Wave Painting Drawing Artistic  - PublicDomainPictures / Pixabay Virtual Conference Experience Data for AI Week September 14 - 18, 2020
100+ Live, On-Demand, and Educational Sessions
Across 5 topics and 3 tracks
Focused on the Data Side of AI
Register Now!

Data for AI Week: Virtual Conference Experience - Addressing the Data Side of AI in a Conference Unlike any Other

The Data For AI Conference Week, taking place September 14-18, 2020, is an online event unlike any you’ve attended. Over the course of one week the event will combine a large library of on-demand content with live keynotes and live webinar-style panel engagements, attendee/expert matching, “ask-me-anything” style expert sessions, and educational content. Content is meant to be consumed around your schedule, not a predetermined schedule created for you. Key topics include Data Engineering, Data Preparation, Data Labeling & Annotation, Sourcing Data and Data Generation for AI.

WHERE

Online

WHEN

Monday to Friday
September 14 – 18, 2020

HOW MUCH

FREE to Attend


Sponsors & Partners

Have Data and AI Needs? This conference is for you!

Five Topics:

  • Data Engineering

  • Data Preparation

    Data Labeling & Annotation

    Sourcing and Generating Data

    All Other Topics Data-Related for AI

  • Three Tracks:

  • Industry Applications

  • Government and Public Sector Sessions

  • Technology Deep Dives


  • Featured Speakers





















    This Conference is for YOU. On Your Schedule.

    So many online events try to replicate the in-person experience by forcing attendees to attend sessions at specific points in time. In the new remote reality we live in with many distractions for your time, this doesn’t make sense. Why be forced to attend on a schedule that doesn’t work for you?

    This online experience is different. You can engage with live content scheduled throughout the week or you can consume hundreds of sessions that have already been recorded and made available to you on-demand. Want to engage with an expert? Schedule for a time that works for you. Attend live sessions and get your questions answered live. Or interact asynchronously with content consumed on-demand on your schedule.


    Register Today!

    Sign up now and get access to content and expert opportunities as soon as they are available! Tickets are free so register to gain access!

    Register Today: Event Starts ONLINE on September 14, 2020

    Register Now

    Conference Schedule

    Check out the continuously growing list of available on-demand content, live sessions, keynotes, educational opportunities, Ask-Me-Anything Expert sessions, 1:1 expert connections, Demo showcases, and more!
    • Content On-Demand


    • This demo-based session walks through each step in the pipeline, emphasizing best practices ranging from combining Azure Machine Learning with an Apache Spark component, such as Azure Databricks, to managing data and models across environments.
      View Session Details
      Data Engineering
      Data Preparation

    • Using machine learning it is possible to develop intelligent patterns that detect fraudulent behavior while complying with regulatory rules (e.g. GDPR). Databricks helps make this process simple, reliable, scalable and reproducible. With Databricks, organizations can build a modular solution that evolves as the fraudulent behavior patterns do.
      View Session Details
      General Sessions

    • This interactive demo will cover popular ML use cases in the Government. You’ll leave with a roadmap towards implementing AI and ML at your agency, allowing you to find insights and value in your data that you never thought possible.
      View Session Details
      Data Engineering
      General Sessions

    • In this session, we’ll provide an introduction to machine learning, discuss key use cases in the Public Sector and explore how big data technologies have evolved to enable machine learning to produce more accurate predictions and unlock insights buried in your data.
      View Session Details
      Data Engineering
      General Sessions

    • With these fundamental data problems solved, health organizations can refocus their efforts on building analytics and ML products instead of wrangling their data. One example is the COVID-19 surveillance solution developed on top of Databricks, which is being deployed in a number of state and local government health departments, as well as by a number of hospitals and care facilities across the U.S.
      View Session Details
      Data Preparation
      General Sessions

    • This demo will demonstrate how to detect available parking spots from imagery using Deep Learning.
      View Session Details
      Data Engineering
      Data Labeling
      Data Preparation
      General Sessions

    • We leverage Databricks to ingest pharma transaction data and red flag anomalous opioid distributions.
      View Session Details
      Data Engineering
      General Sessions

    • Since 2013, cybercriminals have stolen over 14.7B digital records from organizations across the industry. Responding quickly to threats is critical to avoiding a breach. To do this successfully, the private sector and government organizations need to monitor and analyze billions of data signals in real-time and perform ad-hoc analysis over large time windows of historical data. Yet, existing security tools are struggling to keep up. Overcoming these challenges requires a new approach to threat detection rooted in big data, analytics and AI.
      View Session Details
      Data Engineering
      Expert Sessions

    • We will describe how a data scientist, data engineer, and analyst all work together on a real time world use case.
      View Session Details
      Data Engineering

    • Learn about the features of Delta Lake that allow it to embody the best implementation of a lakehouse architecture, including tables that can handle both streaming and batch data, ACID transactions, schema enforcement, and evolution, time travel, and updates, inserts, and deletes.
      View Session Details
      Data Engineering
      General Sessions

