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Video Directory

MLOps World 2022 Conference Talks

Transformative Power of ML in the Real World
Don't Fear Compliance Requirements & Audits Implementing SecMLOps at Every Stage of the Pipeline
One Cluster to Rule Them All ML on the Cloud Using Ray on Kubernetes and AWS
How MLOps Tools Will Need to Adapt to Responsible and Ethical Al Stay Ahead of the Curve
Low latency Neural Network Inference for ML Ranking Applications Yelp Case Study
Eliminating Al Risk, One Model Failure at a Time
Top 5 Lessons Learned in Helping Organizations Adopt MLOps Practices
Scotiabank's Path Towards Accelerated Analytics Through GCP
Robustness and Security for Al and the Dangerous Dismissal of Edge Cases
A Framework for a Successful Continuous Training Strategy
How to Conquer Data Drift & Prevent Stale Models in Production using DVC
Managing Human in the Loop Systems Without Burning Out Your Engineers
SLA Aware Machine Learning Inference Serving on Serverless Computing Platforms
Wild Wild Tests: Testing Recommender Systems in the Wild
Feature Engineering Made Simple
Machine Learning Infrastructure at Meta Scale
The Future of Feature Stores
MLOps for Deep Learning
Shopify's ML Platform Journey Using Open Source Tools Case study building Merlin & AMA
MLOps for Fairness: Creating Comprehensive Fairness Workflows
Managing a Data Science Team During the Great Resignation
Cutting-edge NLP, Large Language Models, and Their Implications For Products and Research
The Critical Things You Have to Build to Transform Your Company to be ML Driven
Supporting Sales Forecasting at Scale for Canada's Largest Grocery Store
Panel: What Every Product Manager Delivering Al Solutions Should Know
What Do Engineers Not Get About Working with Data Scientists
MLOps at Rovio for Personalization Self Service Reinforcement Learning in Production
A Guide for Start ups; How to Scale a PoC to Production System and Not Go Up in Smoke
CyclOps - A framework for Data Extraction, Model Evaluation and Drift Detection for Clinical Use
A Guide to Building a Continuous MLOps Stack
Al in Robotics, Manufacturing, and Media How Good Practices Can Shape the Future
It's All About The Data Continuously Improve ML Models, The Data-Centric Way
Understanding Foundation Models a New Paradigm for Building and Productizing Al Systems
Hands on: A Beginner Friendly Crash Course to Kubernetes
Solving MLOps From First Principles A Framework to Reduce Complexity MLOps World Machine Learning in Production
Panel: Embedding Diversity and Fairness Into Your Model Governance
Building Production ML Monitoring from Scratch Live Coding Session
How We Reduced 83% of ML Computing Cost on 100+ ML Projects
Becoming An ML Platform Power Builder Powered by ML Observability
Introduction to Model Deployment with Ray Serve
UnionML: A Microframework for Building Machine Learning Applications
Concretes Guidelines to Improve ML Model Quality, Based on Future ISO Certifications
A GitOps Approach to Machine Learning
Building Real Time ML Features with a Feature Platform

MLOps World 2022 Virtual Workshops

Supercharging MLOps with the Petuum Platform
Scaling ML Embedding Models to Serve a Billion Queries
MLOps is Just HPC in Disguise A Real World, No Nonsense Guide to Upgrading Your Workflow
Critical Use of MLOps in Finance Using Cloud-Managed ML Services That Scale
Building Real-Time ML Features with Feast, Spark, Redis and Kafka
Trustworthy Al for MLOps
What's in the Box: Automatic ML Model Containerization
WarpDrive: Orders of Magnitude Faster Multi-Agent Deep RL on a GPU
Taking MLOps 0-60: How to Version Control, Unify Data and Manage Code Lifecycles
Scale and Accelerate the Distributed Model Training in Kubernetes Cluster
Production ML for Mission Critical Applications
Personalized Recommendations and Search with Retrieval and Ranking at scale on Hopsworks
MLOps Beyond Training: Simplifying and Automating the Operational Pipeline
Machine Learning Monitoring in Production: Lessons learned from 30+ Use Cases
Lessons Learned from DAG based Workflow Orchestration
Leaner, Greener and Faster Pytorch Inference with Quantization
How to Treat Your Data Platform Like a Product 5 Key Best Practices
How to MLEM Your Models to Production
Generalizing Diversity Machine Learning Operationalization for Pharma Research
Deploying and Managing Machine Learning Models at Scale: A Hands-On Workshop with Seldon
Defending Against Decision Degradation with Full Spectrum Model Monitoring Case Study and AMA
Automated Machine Learning Tuning with FLAML
Accelerating Transformers with Hugging Face Optimum and Infinity
A Zero Downtime Set up for Models: How and Why
Parallelizing Your ETL with Dask on Kubeflow
Table of Contents