Registration and breakfast
Main Stage: Chair's Opening Remarks
Main Stage Opening Keynote: The current market trends and where we are heading in the future
Join us on the main stage for:
- Foundations of data and data processing
- Organizational challenges in data and analytics
- An application of ML for industry
Main Stage: How to monetize your data
- Macro view on AI, the big picture
- What AI means for potential business growth, examples & use cases
- Lessons learned through the definition of the Artificial Intelligence strategy
Main Stage Panel: Data methods, ethics, doubts and future Q&A
- 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
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?
Leadership at Grid Dynamics
Running a successful department to contribute to bottom line
Our panellists will discuss how your business can be more data driven.
Answering questions like what does a future-proof data collection strategy look like?
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.
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.
Data strategy: Past and future Application and implications
. How do you create and define your data strategy?
2. How has the implementation of data/machine learning impacted your business as a culture and was there any barriers that you need to remove?
3. Adopting new technology: Is there any software, hardware or external assistance that helps your day to day role.
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.
How using cloud-based infrastructure can significantly boost a Presidential campaign
During the 2012 election, Obama’s
tech team constructed, utilized, and scaled up all of their applications on AWS. As a result, the campaign ran 200 apps and supported millions of users — all while avoiding a costly IT investment...tens of millions of dollars to be exact.
Networking drinks ends at 6.15pm.
Registration and breakfast
Registration and breakfast
Chair opens: Menti morning
Creating an unforgettable customer experience
- Intelligent real time data applications are a game changer in any industry.
- In the transactional fraud detection industry, we have to make approve or decline decisions within 100 milliseconds.
- How can you manage machine learning model artefacts, data & feature engineering, model & rule execution and verification, continuous deployment, monitoring & analytics and more
Business Capability Architecture is the tie that binds all
I will introduce a metamodel for any big data initiative called the Business Capability Architecture. This is the missing piece in most big data and data lake initiatives in large organizations. Once implemented as the “tie that binds all” data, you will improve your effectiveness and efficiency to generate actionable insights. I will also give examples of where this is being used, in varying degrees, today
Making your“Product Management for AI analytics system simple
Explore particular challenges PMs face when dealing with AI, like setting user expectations, working with messy data and embracing probabilistic and imperfect results.
Panel: Creating a data culture
Creating the perfect marketing campaign using analytics
- There is a paradigm shift around the corner with how data will be used by Businesses.
- Self-served analytics will power Business Units from within rather than executed and deployed by Data Scientists.
- Data Scientists will need to ramp up their business skills if they want to stay relevant.
Experimentation @ DoorDash
Lessons learned about how to run experiments and make informed business decisions leveraging data.
Fireside chat: A Holistic view of your customers
- Understanding time series forecasting
- Machine learning models for time series forecasting
- How to implement the models using open source libraries
Drivers of Growth
- How does audience analytics solve the disconnect
- Focusing on consumer actions
- Sharing data across departments
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