Registration and breakfast
Main Stage: Chair's Opening Remarks
Main Stage Opening Keynote: Virtual Beings Not Virtual Assistants
Main Stage Panel: Spotlight on social media
- Best practice when dealing with large data sets
- How to upscale large ML models
- Understanding data ethics and privacy and how it might affect you in the future
Main Stage: Modern Day Technology
- What AI means for potential business growth, examples & use cases
- Lessons learned through the definition of the Artificial Intelligence strategy
Morning coffee break & networking
Generating real value from real time analytics
The value of data and its analysis is often highest in the moments after it’s created. Real-time analytic solutions unlock this value by driving data into real-time decision-making and business processes. Join this session to learn how Google Cloud and its Data Analytics Platform can help simplify your analytics workflows and allow your teams to focus on getting to insights from your data for your industry and your customers.
Evolution of Data Science at Reddit: from ML Insights to Experiment-Backed Data Products
Success of data science requires mastery of statistical/ML techniques, and deep understanding of company priorities. The most rewarding yet challenging aspect is to build a rhythm to deliver actionable ouputs (i.e., data products) that directly transform business decisions. Here in Reddit, we have uniquely complex data assets, for which it took us rounds of iterations to customize a solution that maximizes our data potential. This talk focuses on our recent journey in the last 18 months to build an experimentation platform, and to revamp the engineering workflow prioritizing the interplay of offline ML modeling and online testing. Not every piece of ML insights checks out in an experiment, but every "setback" is equally valuable as it allows us to iterate faster and more purposefully. Culturally speaking, we encourage calculated risk taking since data products are most powerful when they point to new business directions (vs. validating the existing ones). With a suite of concrete examples, we demonstrate how rhythmically outputting data products answers the common questions that almost all companies are curious about, namely, where are “good ideas” from? How to generate them at scale? Do they hold up IRL? Still valuable if they don’t?
How simulation is transforming advanced analytics
Your data management initiatives are finished. The analytics team is in place. Business users are satisfied with their visualization and BI tools. But what’s next? The answer: Simulation. This session shows how simulation addresses the complex dynamics of any market, including consumer behavior and competitors, to deliver trustworthy answers to business questions. Learn how leading organizations are integrating simulation into their advanced analytics stack to answer their what-if business questions.
With innovative launchpad presentations from:
Nico Rode, TIBCO
Wade Tibke, Sigma Computing
Michael McNair, Codey.ai & 55B Labs
Enterprise-Scale Innovation that Delivers Business Results
The presentation will focus on best practices to develop ML powered applications that can move the needle on business critical KPIs. We will walk through a rapid prototyping framework to develop effective personalization experiences, the mindsets and skills required to execute on an innovation roadmap, how to evaluate and work with vendors that provide 'AI-powered' solutions, and how to design experiments to quickly iterate towards a better experience for customers.
Building a Data Driven Organization in 3 Easy Steps and 1,645,242 Less Easy Ones
Data is all around us and is a driving goal for many organizations, but while the goal may be in sight, the steps needed to achieve data driven results are not early as clear. Trying to do too much at once or being pulled in to many directions is a potential roadblock to getting good repeatable results that allow you to gain solid business insights. This presentation hopes to provide a roadmap that outlines the high level steps needed to implement data driven objectives as well as provide a ground up view of some of the hiccups and hurdles that we as implementers face when dealing with senior leaders.
Panel: Getting closer to your customer: Data methods, doubts and future Q&A
1. How are enterprises bringing all the customer data together to deliver the desired customer experience?
2. How do you pick the right systems to deliver the greatest impact for your business, as applied over your data?
3. What are the best practices along the way?
4. What are some of your pitfalls? How do you avoid the pitfalls?
5. How could you get closer to the customer and a region with analytics
6. What type of skills/team is required to implement customer-centric data Strategy?
7. How will ML improve business decision making as well as customer experience?
8. How are companies addressing the privacy concerns?
Afternoon coffee and networking break
Why successful industrial analytics system needs automated AI/ML
Despite the breakneck pace of innovation, it isn’t often that we in the technology sector come across a technology as transformational as the Internet of Things (IoT). There are many research reports and real-world evidence indicating that IoT will transform our personal, public, and vocational experiences with efficiency gains and automated business processes, as well as optimized decisions. But, extracting these benefits require close interaction between three separate skill sets - the domain expert, the data scientist and the programmer. A typical business user rarely has all these three skills. Therefore, the only way to deliver the benefits of connected experience in most business and personal context is via Automated AI/ML which includes automating model selection, tuning, feature selection, data preparation, accuracy tracking and user knowledge adoption. For the domain user, the analytics system should be simple to setup, easy to understand to act on AI/ML based recommendations. In this talk, we will discuss the design of automated AI/ML in industrial applications with examples of Oracle IoT applications and discuss several customer use cases of how the automated system delivers anomaly detection, predictions and recommendations.
The next generation of Big Data platforms for advanced analytics - 100s of PetaBytes with real-time access
Building a reliable Big Data platform is extremely challenging when it has to store and serve 100s of PetaBytes of data in a real-time fashion. This talk reflects on the challenges faced and proposes architectural solutions to scale a Big Data Platform to ingest, store, and serve 100+ PB of data with minute level latency while efficiently utilizing the hardware and meeting the security needs.
