Serverless Inference; and A New Stock Prediction Architecture

Thanks to INNOQ for sponsoring food & drinks 🙏🍻
And, thanks to Jose Quesada for hosting & MC'ing this event! 🏋️‍♀️

Talk 1: Running inference as a serverless cloud function (45 min)

Speaker: Michael Perlin, innoq

Abstract: Abstract: When deploying your model into production, you have to care about configuring and maintaining the runtime environment, scaling, monitoring and more – all tasks, which are more related to DevOps than to ML. In some contexts, you can achieve your goals in a much simpler way by establishing a "serverless" approach. We’ll take a look at the cloud services "AWS Lambda", "Google Cloud Functions", and "Azure Functions" and show how they enable running ML inference.

Bio: Michael Perlin is Senior Consultant at INNOQ. Since more than fifteen years he has been working on multiple topics around Software Development, DevOps and Machine Learning.

Talk 2: The Architecture of a Stock Prediction System

Speakers: Stefan Savev & Rey Farhan

Abstract:
In this talk, we will share our experience of building a stock prediction system based on the recently released Deutsche Börse Public Dataset (https://registry.opendata.aws/deutsche-boerse-pds/).

Architecture components include the following.

1. achieving insights about stock market behavior via the available data and validating existing predictive approaches;
2. encoding ML models with a Domain Specific Language (DSL) that targets explicitly the properties of financial data;
3. encoding trading strategies based on the results of the ML model using another DSL.

We combine approaches from data science, engineering, and stock market traders. We believe is a rare open source attempt to use ML for stock prediction in combination with a strategy and which evaluates the predictions reliably.

Code:
https://github.com/Originate/dbg-pds-tensorflow-demo https://github.com/Originate/dbg-pds-tensorflow-demo/blob/ss-add-strategy-image/ROADMAP.md.