Nebraska.Code() Sessions tagged machine learning

Making Machine Learning More Effective By Applying Agile Practices via MLOps

You've decided Machine Learning (ML) can help your customers? Great! But ML can be difficult, time consuming, and expensive. Agile practices can reduce these costs and improve outcomes in the same way they've always helped: by making sure we're building the right thing every step of the way.

We've identified, and our presentation will speak to, the following anti-patterns for machine learning and agile approaches that avoid these anti-patterns. We value: * Iterative over incremental * Always be releasable over "toss it over the wall" at the end * ML as a means over ML as the end goal

For each anti-pattern, we will present several techniques to mitigate it.

Avoiding Incremental Development In Favor Of Iteration * Find 'minimal learnable concept' and get a complete ML pipeline quickly * Improve your ML pipeline from data preparation to deployment, don't waterfall it * Realize when something isn't feasible early on to avoid wasted effort

Integrate ML In The Product From The Start, Not At The End * Avoid answering the wrong question by focusing on outcomes and stories * Prevent overwork by only doing what's needed to get the desired result * Experts may identify easier-to-solve adjacent problems that have comparable outcomes for users

ML is a feature like any other - it is a means, not an end * Identify when it is not the right tool for the job * Know when good enough is good enough * ML stories should be prioritized, have story points, etc.

Speaker

Robert Herbig

Robert Herbig

Lead Software Engineer, SEP

Intro to Deep Learning Neural Networks

Deep Learning is a subset of Machine Learning where large and architecturally complex neural networks have been created for specific applications. This presentation will cover neural networks and three types of deep learning networks that are commonly used, in a less technical and more abstract way.

After the presentation, you should feel comfortable discussing the basics of neural networks and be aware that changing the architecture of a neural network can increase it's efficacy. Networks discussed: Convolutional neural networks, Recurrent neural networks, reinforcement learning neuralNetworkZoo

Speaker

Chris Powell

Chris Powell

Software Engineer, Hudl

Creating a deep learning model to predict college basketball scores

Are you interested in how some of the leading gamblers are using machine learning? Are you interested in deep learning and want to see it applied? Are you interested to see how certain stats line up with actual results? If so, this talk is for you.

The talk will start with an overview of data collection, it will then go into how I used Google Colab to train and test my models, finally, it will show how to deploy the model to the masses through an Android application or a web site.

Speaker

Evan Hennis

Evan Hennis

Software Engineer

Anonymous Insights at the Edge

Commercial real estate is responsible for 30% of the world's carbon emissions. There’s 40 billion sqft of unused commercial real estate. A combination of bouncing lasers (and radar) off of people and machine learning on customer hardware is being used to solve this problem - ensuring that realestate is being properly utilized. We take it a step farther, and maintain anonymity while we're at it.

This talk is a deep dive into how Density processes 250,000 sensor readings every second (counting 1 million people daily) to inform actionable insights for Fortune 5000 companies.

Speakers

Ryan Versaw

Ryan Versaw

Data Science Engineering Manager, Density