- Insider's guide to Cloud AI Platform
Agenda: 5:30 - 6:15 networking 6:15 - 7:15 presentation 7:15 - 8:00 networking Presentation: Speaker: Henry Tappen Abstract: Get useful tips and tricks to machine learning training & prediction jobs a piece of cake on Google Cloud. We'll cover topics like: 1) Monitoring, debugging and logging 2) Hyperparameter tuning 3) Using dev environments like Notebooks or Colab with Cloud AI Platform 4) Structuring projects for your own sanity Bio: Henry Tappen, Product Manager, AI Platform
- Live coding a machine learning model from scratch
Agenda: 5:30 - 6:15 networking 6:15 - 7:15 presentation 7:15 - 8:00 networking Presentation: Live coding a machine learning model from scratch Speaker: Google Cloud AI/Developer Advocate Abstract: Do you want to build a machine learning model but aren’t sure where to start? Sara Robinson starts with an empty notebook and live codes a simple neural network in TensorFlow. She demonstrates how to train and serve the model on Google Cloud Platform and uses the deployed model to generate predictions from a web app. Who is this presentation for? Developers with minimal machine learning (ML) experience and ML engineers. Level: Intermediate Prerequisite knowledge: - A basic knowledge of Python - General knowledge of basic machine learning concepts: training, serving, etc. (but not necessarily how to build a model on your own) What you'll learn: - Learn how to build a simple neural network - Understand the end-to-end ML workflow and how to use your trained model for generating predictions Bio: Sara Robinson is a developer advocate on Google’s Cloud Platform team, focusing on machine learning. She helps developers build awesome apps through demos, online content, and events. Previously, she was a developer advocate on the Firebase team at Google. Sara holds a bachelor’s degree from Brandeis University. When she’s not programming, she can be found on a spin bike, listening to the Hamilton soundtrack, or eating froyo.
- Fast machine learning in SQL with BigQuery ML
Agenda: 5:30 - 6:15 networking 6:15 - 7:15 presentation 7:15 - 8:00 networking Presentation: BigQuery ML Speaker: Abhishek Kashyap, Google Product Manager/BigQuery ML Abstract: BigQuery ML enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed as well by bringing machine learning to data. In this talk we will cover BigQuery ML capabilities, and demonstrate how it can be used for a few machine learning problems like classification and clustering. Speaker Bio: Abhishek Kashyap is a Product Manager for BigQuery ML at Google. Prior to Google, Abhishek was a co-founder at MarianaIQ, an AI based marketing platform. Abhishek has worked at Pivotal, VMware, McKinsey, HP, Lucent Bell Labs, and IBM Research in the past. Abhishek holds a PhD from the University of Maryland, and Bachelors from IIT Delhi.
- Empower Google Cloud Functions with Machine Learning
Agenda: 5:30 - 6:15 networking 6:15 - 7:15 presentation 7:15 - 8:00 networking Presentation: Empowering Google Cloud Functions with Machine Learning Speaker: Hannes Hapke, Caravel AI, VP Engineering + AI GDE/ML Abstract: At Caravel, engineers are building conversational AI for eCommerce stores — work that heavily relies on serverless functions and machine learning models. Due to the limitations of serverless functions, it often doesn't make sense to load the machine learning models directly in the cloud function. In this talk, Hannes is sharing Caravel's work in integrating machine learning in Google Cloud functions. He is outlining how models are hosted on Google's AI Platform, how the serverless endpoints can interact with the model server and how TensorFlow Extended is simplifying the data preprocessing. Taking advantage of Google Cloud Functions and models hosted on the AI Platform allows Caravel to scale to ever-changing demands for their applications, but still provides the full machine learning functionality. Speaker Bio: Hannes Hapke is the VP of Engineering and AI at Caravel (caravelapp.com). Caravel's applications are built on serverless functions and a good number of machine learning models. Hannes is a Google Developer Expert and a co-author of machine learning publications like NLP in Action and upcoming O'Reilly publication on Machine Learning Workflows.
- Fast and Lean Data Science with TPUs
Agenda: 5:30 - 6:15 networking 6:15 - 7:15 first presentation: 7:15 - 8:00pm networking Presentation: Fast and Lean Data Science with TPUs Speaker: Martin Görner, Google Cloud AI, Developer Advocate Abstract: Google's Tensor Processing Units (TPUs) are revolutionizing the way data scientists work. Week-long training times are a thing of the past and many models can now be trained in minutes in a notebook. Agility and fast iterations are bringing neural networks into regular software development cycles and many developers are ramping up on machine learning. This session will introduce TPUs, then dive deep into their microarchitecture secrets. It will also show you how to use them in your day-to-day projects to iterate faster. In fact, we will not just demo but train most of the models presented in this session on stage in real time, on TPUs."
- AI as a Service (AIaaS) and AutoML
Agenda: 5:30 - 6:15 networking 6:15 - 6:35 first presentation: AIaaS and AutoML for Software Developers 6:45 - 7:15 second presentation: A code walk thru using AutoML in production code 7:15 - 8:00pm networking First Presentation: AIaaS & AutoML for Software Developers. Speaker: Torry Yang, Google Cloud AI, Developer Program Engineer Level: Fundamentals Torry will discuss Google's AI as a Service and AutoML products. These products provide the ability for software developers and organizations to utilize machine learning for computer vision and natural language processing in their applications --with otherwise little to no experience. Second Presentation: AutoML code walkthru Speaker: Andrew Ferlitsch, Google Cloud AI, Developer Program Engineer Level: Intermediate A code walk through (in Python) demonstrating a how to use AutoML (and openCV) for production code. Walk thru will cover data curation, data engineering, setting up AutoML training, and deploying the trained model.
- Kubeflow Pipelines: From rapid prototyping to production
Presenter: Soroush Radpour, Google Cloud AI - Software Engineer Topic details: Running a machine learning application in production, in a reliable and repeatable manner, is a challenge. This is because an end to end machine learning application entails a lot of phases in addition to model training: data pre-processing and validation, feature engineering, model analysis, deployment, et al. You need a system that makes it easy to compose, orchestrate and run such multi-step pipelines. However, wouldn't it be amazing if the same system also enabled rapid and reliable experimentation of ML techniques for your application. Come learn how Kubeflow enables and simplifies these dual goals. Agenda 5:45 - 6:30 PM Networking/Social/Food 6:30 - 6:35 PM Introduction 6:35 - 7:30 PM Talk and Q&A 7:30 - 8:00 PM Networking/Social/Closing