    • Discover the new MLflow Model Registry, which simplifies the model productionization process by helping data teams stage, test, deploy, and monitor ML models through a simple, collaborative interface.
      View Session Details
      Data Engineering
      Demo Showcase

    • In this demo, you will learn how you can detect nation state attackers like APT19 and cybercriminals like the Turla gang. We bring in terabytes of data from endpoints and DNS logs, enrich them with threat intel and run machine learning models to detect threat activities.
      View Session Details
      Data Engineering
      General Sessions

    • Learn how you can migrate expensive open source big data workloads to Azure and leverage latest compute and storage innovations within Azure Synapse and HDInsight with Azure Data Lake Storage to develop a powerful and cost effective analytics solutions.
      View Session Details
      Data Engineering
      Data Preparation

    • Use Power BI to build scalable analytic solutions that meet your governance needs. Also to leverage big data investments w/connections to data warehouses and apply machine learning w/o compromising performance or security.
      View Session Details
      Data Engineering

    • Stanford masters students as part of CS230: Deep Learning describe the process and workflows for their project in which they focus on identifying suitable areas to land urban air vehicles through satellite imagery. In the recap, Andrew and Seraj share their experience completing image segmentation tasks via Labelbox’s software and labeling service, as well as lessons learned and best practices for other computer vision researchers.
      View Session Details
      Data Labeling
      Data Preparation

    • In this presentation, we’ll be presenting a walkthrough of Labelbox’s training data platform and highlighting some of the main cost and productivity benefits for data science teams.
      View Session Details
      Data Labeling
      Demo Showcase

    • ML practitioners can dramatically reduce the time and labeling budgets by harnessing model-assisted labeling for both strong and weak supervision. Labelbox ML lead will be sharing an interactive demo and tutorial on production workflows that we have worked well with the goal of highlighting important principles that spark inspiration for others.
      View Session Details
      Data Labeling
      Sourcing Data

    • In this session, experience GPU-accelerated analytics through a real-life 5G network planning demo. Learn how OmniSci can be used to combine RF mapping and demographic data sets at scale using service quality and NPV measures to optimize both cost-effectiveness and performance.
      View Session Details
      Data Engineering

    • The AI revolution is here, but it has demands. AI needs clean, contextual data in the proper format to do its job. Too often, though, that process of cleaning and contextualizing takes an extraordinary amount of time—time that could be spent refining your predictive models.
      View Session Details
      Data Preparation

    • If you’re considering a machine learning project, you probably know that you need data, and lots of it. And while many companies are swimming in volumes of data, that data is almost never ready for AI and ML projects. It must be prepared, which can include cleansing, annotation, and more.
      View Session Details
      Data Engineering
      Data Preparation

    • Abstract: Finding purposely-hidden nuclear sites is hard. But new tools and datasets allow analysts to interactively explore huge geotemporal datasets. OmniSci has recently partnered with the Center for Nonproliferation Studies (CNS) and Planet to demonstrate how daily satellite imagery, machine learning for feature extraction, and interactive analytics can help make the world safer. CNS continually assesses potential nuclear missile production sites. It has found that in North Korea these are often hidden at the ends of new mountain roads. How can we turn this insight into actionable data?
      View Session Details
      General Sessions

    • This session will address both sides of the challenge: (1) using data-efficient strategies during the utterance collection and annotation phases to optimize the trade-off between cost and quality when collecting training data, (2) using data-driven approaches to train and generate behaviors for a conversational agent despite noisy data and lack of training labels.
      View Session Details
      Data Labeling
      Data Preparation
      Sourcing Data

    • Gathering an initial data set for your machine learning project is the first hurdle on the path to a successful machine learning algorithm. How do you get your hands on the perfect data set? We joined our partners at Keymakr to discuss the attributes of an ideal data set, the pros and cons of using a pre-created data set, and some best practices for building your own.
      View Session Details
      Data Engineering
      Data Labeling
      Data Preparation
      Sourcing Data

    • Are you training a self-driving car, detecting animals with drones, monitoring equipment for predictive maintenance, or identifying car damage for insurance claims? The challenges to effectively train, deploy & tune a computer vision model at scale remain the same.
      View Session Details
      Data Engineering
      Data Labeling
      Data Preparation
      Sourcing Data

    • Bias in machine learning is a significant concern as technology gets increasingly ubiquitous across many industries. Some types of bias can be attributed to limits in design and tooling; however, the bias in the training data itself is a general phenomenon. Skewed training data propagates into discriminatory AI models that amplify human prejudices. Building a data labeling framework that uses a diverse set of crowd workers to collect and label the data can help reduce bias.
      View Session Details
      Data Engineering
      General Sessions

    • It’s no secret that there is an enormous business opportunity with the rise of autonomous vehicles and the connected car. Whether you are building a fully autonomous vehicle, improving driver assistance features, or in-cabin experience, high-quality annotated training data is the key to effective AI systems. This session will help take you from Level 1 to Level 5 autonomy, driving you ahead of the competition.
      View Session Details
      Data Engineering
      Data Preparation

    • Learn how conversational AI systems are being made more intelligent and scalable.
      View Session Details
      Data Labeling
      Sourcing Data

