Applied AI : Workshop & Tutorial Sessions

– Hands-on workshops and in-depth tutorials –

Ocotber 29th detail Schedule coming soon.

See past schedule below

ODSC Applied AI
09:30
ODSC Keynote: Our Applied AI Future
ODSC Keynote: Our Applied AI Future image
Ben Taylor, PhD
Chief AI Evangelist | DataRobot
09:30
ODSC Keynote: Our Applied AI Future
ODSC Keynote: Our Applied AI Future image
Ben Taylor, PhD
Chief AI Evangelist | DataRobot
09:30
ODSC Keynote: Our Applied AI Future
ODSC Keynote: Our Applied AI Future image
Ben Taylor, PhD
Chief AI Evangelist | DataRobot
09:30
ODSC Keynote: Our Applied AI Future
ODSC Keynote: Our Applied AI Future image
Ben Taylor, PhD
Chief AI Evangelist | DataRobot
10:00
Realizing Value through DataRobot’s AI-Powered Apps

During this session, we will provide an overview of how DataRobot can help your organization close the loop on value by providing a quick, visual, and intuitive way to interact with predictive models to optimize outcomes and support critical decisions using DataRobot’s AI-Powered Applicationsmore details

Realizing Value through DataRobot’s AI-Powered Apps image
Ina Ko
Director, Product Management | DataRobot
10:00
Some Failures and Lessons Learned Using AI in our AI company

We started on an exciting journey for AI-driven Engineering to apply AI in our own company. With enthusiastic developers, nearly limitless cloud resources, and a magical AI product, what could possibly go wrong?
Turns out, a lot.
Borys and Dustin, who lead the Engineering Productivity domain within DataRobot, will tell you some stories of how they use the DataRobot AI platform to improve R&D operations and efficiencymore details

Some Failures and Lessons Learned Using AI in our AI company image
Dustin Burke
Director Of Engineering | DataRobot
Some Failures and Lessons Learned Using AI in our AI company image
Borys Drozhak
Director of Engineering | DataRobot
10:00
The Rise of MLOps

The structure of how enterprises are delivering and consuming AI has changed drastically with the proliferation of open-source technology. The focus has shifted from tooling and platforms focused solely on model development to tools and platforms focused on the overall usage, consumption, and management of models. This emerging field is called Machine Learning Operations or MLOps. MLOps delivers ROI for those organizations that invested in “full-stack” ML technology, from development to operationalization, monitoring, and management.

During this session, you will learn:
The inherent challenges of deploying ML at scale and how to overcome them.
How to eliminate AI-related risks by adopting best practices for MLOps.
Measuring the quality of ML in production over-time with ML-focused monitoring.
What ML production lifecycle management is and why it mattersmore details

The Rise of MLOps image
Seph Mard
Technical Product, Director | DataRobot
10:00
How to Stop Worrying and Start Tackling AI Bias

The stories of bias in AI are everywhere: Amazon’s recruiting tool, Apple’s credit card limits, Google’s facial recognition, and dozens more. The quick solution is just to blame the algorithm and its designers. However, as data scientists, its incumbent on us to understand the true source of the bias and improve the underlying process.
AI does not create bias alone; it exposes the latent bias present in the system it was designed to imitate. We need to reframe the conversation around bias in AI to instead identify it as the first step in building a more ethical system.
In this talk, we show how machine learning can make the implicit bias of a human institution explicit. Bias becomes diagnosable, correctable, and ultimately preventable in a way that cannot be replicated in human decision-making, which is opaque and difficult to change. Bias is not new, but AI represents a new toolset to measure and change it.
The goal is not only to provide you a theoretical understanding of bias, but a practical plan that you can start to implement right away. After all, it’s not whether or not you have bias in your institution, but how you plan to handle itmore details

How to Stop Worrying and Start Tackling AI Bias image
Jett Oristaglio
Data Scientist | DataRobot
10:30
Lessons Learned with Data & Storytelling
Lessons Learned with Data & Storytelling image
Danny Ma
Founder & CEO | Sydney Data Science
Lessons Learned with Data & Storytelling image
Kate Strachnyi
Founder | DATAcated Academy
Lessons Learned with Data & Storytelling image
Jen Underwood
Director Product Strategy | Oracle
Lessons Learned with Data & Storytelling image
Susan Walsh
Founder/Owner | The Classification Guru
Lessons Learned with Data & Storytelling image
Ben Taylor, PhD
Chief AI Evangelist | DataRobot
11:20
Components of AI Infrastructure & MLOps

The boom in AI has led to an exponential rise in compute demand, with data scientists taking to the cloud to experiment and scale their model development. Desires for cost-efficiency, increased performance, and security, have all led to the search for alternative solutions for differing needs, including bringing intensive AI workloads back to local data centers.

For those organizations that have elected to build on-premises, what are the building blocks for the compute, storage, and networking components? What considerations need to be taken when building a homegrown software stack? What MLOps platforms exist and what makes a good solution for multi-tenant teams of data scientists?

