Applied ML consists of ML algorithms at its core and engineering systems around it. For over a decade as an applied ML practitioner, I have built a number of such engineering systems to help unlock the full potential of ML in a variety of problem …
More ethical machine learning using model cards at Wikimedia
First proposed by Mitchell et al. in 2018, model cards are a form of transparent reporting of machine learning models, their uses, and performance for public audiences. As part of a broader effort to strengthen our ethical approaches to machine …
Panel: Common Patterns of the World’s Most Successful ML Teams
There’s a lot we can learn simply by observing the most successful ML teams in the world: how they operate, which technology stack they use, which skill sets they value, and which processes they implement. In this panel, MLOps thought leaders will …
Empowering Small Businesses with the Power of Tech, Data, and Machine Learning
Data and machine learning shape Faire’s marketplace – and as a company that serves small business owners, our primary goal is to increase sales for both brands and retailers using our platform. During this session, we’ll discuss the machine …
Accelerating Model Deployment Velocity
All ML teams need to be able to translate offline gains to online performance. Deploying ML models to production is hard. Making sure that those models stay fresh and performant can be even harder. In this talk, we will cover the value of regularly …
How to Draw an Owl and Build Effective ML Stacks
They’re handing us an engine, transmission, breaks, and chassis and asking us to build a fast, safe, and reliable car,” a data scientist at a recently IPO’ed tech company opined, while describing the challenges he faces in delivering ML …
Why is Machine Learning Hard?
Each of us has a different answer for “why is machine learning so hard.” And how long you have been working on ML will drastically influence your answer. I’ll share what I learned over the past 20 years, implementing everything from scratch …
Panel: What Do Engineers Not Get About Working with Data Scientists?
ML is increasingly making its way into production to power customer-facing applications and business processes. This transition from batch to operational ML raises new organizational challenges. Data scientists and engineers now have to work …
Effective ML System Development
In order to efficiently deliver and maintain ML systems, the adoption of MLOps practices is a must. In recent times, the ML community has embraced and modified ideas originating from software engineering with reasonable success. Software 2.0 (AI/ML) …
ML Projects Aren’t An Island
We’ve all seen the dismal and (at this point, annoying) charts and graphs of ‘>90.x% of ML projects fail’ used as marketing ploys by various companies. What this largely simplified view of ML project success rates buries in misleading abstraction …
Data Engineering Isn’t Like Software Engineering
There’s often a push for data engineers and data scientists to adopt every pattern that software engineers use. But adopting things that are successful in one domain without understanding how it applies to another domain can lead to “cargo cult” type …
Model Calibration in the Etsy Ads Marketplace
When displaying relevant first-party ads to buyers in the Etsy marketplace, ads are ranked using a combination of outputs from ML models. The relevance of ads displayed to buyers and costs charged to sellers are highly sensitive to the output …
