Oh Hai Ai
Oh Hai Ai is a By the Bay technology conference.
Talks & recordings
14 connected sessions
AI at Stitch Fix
I'll review applied deep learning techniques we use at Stitch Fix to understand our client's personal style. Interpretable deep learning models are not only useful to scientists, but lead to better client experiences -- no one wants to interact with a black box virtual assistant. We do this in several ways. We've extended factorization machines with variational techniques, which allows us to learn quickly by finding the most polarizing examples. And by enforcing sparsity in our models, we have RNNs and CNNs that reveal how they function. The result is a dynamic machine that learns quickly and challenges our clients' styles. Chris Moody came from a Physics background from Caltech and UCSC, and is now a scientist at Stitch Fix. He has an avid interest in NLP, has dabbled in deep learning, variational methods, and Gaussian Processes. He's contributed to the Chainer deep learning library ( http://chainer.org/ ), the super-fast Barnes-Hut version of t-SNE to scikit-learn ( http://scikit-le
AI: from an Idea to the Customer Panel
The Idea to the Customer panel takes several companies through the journeys of data-driven business, and covers many aspects of a live AI strategy touching customers. How do you think of Machine Learning and AI being a key part of your business, and how do you execute on it? What do you find along the way? Our distinguished panelists will share their own experiences.
Attention and Memory in Deep Learning Ne...
Attention and memory in Neural Networks.
BachBot: Composing Bach Chorales using D...
Can musical creativity, something believed to be deeply human, be codified into an algorithm? While most music theorists are hesitant to claim a "correct" algorithm for composing music like Bach, recent advances in machine learning and computational musicology may help us reach an answer. In this talk, we describe BachBot: an artificial intelligence which uses deep learning and long short term memory (LSTM) to compose music in the style of Bach. We train BachBot on all known Bach chorale harmonisations and carry out the largest musical Turing test to date. Our results show that the average listener can distinguish BachBot from real Bach only 5% better than random guessing, suggesting that algorithmic composition of Bach chorales is more closed (as a result of BachBot) than open a problem.
Deep Learning is Like Water (it's Everyw...
Deep Learning with H2O.ai, the most performant distributed system for Machine Learning
Deep Learning with the GPUs in Productio...
ai.bythebay.io Nov 2025, Oakland, full-stack AI conference Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: https://scale.bythebay.io. Join us! -----
Deploying and Scaling Spark ML and Tenso...
In this talk, I will train, deploy, and scale Spark ML and Tensorflow AI Models in a distributed, hybrid-cloud and on-premise production environment. I will use 100% open source tools including Tensorflow, Spark ML, Jupyter Notebook, Docker, Kubernetes, and NetflixOSS Microservices. This talk discusses the trade-offs of mutable vs. immutable model deployments, on-the-fly JVM byte-code generation, global request batching, miroservice circuit breakers, and dynamic cluster scaling - all from within a Jupyter notebook. All code and docker images are 100% open source and available from Github and DockerHub at http://pipeline.io.
Developing and Deploying Models at Scale...
Looking for a break after an endless stream of talks about Spark? We'll do a live demo of developing and deploying a predictive model using an easy-to-use, Spark-less platform. We'll show how standard cloud compute resources let you train models using terabytes of RAM, and we'll demo deploying a model and serving requests at web scale, with horizontal scalability and high availability. Benefits of the approach we'll demonstrate include: (a) flexibility to use arbitrary R and Python development without shoehorning work into Spark; (b) simpler infrastructure management by avoiding distributed computing; (c) better performance for problems that are merely "medium data" rather than legitimately "big" data.
Grand Welcome and Opening Remarks
Dr. Alexy Khrabrov, the founder and organizer of By the Bay family of conferences, opens the conference and outlines the three-day exploration of AI to follow.
IBM: Host Sponsor Welcome
IBM is the Host Sponsor of AI By the Bay, enabling cognitive computing on global scale.
Machine Learning, The Right Way
Getting started with data science and Machine Learning is a chicken and egg thing. Do I have enough data to even do data science? Which use cases should I start with? It can be intimidating. Vitaly Gordon, VP of Engineering and Data Science at Salesforce Einstein, will give you the tips you need to get a right start with the right people - at the right time. He will discuss Machine Learning technology but also tell you what data you need, and what business use cases you should start with.
Rooftop Party Overlooking the Bay: Food,...
“Rooftop Party Overlooking the Bay: Food,...” is a recorded developer talk from By the Bay, preserved in the devreal community archive.
State of AI: Chatbots, Robots & Virtual...
Artificial Intelligence is quickly moving from science fiction to science fact, and IBM Watson is not alone in offering machine learning algorithms in easy to consume REST API forms for developers to consume. But in this nascent industry it can be hard to understand the differences between different approaches along with the value of "AI." Drawing on a number of real-world examples, Michael Ludden explains how "Artificial Intelligence" (in quotes for a reason) can and is already being leveraged by developers to reduce the need for teams of data scientists, create insane new applications and services, and allow for personalized experiences that border on creepy. More importantly we'll give a realistic look at the immediate future of the space and some helpful guidance as we rapidly begin to live the futurist novels we read last year.
The Advantages of Deep Learning with Rec...
In this session we will go through several tactics for anomaly detection with sequential and non-sequential data sets. We will review the advantages and disadvantages of these tactics, analyze an overall end to end strategy for real time anomaly detection, and explore the benefits of leveraging RNN architecture as a part of this strategy.
Connections
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