Low Latency Neural Network Inference for ML Ranking Applications - Yelp Case Study
Explore Yelp's ML platform overhaul for low-latency neural network inference in ranking applications. Learn about Tensorflow model training, deployment using MLEAP, and integration with Nrtsearch for improved performance.
5 Techniques to Increase Adoption Rates via Interpretability
Discover 5 techniques to boost ML project adoption through enhanced model interpretability, fostering trust and bridging gaps between end-users and data scientists.
MLOps for Fairness - Creating Comprehensive Fairness Workflows
Explore MLOps techniques for identifying, measuring, and mitigating bias in ML systems at scale. Learn to create comprehensive fairness workflows using Python tools for industry-level challenges.
MLOps with Dataiku - Considerations for Model Deployment and Monitoring
Explore key MLOps concepts, deployment readiness, and model monitoring strategies. Learn how Dataiku streamlines MLOps processes for efficient AI industrialization.
How to Treat Your Data Platform Like a Product - 5 Key Best Practices
Discover 5 key best practices for treating your data platform as a product, ensuring reliability and scalability for maximum business value.
Accelerating Deployment of AI with MLOps Orchestration
Explore MLOps orchestration to simplify AI deployment using Kubernetes and open-source tools. Learn to build efficient ML pipelines, from data preparation to model monitoring, accelerating business value across various use cases.
Implementing SecMLOps at Every Stage of the ML Pipeline
Implement SecMLOps at every stage of the ML pipeline to address security challenges, comply with data privacy laws, and ensure robust ML system architecture.
Deploying and Managing Machine Learning Models at Scale - A Hands-On Workshop
Deploy and manage ML models at scale. Hands-on workshop covers creation, deployment, monitoring, and management using real-life scenarios. Gain practical experience with Seldon Deploy, explainers, and outlier detection.
Critical Use of MLOps in Finance Using Cloud-Managed ML Services That Scale
Leverage MLOps to improve quality and consistency of machine learning solutions in finance. Accelerate model experimentation, deployment, and tracking using cloud-managed services for efficient scaling.
Personalized Recommendations and Search with Retrieval and Ranking at Scale - Hopsworks Workshop
Implement personalized recommendations using retrieval and ranking architecture with two-tower embedding models. Learn offline and online infrastructure for scalable, low-latency systems on Hopsworks platform.
Testing Recommender Systems in the Wild - MLOps World
Explore behavioral testing for recommender systems using RecList. Scale up real-world testing in research and production with hands-on coding and practical examples.
Managing Human in the Loop Systems Without Burning Out Your Engineers
Explore strategies for managing human-in-the-loop systems efficiently, focusing on Pinterest's approach to labeling, training, and measuring shopping content quality with minimal engineering effort.
Automated Machine Learning Tuning with FLAML
Explore automated machine learning and tuning using FLAML, a fast Python library. Learn to automate ML tasks, perform general tuning, and conduct zero-shot AutoML efficiently.
MLOps Beyond Training - Simplifying and Automating the Operational Pipeline
Explore a production-first approach to MLOps, automating operational pipelines for measurable ROI. Learn to design continuous workflows, integrate components, and simplify the transition from research to scalable production.
Top 5 Lessons Learned in Helping Organizations Adopt MLOps Practices
Discover key insights and strategies for implementing MLOps practices at scale. Learn common challenges and effective mitigation techniques for successful organizational adoption.
Implementing MLOps Practices on AWS using Amazon SageMaker
Implement ML pipelines with CI/CD practices using SageMaker Pipelines. Gain hands-on experience in MLOps, incorporating DevOps principles into machine learning workflows for scalable and efficient model deployment.
Defending Against Decision Degradation with Full Spectrum Model Monitoring - Case Study and AMA
Explore Lyft's comprehensive ML model monitoring system, preventing degradation in high-stakes decisions for pricing, safety, and fraud detection. Learn about real-time validation and cultural shifts in ML practices.
Lessons Learned from DAG-Based Workflow Orchestration
Explore DAG-based workflow orchestration challenges and learn about Prefect Orion's DAG-less system for enhanced runtime flexibility and developer experience in data pipelines.
A Guide for Startups: Scaling a PoC to Production System Without Going Up in Smoke
Discover best practices for scaling AI proof-of-concepts to production systems. Learn to avoid common pitfalls, assess feasibility and ROI, and build a framework for successful implementation in startups.
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.
Understanding Foundation Models: A New Paradigm for Building and Productizing AI Systems
Explore foundation models as a new AI paradigm. Learn practical ways to leverage these models in building and productizing AI systems for your company.