Orchestrating Secure Development Lifecycles in the Cloud
Explore cutting-edge approaches to orchestrate Secure Development Lifecycles in the Cloud, addressing challenges and implementing automation for efficient scaling.
Challenges in Perinatal Computational Neuroimaging in the In Vivo and Ex Vivo Domains
Explore cutting-edge neuroimaging solutions for human brain development, focusing on algorithm development and multimodal imaging techniques for infants and postmortem tissue.
Brave New World: How Cardiac Imaging Will Thrive With AI - Grand Rounds 2.23.23
Explore how artificial intelligence revolutionizes cardiac imaging, enhancing diagnostic capabilities and patient care in cardiovascular medicine.
Parallelizing Your ETL with Dask on Kubeflow
Leverage Dask's parallelism capabilities on Kubeflow for advanced ETL processing. Learn to use the Dask Operator to optimize resource utilization in interactive Jupyter sessions and pipeline workflows.
Deploying Efficient Data-Centric AI 100x Faster
Accelerate AI application development and deployment 100x using dedicated platforms, data labeling, pre-built models, and AI-automation. Explore region classification and deep-trained object detection approaches.
The Critical Missing Component in the Production ML Stack - Data Logging for AI Applications
Discover how to enable data logging for AI applications, enhancing testing, monitoring, and debugging. Learn best practices to supercharge MLOps in your team.
End-to-End MLOps for Computer Vision
Explore end-to-end MLOps for Computer Vision, from data gathering to model monitoring. Learn to implement ML pipelines using tools like Amazon SageMaker, Kubernetes, Kubeflow, and MLFlow.
Build High Performance MLOps With ML Monitoring and AI Explainability
Discover techniques for enhancing MLOps through ML monitoring and AI explainability. Learn to identify model failures, address operational issues, and streamline model deployment using advanced analytics.
Shopping Recommendations at Pinterest
Explore Pinterest's shopping recommendation systems, from candidate generation to serving methods for diverse recommenders and neural network ranking models.
The Ops in MLOps: Creating Cost-Effective and Compliant ML Environments
Explore techniques for creating cost-effective, secure, and compliant ML environments with proper data governance in MLOps.
Deploy High Scale ML Models Without the Hustle
Learn to build and operate production-grade ML systems, from model training to deployment and monitoring, using the Qwak platform for efficient, high-scale solutions.
Deploying Model Artifacts in Hard Mode
Automate and streamline complex model deployment processes across multiple products. Learn strategies for overcoming documentation gaps, ambiguous terminology, and legacy systems in a collaborative remote environment.
Best Practices for Deploying MLOps Solutions in Enterprise
Explore best practices for deploying MLOps solutions in enterprise environments. Learn from industry experts about common pitfalls, avoidance strategies, and early adoption techniques for model lifecycle management.
Intro to MLOps: Build, Deploy, and Retrain a Model in Production
Build, deploy, and retrain a production ML model using open-source tools. Learn robust MLOps pipeline techniques for quick model deployment and continuous improvement.
Building an ML Platform from Scratch
Develop a customizable ML platform using open-source tools. Learn to integrate Cookiecutter, DVC, MLFlow, KFServing, and more for a flexible solution tailored to your organization's needs.
Automatic ML Model Containerization: Best Practices and Tools
Explore best practices for containerizing ML models for production inference. Learn about secure, multi-tenant image builds, avoiding lock-in, and tools like chassis.ml and Open Model Interface for seamless integration.
A Zero Downtime Setup for Models: How and Why
Discover techniques for achieving zero-downtime model updates and handling failures in production environments. Learn a robust setup to ensure continuous availability and reliability of deployed models.
Using Reproducible Experiments to Create Better Machine Learning Models
Enhance ML model efficiency through reproducible experiments. Learn to track hyperparameter, code, and data changes using DVC for grid and random search tuning methods.
Continuously Improving Large Scale Recommenders with MLOps Tools and Practices
Leverage MLOps tools and practices to operationalize and continuously improve recommendation systems at scale using NVIDIA Merlin and Kubeflow on Google Kubernetes Engine.
Running ML Inference Services in Shared Hosting Environments
Optimize ML inference in shared hosting: Learn to boost latency, throughput, and resource utilization through queue management and OpenMP tuning.
Machine Learning for Entertainment - Challenges and Opportunities
Explore challenges and solutions in ML for streaming services, including personalization, content understanding, and ads targeting. Learn about balancing metrics, feedback loops, and algorithmic fairness.