    • The role of humans isn’t limited to training data preparation -- it extends across the entire model development process. Join CloudFactory for a look at how you can deploy humans in the loop throughout the AI lifecycle -- from proof of concept to production -- to improve model output, reduce costs, and accelerate model development.
      View Session Details
      Sourcing Data

    • The end result for every data labeling project is quality data - but how do you get there? There are several quality assurance workflow types but each has pros and cons when it comes to the quality and speed of data outputs. When you're evaluating data labeling providers or planning in-house processes, you should consider which QA workflow will work best for your business and data needs.
      View Session Details
      Data Engineering
      Data Preparation

    • During the session, we'll be covering: - Some best practices for AI in Production - Produce demo and overview - How a training data platform helps you optimize data labeling costs - How Labelbox helps AI-focused product leaders get to production faster - The specific features that will help you decrease costs, improve automation and collaboration for your ML products and projects.
      View Session Details
      Data Engineering
      Data Preparation

    • In this session, we’ll discuss how a predictive algorithm helped the City of Flint focus their service line (water pipe) investigations in the areas at highest risk for having lead or galvanized steel service lines. We’ll discuss our work (in progress) to create a public map using best visual data and public health communication practices that, when completed, will allow Flint residents to visualize the predictive model outcomes and the pipe replacement progress in the city.
      View Session Details
      Data Engineering
      General Sessions

    • Walkthrough on key data prep functions, such as cluster, edit, and dataset linking.
      View Session Details
      Data Engineering
      Data Preparation

    • For this dashboard we dug into an April 25th, 2020 Washington Post article about COVID-19 hotspots at US meat processing plants. Using data from our partners at X-Mode and Safegraph, we analyze activity at these plants as well as the COVID-19 infections in nearby communities. Finally, we trace activity from one plant to identify potential virus hotspots in nearby locations. X-Mode’s dataset used in this research is aggregated and generalized. X-Mode does not collect or share any personally identifiable information such as name, email, or phone number. All devices have given consent to location collection.
      View Session Details
      General Sessions

    • Companies across industries face new challenges connecting with empowered consumers. Customers have been completely altering their shopping habits in recent months like never before. With shops closed or limited entry, purchasing has shifted to online, loyalty has shifted by industry and brand, safety is paramount, and all of it varies by geography. Learn how best-in-class retailers are using AI and machine learning to identify signals in their data to become truly customer-centric.
      View Session Details
      Data Engineering
      Data Preparation

    • The Empowered Consumer is more connected and informed than at any other time in retail history. Retailers must now start anticipating and predicting their consumer’s evolving needs and habits by transforming their business decisions through embedding AI in their data-driven culture. Learn how best-in-class retailers are using AI and machine learning to identify signals and patterns in their data to become truly customer-centric.
      View Session Details
      Data Engineering
      General Sessions

    • As the global coronavirus pandemic is causing major disruptions to communities and the economy, many existing data science models struggle to adapt to these shifts due to a shortage of available data. learn more about: Strategies to build a "cold start" model. Checks to ensure you have meaningful, consistent signal from limited examples. Diving deeper into model insights to verify meaningful model fit.
      View Session Details
      Data Engineering
      Data Labeling
      Data Preparation
      Sourcing Data

    • Predictive analytics is a key differentiator for asset management firms, but how does an organization do it at scale for business impact? Buyside firms — and the broader financial services industry — are capitalizing on AI advances to predict activities across the front, middle and back office to increase revenue, improve efficiencies, reduce costs — and improve risk management.
      View Session Details
      General Sessions

    • For years, insurers have been using machine learning to identify fraud, develop pricing strategies, improve underwriting processes, and more. Now, a new class of automated machine learning tools is democratizing data science, making it possible for insurers of all sizes to increase ROI on machine learning initiatives and transform into AI-driven enterprises.
      View Session Details
      General Sessions

    • One of the most common questions about machine learning is “How do I prepare my data for a machine learning project?” In order to run successful machine learning projects, and create highly-accurate predictive models for your business, you need to prepare your data effectively. But this process doesn’t have to be a burden.
      View Session Details
      Data Labeling
      Data Preparation

    • In this two-part learning session, we discuss best practices around data partitioning and working with imbalanced datasets. Five-fold cross-validation is often the silver bullet for partitioning your validation dataset, but there are some dangerous caveats you have to be aware of to make sure that you're building robust models. In this learning session (part 1) , we talk about those pitfalls and outline strategies for handling them. Binary target variables are very common in data science use cases, many of which are severely imbalanced. When you're building models for infrequent events, such as predicting fraud or identifying product failures, it's important to watch out for imbalance in your data. (In part 2 of this learning session we discuss strategies for working with imbalanced datasets and provide some rules-of-thumb for these types of use cases.)
      View Session Details
      Data Engineering
      Data Preparation
      Sourcing Data

    • Understand how DataRobot can enhance your workflow and how to use DataRobot to more efficiently perform the most common types of business analyses.
      View Session Details
      Data Engineering

    • This session is an introduction to the DataRobot platform. It will focus on how to use DataRobot to quickly build, interpret, and implement highly accurate machine learning models.
      View Session Details
      Data Engineering

