Online: Pitfalls and Challenges of ML-Powered Applications

Data Works MD
Data Works MD
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Online event

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Building machine learning applications can be complex. Choosing the right ML approach for a given feature, analyzing model errors and data quality issues, and validating model results to guarantee product quality are all challenging problems that are at the core of the ML building process. Join us in August to learn about some of the pitfalls in deploying ML applications.


12:00 PM -- Greetings
12:05 PM -- Pitfalls and Challenges of ML-Powered Applications - Emmanuel Ameisen

Zoom and YouTube Streaming
A link will be sent out prior to the event. Please note that Zoom is capped at 100, so if you do not get into the Zoom, you will be able to watch via YouTube.

Pitfalls and Challenges of ML-Powered Applications
As a field, we often hear about success stories. This is true in research, where a publishing incentive can pressure authors to focus on consistently exceeding state of the art results. It is also true in industry, where companies attempt to attract engineering talent by describing how impressive their production ML systems are.

However, every practitioner here knows that in engineering and in ML, the road to success is paved with failures. The field of ML in production is new, and so has a lack of cautionary tales of things that can go wrong with models. This talk will try to help correct that.

We will discuss challenges such as performance mismatch between offline training and online inference, feature generation and data leakage, and adequate roadmap planning for ML.

Emmanuel Ameisen is a machine learning engineer at Stripe. He is the author of the book "Building Machine Learning Powered Applications" Previously, he led Insight Data Science’s AI program, directing more than a hundred machine learning projects. Before that, he implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France’s top schools. Follow on Twitter @mlpowered.

Building Machine Learning Powered Applications: Going from Idea to Product
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.