How to Automate Machine Learning with GitHub Actions
Automate machine learning workflows using GitHub Actions to speed up development cycles and streamline model deployment.
Productionizing ML Models for Online Shopping at Loblaws
Explore ML model productionization for online shopping, covering microservices, CI pipelines, AB testing, and cloud-based implementation using tools like Seldon Core.
A Tale of A Thousand Pipelines: Building Scalable Deep Learning Pipelines for Time-Series Datasets
Discover scalable deep learning pipelines for massive time-series datasets, reducing costs by 80% while training hundreds of models efficiently.
AI Value for Investors and Key Factors Attracting Investment in AI Companies
Explore AI investment strategies, evaluate AI pitches, and discover key factors attracting investors to AI companies. Gain insights from industry experts on assessing AI value and potential.
Docker Based Workflow for Deploying a Machine Learning Model
Explore Docker's role in ML development, from container basics to full deployment, focusing on reproducibility, portability, and ease of implementation.
Human-Machine Collaboration Approach to Deep Learning Development
Explore human-machine collaboration in deep learning development with insights from AI expert Alexander Wong on computational imaging and computer vision.
MLOps That Works: Building ML Pipelines for Autonomous Factory Models
Explore MLOps strategies for autonomous factories. Learn design patterns and anti-patterns for deploying ML models in dynamic industrial environments using flexible pipelines.
The Do's and Don'ts of Delivering AI Projects: A Practitioner's Guide
Discover strategies for successful AI project delivery, including evaluation tools and techniques to overcome common challenges in this rapidly evolving field.
COVID-Net for COVID Detection and Risk Stratification via Chest Radiography
Explore COVID-Net's neural network for detecting and assessing COVID-19 risk through chest X-rays, including its rapid development and operational benefits.
DevOps for ML and Other Half-Truths - Processes and Tools for the ML Lifecycle
Explore processes and tools for enterprise-ready ML deployment, addressing the challenges of standardization in the ML lifecycle and supporting IT operations management.
Distributed Training and Gradient Compression - Lecture 14
Explore distributed training challenges, focusing on bandwidth and latency bottlenecks. Learn gradient compression techniques and delayed gradient averaging to optimize communication in deep learning systems.
Distributed Training and Gradient Compression - Lecture 14
Explore distributed training challenges, focusing on bandwidth and latency bottlenecks. Learn gradient compression techniques and delayed gradient averaging to optimize communication in deep learning.
Basics of Quantum Computing - Lecture 21
Explore quantum computing fundamentals, including key concepts and potential applications in computational problem-solving and information processing.
Basics of Quantum Computing - Lecture 21
Explore quantum computing fundamentals, including key concepts and potential applications in computational problem-solving and information processing.
AI Model Efficiency Toolkit (AIMET) - Lecture 25
Explore AI model efficiency techniques with Qualcomm's toolkit. Learn about adaptive rounding, autocon, and transformer quantization for optimized inference and training on resource-constrained devices.
AI Model Efficiency Toolkit (AIMET) - Lecture 25
Explore AI model efficiency techniques with Qualcomm AI Research. Learn about AIMET, community research, core technologies, and industry applications for optimizing deep learning models.
Distributed Training: Techniques and Strategies - Part II - Lecture 18
Explore advanced distributed training techniques for machine learning, focusing on efficiency and scalability in large-scale AI systems.
Distributed Training for Efficient Machine Learning - Part II - Lecture 18
Explore advanced distributed training techniques for machine learning, focusing on efficiency and scalability in large-scale systems.
EfficientML.ai - Basics of Neural Networks - Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques. Gain insights into efficient machine learning concepts and applications.
Basics of Neural Networks - EfficientML.ai Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.
Pruning and Sparsity in Machine Learning - Part I
Explore pruning and sparsity techniques in machine learning, focusing on improving model efficiency without sacrificing performance. Learn key concepts and practical applications.