    • General Sessions

    • Jason McGhee, Senior Machine Learning Engineer at DataRobot, has been spending time applying deep learning and neural networks to tabular data. Although the deep learning technique can prove challenging, his research supports how valuable it is when using tabular datasets. In this video, Jason shares some important techniques for implementing deep learning when learning heterogeneous tabular data.
      View Session Details
      Data Engineering

    • Learn the importance of understanding your models and how that can help counter biases to help build more reliable models.
      View Session Details
      Data Engineering

    • This session shows how to use DataRobot’s Automated Time Series. DataRobot builds for the preprocessing and construction of sophisticated time series models that predict the future values of a data series based on its history and trend.
      View Session Details
      Data Engineering

    • How can you incorporate images into your model workflows? DataRobot can easily build and deploy highly accurate and explainable machine learning models with images using commodity hardware.
      View Session Details
      Data Engineering

    • The DataRobot platform allows for deployment using native DataRobot models, custom-trained models using containers, and monitoring prediction servers using the DataRobot agent. We cover each of these modalities and provide attendees with example code.
      View Session Details
      Data Engineering
      Data Preparation

    • 2020’s word of the year is likely to be “unprecedented” — which might make us rather nervous as data scientists or analytics professionals. Machine learning is, after all, the art and science of predicting outcomes of upcoming events based on historical data; in a period without precedent, most available data describes a different, bygone world. In this session, we discuss how this affects our ability to build useful predictive analytics models for the current environment and how to ensure that data science and machine learning continue to add business value.
      View Session Details
      Data Engineering

    • See how OmniSci and our partners at AWS, Safegraph, Veraset, and X-Mode are using anonymized, data-driven methods to contribute to relief efforts at a national scale for the next phase of the COVID-19 response efforts.
      View Session Details
      Data Preparation
      Demo Showcase
      General Sessions

    • We demonstrate the Alegion platform's capability to support 4K video annotation and accelerate high-quality labeling through automation and machine learning.
      View Session Details
      Data Labeling
      Demo Showcase
      General Sessions

    • Learn about the different parts of the Databricks Unified Data Analytics platform, and how they work together in this overview. We'll cover some of the main data analytics use cases, and show you how Databricks simplifies the workflow
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      Data Engineering
      Demo Showcase
      General Sessions

    • Watch the demo to see a real-time demonstration of our AI Powered transcription, redaction, and facial recognition capabilities.
      View Session Details
      Data Engineering
      Data Labeling
      Demo Showcase
      General Sessions

    • In this demo, we'll walk you through the Labelbox platform and show you how quickly you can create projects and start annotating high-quality training data.
      View Session Details
      Data Labeling
      Data Preparation
      Demo Showcase

    • Watch the demo to see how DataRobot’s automated machine learning platform combines predictive modeling expertise with the best practices of data science to deliver accurate and actionable predictions with full transparency and interoperability.
      View Session Details
      Data Engineering
      Data Preparation
      Demo Showcase

    • How to level up your modeling after running Autopilot. Learn about new techniques, algorithms, and features that DataRobot makes available to improve your models.
      View Session Details
      Data Engineering

    • Telco markets worldwide are highly competitive. Consumers have more options than ever to satisfy their telecommunications needs. This means providers are competing harder, and paying closer attention to critical factors like cost of customer acquisition, average revenue per unit (ARPU), and customer churn. The smartest providers understand the tight relationship between these issues and their massive, and growing, stockpiles of data. Fortunately, there are new technologies that can handle billions of rows of data, joined across multiple datasets, with millisecond filtering and visualization times. With OmniSci, massive customer data is an asset instead of a barrier.
      View Session Details
      Data Engineering

    • DataRobot offers a comprehensive set of APIs that let you integrate AI into your applications with ease. In this workshop, we cover the range of APIs available to you, how to get started, and demonstrate an end-to-end example of building an AI-powered application with best practices.
      View Session Details
      Data Engineering

    • Watch as we look at areas that are adhering to shelter in place policies and to what degree there is a correlation between adherence and the spread of the disease. With this movement data, we can determine home location then visualize if devices are at home, for how long, track changes over time and correlate to the spread of the disease. X-Mode’s dataset used in this research is aggregated and generalized. X-Mode does not collect or share any personally identifiable information such as name, email, or phone number. All devices have given consent to location collection.
      View Session Details
      General Sessions

    • In this video we show how OmniSci can be used to track the effectiveness of social distancing policies to help fight the spread of COVID-19. Using premium location data from our partner, X-Mode, we aggregate 4.5 billion anonymized location records at the county level. We can slide across this data over the time, revealing how well counties are sheltering in place.
      View Session Details
      General Sessions

    • How to use AI to assure 5G services, including 5G slicing, perform as planned with API-based use cases including customer experience, churn-management and fraud-management.
      View Session Details
      Data Preparation
      General Sessions