In this talk, we'll dive into the technical aspects of how the ingestion platform can be re-architected to bring in 10+ trillion events/day at minute-level latency, how the storage platform can be scaled, and how the processing platform can be redesigned to efficiently serve millions of queries and jobs/day. We will provide a behind-the-scenes look at the current Big data technology landscape, including various existing open-source technologies (e.g. Hadoop, Spark, Hive, Presto, Kafka, Avro, Parquet) as well as what we had to build at Uber and open-source to fill the gaps and push the boundaries such as Hudi and Marmaray.
The audience will leave the talk with greater insight into how things work in an extensible modern Big Data platform and will be inspired to re-envision their own data platform to make it more generic and flexible for future new requirements.
Maximizing Talent: Getting the Most Out of Your Tech Teams
As AI continues to grow in popularity, the challenges of leading a tech team impacts nearly every industry, including Education, Finance, and Healthcare. Talent is not only hard to find, but leading a dynamic and complex group of experts and specialist can seem daunting. In this talk , we will unlock the keys to successfully leading a tech team from the perspective of great technical leader in science and technology.
Networking drinks ends at 6.30pm.
Registration and breakfast
Registration and breakfast
Round Table Discussion: Tapping Unstructured Data for Insights & Analytics: Business Imperatives & Success Factors
This roundtable will address the universal need to manage, integrate and leverage for business decision the torrents of unstructured data accumulating daily at the enterprise level. We’ll assess and discuss platforms, approaches, use cases, experiences, work arounds and new research and insights from deep learning techniques.
When Can We Trust a Decision Made by a Machine: Building Trustable AI and Detecting Misinformation
With the advent of machine and deep learning, explainability and interpretability has become paramount to traceable and justifiable and explainable results:
when someone's house gets foreclosed, some one is sentenced, an insurance claim is denied, a mortgage application is denied. There are legal, human, organizational, IP and societal implications of leveraging the augmented intelligence of machines in supporting highly complex and previously only open to highly educated, highly skilled experts in medicine, underwriting, law, adjudication, etc.
The need to create and train unbiased or minimally biased datasets for deep learning in the interests of Fairness, Accessibility and Transparency requires best practices that are not known to organizations embarking on machine learning and AI activities.
In this session we will cover the methods and techniques and best practices to detect, manage and mitigate bias in datasets, deep neural network training, application integration. We will also explore the cognitive biases that lead us to curate date to create less than trustworthy blackbox AI systems, and how to avoid them.
We will explore in detail (code level as well as architecture), the project AlternusVera which detects fakeness or deliberate misinformation in a body of text.
Magic Dust for Artificial Intelligence Product Management; specific skills, techniques, attitudes and responsibilities for PMs when it comes to AI-driven products.
Explore particular challenges PMs face when dealing with AI, like setting user expectations, working with messy data and embracing probabilistic and imperfect results.
How Data and Machine Learning enable Creatives and Storyteller
Morning coffee break & networking
A New Ecosystem Approach to Improve Data Science Success
- The dynamic nature of deep learning methods
- Creating greater personalisation of customer analytics
- Improving accuracy and performance in applications
Creating Iconic Viral Trends: A Look Into the $500m + Fidget Spinner Phenomenon
Creating a marketing campaign that “goes viral” is the goal of every marketer. With product marketing, it makes that goal even more satiable. However, the approach to product marketing goes much deeper than the sales enablement and marketing campaigns. The key doesn't lie in pay-to-post Facebook ads, algorithms and various other marketing nuances geared to track SEO numbers and customer engagement. Most companies don't understand how much that hurts their marketing. With in-your-face sales ads now being a thing of the past, consumers just want more. The secret to creating a campaign that is going to 'go viral?', isn't accidental or methodical, viral trends are about depth. Maneuver your product positioning, emotionally connect with your target consumer through content marketing and events, to effectively build your community.
Experimentation @ DoorDash
Lessons learned about how to run experiments and make informed business decisions leveraging data.
With innovative launchpad presentations from:
Danielle Deibler, Marvelous.ai
Ted Benson, Instabase
Pat Giblin, 451 degrees
Building the Cities of the Future: Smart Cities, Startups and Minimum Viable Policies
Thoughtful integration of city planning, smart cities technology and policy development that puts people first is critical for governments and societies to adapt and thrive in an AI-powered world. As major cities around the globe continue to experience population growth, city governments will face increasing challenges to meet the needs of its communities. Advances in artificial intelligence are expected to replace millions of jobs and breakthrough technologies are being deployed faster than public policies, posing both incredible opportunities and unanticipated consequences on society. Breaking data silos across city departments and municipalities, cultivating a data-driven culture and creating a data-skilled workforce within government, as well as building strategic partnerships with the Govtech community, are key to successfully leveraging the full potential of data analytics to help to build a better foundation for the cities of the future.
Data Strategies for Enterprise: Model Risk; Operationalisation of Data Insight
- Innovations in the industry
- Dealing with large amounts of data
- Understanding the advanced database platform
Main stage: DATAx Start-up Showcase
Main stage: DATAx Start-up Showcase ends at 5.00pm.