
Explore federated learning to leverage distributed data for robust machine learning models without centralizing sensitive information. Learn benefits, challenges, and practical applications.
Modeling, Querying and Seeing Time Series Data within a Self-Organizing Mesh Network
Explore graph database modeling for dynamic power grid networks. Learn to query and analyze sensor data, triage at-risk components, and understand network healing processes.
On-Device Training and Transfer Learning - Lecture 15
Explore on-device training and transfer learning techniques, focusing on memory bottlenecks, efficient algorithms like TinyTL, and system support for optimized on-device training.
On-Device Training and Transfer Learning - Lecture 15
Explore on-device training and transfer learning techniques, focusing on memory bottlenecks, efficient algorithms like TinyTL, and system support for optimized on-device training.
TinyEngine - Efficient Training and Inference on Microcontrollers - Lecture 17
Explore TinyEngine library for efficient neural network training and inference on microcontrollers. Learn techniques to optimize deep learning for resource-constrained devices.
Efficient Video Understanding and Generative Models - Lecture 19
Explore efficient video understanding and generative models, focusing on techniques for deploying neural networks on resource-constrained devices and optimizing deep learning applications.
Efficient Video Understanding and Generative Models - Lecture 19
Explore efficient video understanding and generative models, focusing on techniques for deploying neural networks on resource-constrained devices and optimizing deep learning applications.
EfficientML.ai: Quantization Part II - Lecture 6
Explore advanced quantization techniques for efficient machine learning, including Rooney quantization, scaling factors, and fine-tuning strategies.
Basics of Neural Networks - Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques for efficient machine learning model development.