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.
A Gentle Introduction to Sparsity with a Concrete Example
Explore sparsification techniques for ML models, including popular methods and a concrete example to understand their benefits.
Scaling ML Embedding Models to Serve a Billion Queries
Learn to scale ML embedding models for visual search, exploring eBay's architecture, workflows, and solutions to handle billions of queries efficiently.
Taking MLOps 0-60: How to Version Control, Unify Data and Manage Code Lifecycles
Explore MLOps fundamentals, focusing on version control, data unification, and code lifecycle management for reliable and adaptable machine learning systems.
Supercharging MLOps with the Petuum Platform
Explore innovations in Composable, Automatic, and Scalable ML (CASL) and their role in transforming MLOps into a repeatable assembly-line process using the Petuum Platform.
SLA-Aware Machine Learning Inference Serving on Serverless Computing Platforms
Explore adaptive batching for efficient ML inference serving on serverless platforms. Learn to optimize costs and ensure SLA compliance using MLProxy for improved performance.
Shopify's Machine Learning Platform Journey: Building Merlin with Open Source Tools
Explore Shopify's journey in building Merlin, an open-source ML platform. Gain insights into the architecture, technologies, and tools used to scale machine learning work effectively.
Trustworthy AI for MLOps
Explore strategies for implementing trustworthy AI in MLOps, addressing operational challenges, and fostering transparency to turn AI into a business advantage.
Scale and Accelerate Distributed Model Training in Kubernetes Clusters
Orchestrate and accelerate distributed deep learning workloads across multiple GPUs and nodes using Kubernetes, Kubeflow PytorchJob, RDMA, RoCE, and SRIOV for near-linear performance scaling.
How MLOps Tools Will Need to Adapt to Responsible and Ethical AI - Stay Ahead of the Curve
Explore ethical AI guardrails for MLOps platforms, focusing on GDPR compliance, data privacy, and future regulatory requirements. Learn to implement responsible AI practices in machine learning operations.
A Framework for a Successful Continuous Training Strategy in MLOps
Develop a robust continuous training strategy for ML models in dynamic data environments, addressing concept drift and optimizing retraining approaches.
MLOps for Deep Learning: Drift Detection and Efficient Retraining in Model Serving
Explore advanced MLOps techniques for deep learning, including drift detection, efficient retraining, and continuous deployment. Enhance model reliability and performance in production environments.
Feature Engineering Made Simple
Master feature engineering techniques to streamline your machine learning workflow. Explore systematic approaches using Anovos, an open-source library for efficient data processing and model building.
MLOps is Just HPC in Disguise - A Real-World, No-Nonsense Guide to Upgrading Your Workflow
Upgrade your ML workflow with efficient tools and techniques for automating tasks, managing resources, and streamlining production deployment in this practical guide to MLOps.
Generalizing Diversity - Machine Learning Operationalization for Pharma Research
Explore ML operationalization in pharma research, addressing challenges of transitioning from one-off to repeated model usage in critical workflows. Learn strategies for leveraging MLOps platforms in enterprise settings.
ML Observability: A Critical Piece in the ML Stack
Explore ML Observability's role in addressing model failures, monitoring performance, and improving models. Learn industry best practices for enhancing your ML stack's reliability and efficiency.
Reproducible Machine Learning - How Not to Repeat the Past
Explore techniques for organizing and reproducing deep learning experiments, including standardized logging, versioning, and templated workflows. Enhance collaboration and minimize duplication in ML projects.
Automated Monitoring in Production for Continuous Model Improvements
Implement automated monitoring for ML models in production. Continuously evaluate performance, visualize metrics, detect degradation, and correlate issues with input data for quick debugging and improvement.
Human Centric MLOps - Why Productivity Matters the Most
Explore human-centric MLOps and its impact on productivity. Learn how focusing on people, not just systems, can revolutionize machine learning operations and drive innovation.
Building Real Time ML Pipelines with a Feature Store
Explore real-time ML pipeline construction using feature stores. Learn to optimize compute efficiency and enhance ML workflows for production environments.