    • Abstract: Data and analytics have played a central role in the first wave of the global response to COVID-19. From the earliest days of the pandemic, we’ve had up-to-date reports of case counts, fatalities and recoveries gathered by industrious volunteers everywhere. See how OmniSci and our partners at AWS, Safegraph, Veraset, and X-Mode are using anonymized, data-driven methods to contribute to relief efforts at a national scale for the next phase of the COVID-19 response efforts.
      View Session Details
      General Sessions

    • In this demo video we explore the COVID-19 pandemic, filtering across location and time. OmniSci's demo allows users to visualize the spread of the virus using maps and charts, compare the growth of cases across various countries and US states, and analyze the recovery rate in various regions of the world. This demo includes data from Johns Hopkins (https://coronavirus.jhu.edu/) and is updated daily.
      View Session Details
      General Sessions

    • Check out how OmniSci can explore movement patterns and population movement easily on billions of records. Here, you will see how we can look through space and time to observe Italian travelers as they come to NYC and other locations around the world. X-Mode’s dataset used in this research is aggregated and generalized. X-Mode does not collect or share any personally identifiable information such as name, email, or phone number. All devices have given consent to location collection.
      View Session Details
      General Sessions

    • See how we leverage data provided by the New York Times including population demographics, movement patterns and external factors such as weather. OmniSci can look at county level infections to understand the current situation and use Jupyter Labs to test hypotheses and predict future events.
      View Session Details
      Data Engineering

    • Data Engineering
      General Sessions

    • Day 1

      Mon. Sep. 14, 2020

    • Welcome to the Data for AI Week 2020 Virtual Conference. In this session we'll go over how the event website works, highlights of the various sessions, and all the great opportunities, features, and benefits in participating in this conference.
      View Session Details
      General Sessions

    • It is essential for both public entities and private businesses to prioritize efficiency and effectiveness in their work. This has become even more important in the COVID-19 era, where organizations are faced with stretched budgets and a changing workforce landscape. In this session, learn how the Maverick AI platform has been essential in achieving success for both public and private sector clients, and what the public and private sectors are thinking about as they consider implementing AI.
      View Session Details
      General Sessions

    • The National Science Foundation supports research on questions at the heart of many of our national priorities. The broad behavioral, social and economic impacts of artificial intelligence constitute one such priority. In this interactive keynote Dorothy Aronson will share how the NSF plans to embrace AI technologies, what AI projects they are currently funding, and how AI will impact the agency in the coming years.
      View Session Details
      General Sessions

    • For many years banks and financial institutions have been at the forefront of using technology to help with many operations and processes. In this panel we’ll explore how various financial companies are using data and machine learning to help catch fraud, improve banking processes, and improve the overall customer experience.
      View Session Details
      Data Engineering
      Data Preparation
      General Sessions

    • Chris Mattmann will explain how JPL’s Division 174 for AI, Analytics, and Innovation in the Information Technology and Solutions Directorate (ITSD) supports advanced analytics, AI, and Machine Learning for Smarter Rovers, a Smarter Campus, and beyond!
      View Session Details
      General Sessions

    • AI is enabling a new paradigm. By leveraging voluminous real-time information and new algorithms, there is a promise of better and more efficient care. In this session, several case studies from the work of the National Artificial Intelligence Institute at the VA and collaborations will be discussed, including research and development that empowers Veterans to search for clinical trials and physicians to evaluate COVID-19-associated prognosis and needs.
      View Session Details
      General Sessions

    • Machine learning requires data, and many companies have lots of data that is useful for many very important tasks. However there are many questions about how this data should be used, shared and applied. Additionally, companies walk a fine line with just how much they want to let customers and users know about the data they have on them. This panel will explore the ethical side of data usage from an industry perspective.
      View Session Details
      General Sessions

    • This talk will focus on how teams can adopt an open source approach in developing data and machine learning learning based solutions. We’ll explore how we can create a reusable code-base and leverage it to build powerful data applications. We will explore scenarios on how an analyst vs an executive can benefit from the same codebase. If this sounds fun, tag along.
      View Session Details
      Data Engineering
      Data Preparation

    • Many AI projects fall short of expectations due to poor model performance or the unintended consequences of inaccurate AI decisions. What if there was a universal way for MLOps/AIOps to evaluate and monitor the performance and behavior of AI models, both pre-deployment and ongoing, no matter the vendor or features used? In this session, we will review the pitfalls of opaque AI models, and discover how to evaluate, compare, and monitor performance and behavior across AI models, for better AI model trust and explainability.
      View Session Details
      Data Engineering
      General Sessions

    • Machine learning requires data, and organizations of all types and in different industries have lots of data that is useful for many very important tasks. However enterprises are finding that there are concerns and restrictions on how that data can be used, shared and applied. This panel will explore the ethical side of data usage from an industry perspective.
      View Session Details
      General Sessions

    • Machine learning outcomes are only as good as the data they are built on, but preparing data for these advance workloads can be time-consuming and difficult to scale, especially if you are looking to implement machine-learning applications that rely on data from across your entire organization. In this session, Ben Snively will share some best practices related to collecting, storing, and processing big data and disparate data sets so that you glean intelligent insights from your machine-learning algorithms. We will share some architectural design patterns.
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      Data Preparation

