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.
Testing in Production - PowerShell Summit 2023
Explore testing in production strategies with Talia Nassi's PowerShell Summit 2023 talk, enhancing software reliability and deployment confidence.
Insights from Building Enterprise-Grade ML Experiences at Asana
Discover key learnings from Asana's journey in developing and deploying machine learning features for millions of users, covering modeling, monitoring, scaling, and leveraging Databricks for data science solutions.
Effective Android Development
Explore techniques, tools, and processes for building high-quality Android apps, from prototyping to production. Learn about agile development, continuous delivery, automated testing, and effective collaboration.
Feature Flags - The Toggle, the A-B Test and the Canary
Explore feature flags for DevOps: separating deployments from releases, canary releases, A/B testing, and more. Learn to control deployments using open-source libraries and SaaS products.
Optimizing Search and Discovery Systems - Machine Learning Challenges and Solutions
Discover how Depop's search and ranking team tackles complex machine learning challenges while maintaining model reliability and enabling rapid feature development in a marketplace environment.
How to Set Up an Experimentation Process for Data Science
Master the fundamentals of effective business experimentation, from hypothesis formation and metric selection to experiment design, group splitting, and results analysis for data-driven decision making.
DevEx and Productivity Metrics on Backstage - Best Practices and Common Pitfalls
Discover best practices for implementing developer productivity metrics in Backstage, avoiding common pitfalls while maximizing value through benchmarking, contextual data analysis, and effective metric visualization.
Experimentation Programs at Scale - Lessons Learned from Top Companies
Discover proven strategies for scaling experimentation programs across organizations, from implementing feature flags to maintaining quality results while increasing experiment frequency and driving data-driven product development.