Day 1 - 13 November 2019
Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks
Edge Processing for Data Analytics and Training AI Algorithms
- How the huge influx of data will require fit-for-purpose architecture. What is the distance from the edge to your device and how to consider this during the creation of your IOT / AI architecture?
- Discussing how IoT / AI architectures need to be put in place to ensure increased compatibility across domains.
- Using cloud analytics platforms to derive value from IoT / AI data vs physical gateways -pros and cons.
Keynote: Information is Everything
Data is the new oil is a familiar paradigm but until we learn to process and derive actionable insights from this data how valuable is it? This talk will cover a real life case study where a business has successfully taken data generated by the IoT and converted into into real business actions. Hear about their journey, and their recommendations for uncovering new economic structures made available by access to intelligent data
Leading the way into the new era of IoT analytics with AI
Businesses today are looking to leverage all types of data to promote a data-driven decision making culture for their customers as well as own organizations. Specifically in the Internet of Things (IoT) domain, the amount of data being generated from sensors, devices, equipment, and infrastructure is on a very rapid incline. As a result, there is a tremendous need for the use of analytics algorithms and methodologies along with embedded AI to tackle, understand, and process IoT data to derive meaningful business insights. This session will focus on key aspects of AI as it pertains to IoT and a few customer stories across these domains
Panel: IoT and AI Data analytics for intelligent decision making
- Identifying target-rich, high-value data that can be used to generate business intelligence
- Using cloud analytics platforms to derive value from IoT data
- Discussing the barriers to widespread IoT/ AI /Big Data value delivery and how these might be overcome.
- Real time data analytics in practice – examples of how IoT / AI data is creating business efficiency and revolutionising working practices
The collision course between Big Data+AI, Privacy, Ethics and Regulations in the IoT world
Afternoon Keynote: AI, Big Data and Autonomous vehicles
Panel: Big Data – Creating Intelligent Data Models
- The increased need for big data analytics to drive AI & Machine learning
- How to successfully unlock unstructured data & transform into learnable features
- The advancement of self-service big data tools & its benefit for your organisation
What is the state of Hadoop today?
NoSQL for big data analytics: Best practice and use cases
- NoSQL vs Hadoop vs SQL
- Enterprise implementations and use cases
- Advantages of horizontal vs vertical scalability
- Ensuring greater performance with larger data sets
Case study: How to get the most out of Apache Spark
- Moving from testing and proof-of-concept through to production applications
- The industries set to be impacted – financial, manufacturing, pharmaceutical
- Flexibility and adaptability in workloads