    • The Retail industry has been disrupted by the e-commerce revolution more than any other industry. As a Director of Core Data Science at TheHome Depot which is the largest home improvement retailer in the world, I deal with the challenges of building such systems utilizing the cutting-edge technologies in AI, machine learning, and data science. In this talk, I would like to discuss and highlight how we have leveraged different aspects of AI to solve challenging e-commerce problems for HomeDepot
      View Session Details
      General Sessions

    • Day 2

      Tue. Sep. 15, 2020

    • This presentation will discuss benefits and applications of “smart data” and intelligent data understanding for operationalizing AI. Techniques that enable and benefit from smart data are data discovery, machine learning, knowledge graphs, semantic linked data, knowledge discovery, and knowledge management. Intelligent data understanding thus meets the needs for AI operations, which must devour streams of data – not just any data, but smart data – the right data at the right time in the right context.
      View Session Details
      Data Preparation

    • This session will highlight actions and initiatives to accelerate the use of data inside the Federal government that are currently happening as well as outline investments and actions needed to evolve operating culture and build the workforce for the future. Hear about key elements and Agency specific activities ranging from Federal Data Strategy and Agency AI efforts. Kent will also share thoughts on actions needed to continue to develop workforce for today and the emerging jobs of tomorrow. Actions that include educational shifts, cultural shifts and commitments from leaders inside government and across private sector.
      View Session Details
      Data Engineering
      General Sessions

    • Azure Synapse is an end to end data platform that combines data warehousing, visualization, data science, and ETL / ELT processes all in one workspace. Dayo will list out and Demo some of the Top features that makes Azure Synapse Analytics a very powerful tool to create a seamless Data Process
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      Data Engineering
      Data Preparation

    • This panel will address key challenges for data and AI at the state and local government level
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      General Sessions

    • Join us for an open Ask an Expert Session with experts from Amazon Web Services (AWS)
      View Session Details
      Expert Sessions

    • Once upon a time working on artificial intelligence or machine learning meant constantly yearning for data and struggling to find cool problems to work on for which there was some. Now data’s aplenty but that old longing for data seems to have left a deep scar. Will more data by itself make the machine intelligent? What if making AI business-ready for you was altogether different than collecting data? What if it had to do with the (business) questions you ask? That sounds like a conversation worth starting. Introduced by: Kathleen Walch, Managing Partner & Principal Analyst, Cognilytica
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      Data Engineering
      Data Preparation
      General Sessions

    • For many in government, they know that the government is drowning in data. However, unlike private industry there are some unique challenges that Governments have when it comes to data collection and data management. In this panel we’ll have folks from various government agencies including both civilian and DoD to share challenges in data management and what agencies can do to overcome these challenges
      View Session Details
      Data Engineering
      Data Labeling
      Data Preparation
      General Sessions

    • This workshop will present some typical GEOINT applications delivered as interactive dashboards and will drill down into the construction of one of them in detail. Attendees will learn all three levels of a modern web stack.
      View Session Details
      Data Preparation

    • Successful MLOps not only requires strong collaboration between the AI data team, AI model team, and DevOps- it’s the ability to effectively manage and mitigate risk across the deployment, integration, scale, monitoring, and compliance stages of an AI project. Learn more about ML Ops and implementations in this session.
      View Session Details
      General Sessions

    • GSA released a publicly available Request For Information to the industry in order to explore the possibilities of artificial intelligence in data analysis of publicly available procurement data. We requested an analysis and aggregation of publicly-available government-wide data sources of their choosing.
      View Session Details
      Data Engineering
      Data Preparation

    • Day 3

      Wed. Sep. 16, 2020

    • This talk will provide an overview of the NSF Convergence Accelerator program (C-Accel) and Track D of the Accelerator on Enabling AI Innovation via Data and Model Sharing.
      View Session Details
      Data Engineering
      Data Preparation
      General Sessions

    • Innovation in AI is accelerating at a steep rate, and the tools and processes used to support such AI innovation are no exception. Long the domain of a human workforce, data annotation is seeing advances, such as using AI and automation to pre-label data and conduct quality assurance, that seemingly promise to eliminate the need for human-powered labeling. This panel will explore the future of humans-in-the-loop data annotation and what role your labeling workforce will play in the years to come.
      View Session Details
      Data Labeling

    • Cheryl Ingstad, Director of the Artificial Intelligence & Technology Office (AITO) for the Department of Energy (DOE) will share how DOE (including its 17 national labs) is leading the federal government in the development and application of AIto strengthen its core missions of energy, cyber, and national security and accelerate scientific discovery. Join us for this interactive discussion followed by Q&A.
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      General Sessions

    • Valuable unstructured content is everywhere in organizations. Video, audio, and text content abounds, just waiting to be discovered and utilized--whether within BPA and RPA processes or stored in content management and line of business systems. The challenge is, how to enrich and extract that content for better findability and insight, and how to do it quickly, easily and at low cost? Join us in this session as we explore how to leverage an expansive ecosystem of hundreds of ready-to-deploy AI models to extract business value from content, and how to do so at scale, in near real time, without the need for AI expertise.
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      Data Preparation

