Introducing Jules S. Damji > @2twitme < presenting “MLflow: Infrastructure for a Complete Machine Learning Life Cycle”.

Jules S. Damji is an Apache Spark Community and Developer Advocate at Databricks. He is a hands-on developer with over 20 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science and MA in Political Advocacy and Communication from Oregon State University, Cal State, and Johns Hopkins University respectively.

He’s also the Program Chair for Spark + AI Summit

ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.

In this session, we introduce MLflow (mlflow.org), a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.

With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics

Come check out Jules S. Damji’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Introducing Siyu Yang > @yangsiyu_ < presenting “Deep Learning for Computer Vision with Applications for Wildlife Conservation”.

Siyu is a data scientist at the AI for Earth program at Microsoft focusing on developing machine learning enabled solutions to aid environmental conservation efforts at university labs, startups, non-profits, and government agencies. Before joining this team, she worked on data powered code completion tools at Visual Studio.

At the AI for Earth program at Microsoft, we find new ways to use machine learning to solve global environmental challenges. This talk is an overview of several problems in wildlife conservation where computer vision is poised to make a significant impact. We will survey three different problems in this space: species classification from handheld photos, automated camera trap image processing, and animal detection from aerial imagery. For each area, we will discuss (1) why this problem is challenging from a computer vision perspective, (2) why solving this problem can support conservation, and (3) the data sets we’re working with and the models we’ve built to address this problem. The talk will also include a demo of the AI for Earth Species Classification API and the Camera Trap Animal Detection API.

What will attendees learn:

1. Find out what Microsoft’s AI for Earth program is all about
2. Understand how data, particularly visual data, is used by wildlife biologists for conservation.
3. Experience through live demos how computer vision techniques can bring value to people working with such data, and understand our plan to deploy these technologies.

Come check out Siyu Yang’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!