UPDATED: Video Added from the Conference!

Introducing Hannes > @hanneshapke < presenting “MLOps for non-DevOps folks, a.k.a. “I have a model, now what?!”“.

Machine learning has been fascinating to Hannes for many years. He is currently the VP of Engineering and AI at Caravel, an AI-powered shopping platform built for eCommerce stores. Before joining Caravel, Hannes was a Senior Data Engineer at Cambia Health Solutions and a Machine Learning Engineer at Talentpair, Inc. He is a Google Developer Expert for Machine Learning. At the moment, Hannes is working on his second machine learning publication with O’Reilly Media focusing on Machine Learning Workflows.

The prospect of developing and training machine learning models on datasets is exciting. While many conference attendees understand the potential value of deploying machine learning models and may even have a model in mind, not all are aware of the frameworks and tools to needed release them in the real world.

This talk will present an applicable framework to bring attendees’ machine learning models to life. Hannes will introduce the audience to tools including Tensorflow Serving and Google ML Engine and discuss them in the context of other popular serving applications such as Kubeflow, AWS Sagemaker, and Azure Machine Learning. He’ll guide the audience through a sample (live) deployment and highlight things to watch out for, such as scaling and costs, as well as the pitfalls of model versioning.

Attendees will leave the talk familiarized with popular approaches to machine learning model deployment, as well as an actual framework they can use to deploy their models for the greater good for the world.

Come check out Hannes’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!

UPDATED: Video Added from the Conference!

Introducing Wale Akinfaderin > @WaleAkinfaderin < presenting “The Creative Art of Engineering Bespoke Features”.

Wale Akinfaderin is a Data Scientist at Lowe’s Company, Inc with expertise in machine learning, deep learning, statistical experimentation and general information theory. He has broad experience in implementing and extending ML techniques to solve practical and business problems.

In the past, he has built a data veracity model using machine learning and natural language processing techniques for international development with IBM Research and he was a 2018 Insight Data Science Fellow. He is an advocate of open source and democratizing learning and his writings on data science and machine learning have been featured on Hacker News, Data Science Central, Dataconomy, R Bloggers and IBM DSX.

He is a machine learning proposal reviewer for Cambridge University Press, the founder of Data Science Tallahassee meetup group, and a data science expert mentor for Insight2Impact which is funded by the Bill and Melinda Gates Foundation. He also advises startups in the data science and cognitive computing space.

Feature engineering is rudimentary to applying machine learning models to raw and messy data. From multiple imputations for missing data to filtering in feature selection for issues like information redundancy, crafting bespoke features can be used to understand the underlying patterns in a dataset, facilitate the machine learning processing pipeline and improve model evaluation metrics.

In this talk, we will explore feature extraction and representation techniques for numeric, categorical and spatio-temporal features. We’ll also highlight other advance feature engineering methods like non-linear encoding for linear algorithms, topological filters, data leakage and best practices.

Come check out Wale Akinfaderin’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!

UPDATED: Video Added from the Conference!

Introducing Dr. Sarah Kaiser > @Crazy4pi314 < presenting “What is Quantum Machine Learning, and Is It A Thing?”.

Dr. Sarah Kaiser is a Research Engineer at Pensar Develop working on developing cutting edge consumer technology. She has a PhD in Physics specializing in quantum information where she melted things with lasers and prototyped quantum encryption satellites. In her spare time she writes books about engineering and machine learning for babies, and tries not to laser cut all the things.

In the rush to add the word quantum to everything from batteries to banking, Quantum machine learning has entered the fray. A perfect combination of buzzwords that will get all of the funding….right? In this talk I want to look at: quantum computers are and how you program one. With this context, we can look at what machine learning tasks are being explored for possible speedups with quantum computing.

Come check out Dr Sarah Kaiser’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!

But wait, that’s not all, Sarah has also written a few books with Chris Ferrie that I wanted to give her a shout out for! Robotics for Babies, Neural Networks for Babies, and ABCs of Engineering.

 

 

UPDATED: Video Added from the Conference!

Introducing Catherine Nelson > @DrCatNelson < presenting “Practical Privacy in Machine Learning Systems”.

Catherine Nelson is a Senior Data Scientist for Concur Labs at SAP Concur, where she explores innovative ways to use machine learning to improve the experience of a business traveller. She is particularly interested in privacy-preserving ML and applying deep learning to enterprise data. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University.

Data privacy is a huge topic right now for any business using personal data. People are questioning whether they should allow companies to collect data about them, and they are asking about what happens to that data after they hand it over. Machine learning systems often depend on data collected from their users to make accurate predictions. But can we build cool products powered by machine learning while still providing privacy for our users?

To start with, privacy is not a binary choice. There are many options available to developers for adding privacy to ML. In this talk, I’ll discuss some practical options, from simple methods to differential privacy, and show some examples of how we can incorporate these into real products. I’ll show how we’ve built a Data Washing Machine that allows us to provide nuanced levels of privacy via a simple API. I’ll also explore the tradeoff between user privacy and model accuracy, and show how we can still make accurate predictions with our models even when privacy is increased.

Come check out Catherine Nelson’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!