    • Given the GSA Centers of Excellence's government wide perspective, Alex and Krista will share their experience from on the ground data management and implementation to help agencies prepare for Artificial Intelligence as an enabling technology to help support mission delivery. They will share best practices and lessons learned from across government.
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      Data Engineering
      Data Preparation
      General Sessions

    • Practical decision systems require much more than end-to-end learned models. This talk will focus on research and engineering questions on machine learning robustness and the two broad categories of work in that area.
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      General Sessions

    • The AI system you build is as good or bad as the data you have trained it on. Whether you are training a self-driving car, building a customer service chatbot, or diagnosing diseases with AI, a scalable training data strategy is integral to your success. In this session, we will discuss how much training data is enough, how to effectively manage quality, quantity, and throughput in your data, and more!
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      Data Labeling

    • Amidst challenging times for law enforcement, police departments and other public safety agencies have an opportunity to foster greater public trust and increased transparency through the application of AI. In this session, Retired Chief David Jantas from the Pemberton Township, NJ Police Department will share his experiences and best practices on how agencies are leveraging AI driven technology to not only help save costs and resources, but provide greater transparency to the public such as the release of redacted public records requests.
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      General Sessions

    • Join us for an open Ask an Expert Session with experts from Alegion
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      Expert Sessions

    • Six years ago BLS staff read and manually classified hundreds of thousands of written descriptions of work-related injuries and illnesses each year. Today, more than 85% of these classifications are assigned by a deep neural network that is more accurate, on average, than trained human workers. In this presentation, Alex will discuss how BLS addressed some of the many challenges inherent in this transition including how to build these systems, how to decide when and how to use them, and how to monitor and maintain them to continually improve performance.
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      Data Engineering
      Data Labeling
      General Sessions

    • In this session you will learn about how Veritone uses AI to route energy like data, stabilize the grid, and make green energy the efficient, low-cost source it was meant to be. You will understand the key components to Veritone’s patented energy automation solution: demand forecaster, energy optimizer, device controller, and energy arbitrage.
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      Data Engineering
      General Sessions
      Sourcing Data

    • The COVID-19 pandemic has dramatically expanded telehealth services and, with it, highlighted the need to share analytics across networks without moving data.The promise of applying Artificial Intelligence (AI) to all of this data for more personalized and wholistic diagnosis and treatment is threatened by communications challenges and eroding patient trust. In this presentation, we will explore how sharing analytics rather than data solves these problems and more.
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      Data Engineering
      Data Preparation
      General Sessions

    • Day 4

      Thu. Sep. 17, 2020

    • AI has the capability to leverage large amounts of data to make predictions and do analysis. This capability will be especially useful to address the COVID-19 pandemic, with the ability to conduct early disease monitoring to predict future outbreaks and control further spread. In this session, hear from panelists and learn about how MTX Group, Inc. is leveraging Maverick AI and the Data Lake concept to help states control the pandemic.
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      Data Engineering

    • AI is here and is already having a massive impact on public and private sector organizations. There is a huge cost financially, operationally, and competitively for not being prepared. In this session, MTX Group, Inc. will discuss the Maverick AI platform and how we work with public and private sector organizations to understand, prepare for, and implement AI solutions
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      General Sessions

    • Since his co-authored Harvard Business Review article and Only Humans Need Apply book in 2015, Tom Davenport has argued that augmentation is a more likely and more desirable outcome for organizations and people than large-scale automation. Over the last year he’s been gathering detailed examples of human workers who toil alongside smart machines. In this presentation he’ll describe what “the future of work now” looks like and how people, organizations, and machines will need to change to facilitate this outcome.
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      General Sessions

    • AI has fast emerged as a top issue for policymakers at the highest levels of government in Washington, DC. Jeremy Bash will discuss macro trends affecting AI in the U.S.and why policymakers must strengthen domestic AI capabilities, ensure that trust and bias prevention are enshrined in AI platforms, and inspire the next generation of AI engineers.
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      General Sessions

    • In this keynote Jose Arrieta, Former CIO and acting CDO at the United States Department of Health & Human Services (HHS) will share his experiences dealing with the cultural change involved with AI, data, and transformative technology. He will share how as CIO and interim CDO he dealt with key issues around data security, data privacy, data transparency, and sharing data with integrity. He will share how AI and data force organizations and agencies to focus on those topics, and why it’s so important to have meaningful discussions around these topics.
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      Data Engineering
      Data Preparation
      General Sessions

    • In this presentation, Brian is going to share the key reasons for why even with all the projections that AI s going to transform businesses and industries that for leading AI teams, this is happening more slowly than most of us thought it would. Teams are hitting major roadblocks along the way and the reason is because getting high-quality labeled data and data management are at the heart of the problem.
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      Data Labeling

    • Organization contact centers face new challenges during the pandemic as they grapple with spikes in demand, a remote workforce, and compliance concerns. These challenges create opportunities for AI automation solutions that deploy digital workers to come alongside human workers and make their jobs easier.
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      General Sessions

