UPDATED: Video Added from the Conference!

Introducing Damon Danieli > @damondanieli < presenting “Time-Series Behavioral Analysis for Churn Prediction“.

Vice President of Engineering for Platform Architecture at DocuSign. I am responsible for the internal advanced analytics data platform as well as applied machine learning for both internal efficiency for sales, marketing and customer success and external intelligent features. Big things are coming!

Previously…

Founder and CTO of Appuri [Acquired by DocuSign in December 2017]. Appuri is a Customer Data Platform that enables Software as a Service (SaaS) businesses to maximize their retention, engagement and revenue. Appuri captures and connects behavioral data from your website and mobile applications and combines it with other sources such as transactional data and 3rd party demographic augmentation.

This talk covers the data engineering, feature extraction, and the processing for predicting future user behavior. By the end of the presentation, you should have a direction to explore if you are building your own system as well as some concrete patterns that we found worked for us.

We will use real-world examples for B2B SaaS churn prediction, but this talk is equally applicable to predicting any type of outcome that is correlated with user behavior such as conversion to a paying customer, upsell to additional products, etc.

We will present lessons learned from several iterations of productizing a system that can take product usage user telemetry events (from Mixpanel, Amplitude, Heap, homegrown, etc) and combine that with business objects (Salesforce) and the application database. We will include some mistakes we’ve made along the way.

Come check out Damon Danieli’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 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.