Organization and Processes Archives | Tecton

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Engineering for Applied ML

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 domains. This talk is about my learnings in building those systems and patterns that I’ve found to work well across applications. … Read More

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 learning at Wikimedia, we started implementing model cards for every model hosted by the Foundation. This talk is a description of our process, motivation, and lessons learned along the way. … Read More

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 come together to share their learnings from speaking with hundreds of leading MLOps teams. They’ll discuss their insights from identifying common patterns between these teams. … Read More

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 learning and data-related lessons and challenges we’ve encountered over the last 5 years on Faire’s journey to empowering entrepreneurs to chase their dreams. … Read More

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 redeploying models, and the failure modes of not doing so. We will discuss approaches to make ML deployment easier, faster and safer which allowed our team to spend more time improving models, and less time shipping them. … Read More

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 applications using existing tools and platforms. Although hundreds of new MLOps products have emerged in the past few years, data scientists and ML engineers are still struggling to develop, deploy, and maintain models and systems. In fact, iteration speeds for ML teams may be slowing! In this talk, Sarah Catanzaro, a General Partner at Amplify Partners, will discuss a dominant design for the ML stack, consider why this design inhibits effective model lifecycle management, and identify opportunities to resolve the key challenges that ML practitioners face. … Read More

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 for 1 model in web search ranking, 100s of models for Sybil and 1000s of models for TFX. You’ll see why I’m convinced that data and software engineering are critical for successful data science – more so than models. Regardless of your experience, I’ll share some tips that will help you overcome the hard parts of machine learning. … Read More

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 collaboratively as a single team. This requires adaptation on both sides – combining data science and engineering processes into a well-integrated MLOps machine. Our panel of data scientists will provide their perspective on how data engineers can support this transition and more effectively work with data science teams. … Read More

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) poses some additional challenges that we are still struggling with today. In addition to code, data and models also abide by the continuous principles (Continuous Integration, Delivery and Training). At Volvo Cars, we are embracing a git-centric, declarative approach to ML experimentation and delivery. The adoption of MLOps principles requires cultural transformation alongside supportive infrastructure & tooling that enables efficient development throughout the ML lifecycle. Join us for this session to learn about how Volvo Cars embraces MLOps. … Read More

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