Join this session to learn about NVIDIA’s take on how we build AI Infra in-house and what our advice is for organizations looking to replicate our experience...more details

Components of AI Infrastructure & MLOps image
Michael Balint
Sr. Product Manager | DGX @NVIDIA
11:20
Experimentation, Metrics and Analytics: An Ecosystem for Data Informed Decisions

From hypothesis generation, to hypothesis testing to shipping decisions, product companies require data informed decisions. When shipping a product, companies often think experimentation. But getting to the point of an experiment requires a lot more than a platform. In this talk, I will discuss why informed product decisions require a complementary data ecosystem that supports metrics, analytics and experimentation…more details

Experimentation, Metrics and Analytics: An Ecosystem for Data Informed Decisions image
Eric Weber
Head of Experimentation, Scientific Advisor | Yelp, Propulsion Academy
11:20
Fireside Chat with Jacqueline Ros Amable – AI in Climate Tech

During this session we will be exploring where and how Jackie sees AI playing a role in solving climate change. We hope attendees will leave this session inspired to dig deeper into climate technology and will start trying to solve some of the challenges with AI.

Session Outline
Introduction
Discussion around the largest opportunities
Why/How there is money for innovators in the space
How Nextcorps views early-stage companies with/without AI
Q&A..more details

Fireside Chat with Jacqueline Ros Amable – AI in Climate Tech image
Jacqueline Amable
Managing Director | NextCoprs
Fireside Chat with Jacqueline Ros Amable – AI in Climate Tech image
Ryan Sevey
General Manager | DataRobot
11:20
Building an Analytics COE: One Leader’s Story

Do you have the desire to understand and use AI, but are unsure where to start? Edward Young was in the same position just a few years ago. He created his own path to learn AI and had some fits and starts along the way, but today he can spot use cases, help work through complex data issues, and move with ease between the data science and business teams within his organization. Ed’s story will inspire and empower you to get started and upskill yourself into a critical position within your current organization or the nextmore details

Building an Analytics COE: One Leader’s Story image
Edward M. Young
Director of Advanced Analytic | FCA Fiat Chrysler
12:15
Solving Practical Computer Vision Problems in 10 Minutes

This session will give a brief intro into Computer Vision and jump straight into real world examples. We will keep the learning practical by walking through a number of projects in manufacturing, agriculture, retail, and home insurance, with takeaways that will be applicable to any organization and use case. Some common pitfalls around image models building and evaluating will be demonstrated as well as how to get around themmore details

Solving Practical Computer Vision Problems in 10 Minutes image
Anton Kasyanov
Principle Machine Learning Engineer | DataRobot
Solving Practical Computer Vision Problems in 10 Minutes image
Ivan Pyzow
Deep Learning Engineer | DataRobot
12:15
A Tutorial on Robust Machine Learning Deployment

A hands-on tutorial for productionizing machine-learning models using robust open-source tools. This tutorial shows you how to go from a python scikit model, get REST API endpoint, test it for common deployment issues, containerize, and deploy it. This is performed using a new open-source package, DRUM, that moves beyond flask and takes advantage of NGINX and uWSGI for serving model in a production-grade manner.

This package provides support for a variety of modeling frameworks including: Keras, scikit learn, R, H2O, DataRobot, and more. The package also incorporates unit testing for common deployment issues. All of this is easy to containerize and even add monitoring agentsmore details

A Tutorial on Robust Machine Learning Deployment image
Tim Whittaker
Customer Facing Data Scientist | DataRobot
A Tutorial on Robust Machine Learning Deployment image
Rajiv Shah, PhD
Data Scientist | DataRobot
12:15
Hands-on Data Science for Software Developers — A Live Coding Session with Data Robot Self-Service

David “Gonzo” Gonzalez will code up an AI-powered API beginning with real-world datasets and walk through all the steps and thought processes that govern a successful implementation of AI from a Software Developer’s perspective. We will NOT be using Jupyter notebooks. We will be taking questions in real-time and checking code into a community repo for all to reference after the session...more details

Hands-on Data Science for Software Developers — A Live Coding Session with Data Robot Self-Service image
David Gonzales
Software Developer Experience Director | DataRobot
12:15
The AI Practitioner Series – Data Prep Walkthrough (A Reusable Framework!)

Join us as we walk through the AI Practitioners Data Prep; from idea generation and socialization to problem framing and data prep! We’ll walk you through the first three of our AI Practitioners worksheets and then demonstrate framing up a dataset for prediction with DataRobot’s Paxata. Don’t miss out on getting your copy and overview of these agnostic and reusable worksheets!..more details

The AI Practitioner Series – Data Prep Walkthrough (A Reusable Framework!) image
Shyam Ayyar
Product Manager | DataRobot
The AI Practitioner Series – Data Prep Walkthrough (A Reusable Framework!) image
Sean Smith
Director of AI Success | DataRobot
Select date to see events.

How will ticket holders get access to the event?

  • Attendees will receive an email with login instructions 3-5 days before the event.  If you have trouble logging in or haven’t receive any details by October 27th, please email info@odsc.com

What are the technical requirements to be able to participate?

Open Data Science

Open Data Science
Innovation Center
101 Main St
Cambridge, MA 02142
info@odsc.com

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