    • Video annotation is the new frontier of data labeling, due to rapid advances in computer vision-based machine learning and AI. However, video data is still difficult to annotate because it is highly complex, both technically and in terms of the density of annotation. Managing the annotation pipeline for video data is much harder than with images. Video is dense, hard to manipulate, and generally more difficult to make something useful out of. At Alegion, we have built a powerful, efficient, and comprehensive solution for video annotation that empowers teams to solve their largest and most complex computer vision problems.
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      Data Labeling
      Data Preparation
      General Sessions

    • It’s no secret that companies with an AI focus have been receiving lots of funding these days. It’s always interesting to get venture capitalists (VCs) perspectives on the market and how new technologies are trending. In this panel we will discuss what thecurrent climate is for AI companies looking for funding, and what makes companies stand out to investors
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      General Sessions

    • By increasing data-use and literacy to improve the efficiency and effectiveness of decisions, readiness, mission operations, and cybersecurity, we are changing the Air Force’s culture to be a more collaborative organization. The Air Force is facing an ever-more disruptive battlefield (i.e., information warfare, malicious cyber activity, and political information subversion). Combating these threats requires rapid advancements in our data. To ensure we have the big data necessary to support AI autonomy, we need to take existing data stovepipes and change the culture toward visible and accessible data while still allowing for security and appropriate access.
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      General Sessions

    • LinkedIn serves close to 700 million members in more than 200 countries, and AI is woven into virtually every experience on the site. Last year alone, members viewed nearly 400 billion feed updates, and the rate of content creation on the site is rapidly expanding. To maximize a useful, personalized experience, LinkedIn uses AI to customize things like a user's feed, job notifications, and learning content.
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      General Sessions

    • Day 5

      Fri. Sep. 18, 2020

    • When using customer data for machine learning models, there are some considerations around data privacy and ethics of data collection, storage, and usage of that data. Companies have long treated data as assets, so it should come as no surprise that they take the use of this data seriously. Organizations are increasingly establishing rigorous governance processes around their data management. This panel will explore how various industries and companies are approaching the challenge of data privacy & ethics for AI.
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      General Sessions

    • While many leaders have called data as the ‘fuel’ for advanced analytics and AI, Shiv feels it is more than just the fuel. Fuel can be replaced, but good data is non-negotiable. A big business decision that is built using flawed data, can make an organization lose more than just millions or in some cases billions of dollars in revenue. Hence, Shiv likes to think of an accurate data set as the ‘Oxygen’ for any good statistical analysis. Join him in learning more, especially as Shiv shares how having the right data led to several winning business decisions at some of the best known Fortune 500 brands.
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      Data Engineering
      General Sessions

    • One of the seven patterns of AI, predictive analytics is being used in many areas of government to help humans make better decisions. Some examples of this pattern being applied include assisted search and retrieval, predicting some future value for data, predicting behavior, predicting failure, giving advice, and intelligent navigation. The idea is that it helps to make better decisions, providing augmented intelligence capabilities. Machine learning is what is helping to make the decision, adapting over time to provide better results.In this panel we’ll explore how various agencies are successfully applying the predictive analytics pattern of AI.
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      General Sessions

    • Join us for an open Ask an Expert Session with experts from Labelbox
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      Expert Sessions

    • Today, more than ever, decisions are informed by data, and increasingly by digital technology that uses data in new and sometimes unexplainable ways. Everything produces data. Even data begets data. Dr. Anthony Scriffignano, SVP/Chief Data Scientist at Dun and Bradstreet, will discuss some of the ways in which hyperdisruption is challenging business leaders to think differently about making decisions with data. He will share best practices, discuss challenges and future risks, and explore models for problem and opportunity formulation that are helpful in this kinetic environment.
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      General Sessions

    • Conventional vegetation management by power utilities has been based on 3-year revisit scheduling and largely ground-based monitoring. These efforts have not been minor-utilities spend rather many millions of dollars per year. But recent events in California have made it clear that they are completely insufficient. At OmniSci, we have developed an alternative solution based on continuous monitoring, data, and fire science. Our system integrates near real-time satellite, LIDAR, and weather and micro-demographics data in order to assess risk dynamically.
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      Data Engineering

    • Join us for an open Ask an Expert Session with experts from Veritone
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      Expert Sessions

    • What if you could turn your organization’s vast store of documents into something useful? What if you could mine all that latent knowledge? With Azure Cognitive Search you can leverage all that content and make it instantly accessible, searchable, and actionable.
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      Data Preparation
      General Sessions

    • At Morningstar, we’re leveraging Machine Learning to collect financial data on many different instrument types across global markets. A task that had been executed manually in the past is now becoming highly optimized to deliver scale and quality by leveraging Machine Learning. We’re building a self-sustaining model improvement lifecycle that includes automated continuous feedback collection, retraining and deployment.
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      Data Engineering
      Data Preparation
      Sourcing Data

    • Presenting an overview of DataRobot's MLOps to monitor and manage your machine learning models - what works, what doesn't, and what to do about it.
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      Data Engineering

    • Learn how data and machine learning are driving innovation across agencies.
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      General Sessions

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