The call for presentations has been extended until July 14th.
If you’ve got something interesting to share, whether that’s a unique use case or a new approach to the application of Machine Learning to deliver increased visibility, efficiency, or insight into Business and Operations, please consider submitting a talk. The last day to submit presentations is July 14, 2017.
We’re accepting a broad spectrum of talks, with topics including but not limited to:
The modern enterprise is highly automated and uses machine learning to optimize the operations of its devices, networks and applications.
Some example topics are:
- Decision Making Case Studies – Experience reports, and pearls
- Pipelines – Solving for lagging to leading indicators in real-time
- Ecosystems – Designs, implementations, and deployment of tools and mechanisms for improving visibility, efficiency, or insight
- Patterns, Algorithms, Methods – Correlation, multidimensional analysis, attribution, causality, anomaly detection, auto-failure detection, self-healing, model management/deployment
- Future Opportunities – Unsolved, unthought, unknown, how to make consideration for the “unknown unknowns”
Artificial intelligence and machine learning are helping to create product experiences that improve people’s lives. In this track, we will discuss how artificial intelligence and machine learning are bringing advanced features into products, how it is enabling new ways to interact, and how it is anticipating customers’ needs better than ever. We invite speakers to discuss specifics on how AI is a differentiator for the product strategy and user experience.
Talks can cover a variety of product areas, including but not limited to:
- Conversational interfaces
- Notifications and feeds
- Smart cameras
- Content discovery and recommendations
- Personal assistants and chatbots
Predictive models go beyond the spectrum of data science team. They are consumed across platforms by various stakeholders within and outside the organization. We are looking for use cases on the concept of creating data science as a service by leveraging data assets. Topics can be around innovative data science deployments, integrations and management processes.
Content can include and is not limited to:
- Deep learning
- Data science platforms
- Natural Language Processing
- Structured Streaming
- Optimization and recommendation engines
- Image recognition and automation of workloads
Solutions presented should be deployed in an active environment with scale. Presentations should showcase organizational context, challenges and how the solution proved to be effective, lessons learned during the process, and any benchmarking results showing benefit. The expectation is to help the audience with their learning growth and potential implementation.