Ocotber 29th detail Schedule coming soon.
See past schedule below
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 Applications…more details
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 efficiency…more details
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 matters…more details
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 it…more details
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
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
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.
Discussion around the largest opportunities
Why/How there is money for innovators in the space
How Nextcorps views early-stage companies with/without AI
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 next…more details
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 them…more details
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 agents…more details
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
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
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 firstname.lastname@example.org