Day 1 - 4 November 2020

08:30AM

Registration, Coffee & Networking in the Exhibition Area

10:00AM

AI Technology & Implementation – Chair’s Opening Remarks

10:15AM

Presentation: Building a sustainable future with AI

  • Environmental applications of AI
  • How can businesses proactively meet the challenge of new AI applications?
  • How might AI influence economic growth?

10:45AM

Presentation: Practical AI implementation and application

  • Generating business value from your AI applications
  • Building a governance framework
  • Ensuring ethical AI
  • Lesson from real world deployments

11:05AM

Presentation: The new wave of AI hardware accelerators and what they mean for your organisation

  • Generating business value from your AI applications
  • Building a governance framework
  • Ensuring ethical AI
  • Lesson from real world deployments

11:25AM

Presentation: Taking the headache out of Data prep

Overcoming common data prep challenges with AI

11:45AM

Presentation: Untangling unstructured data

  • A look at analytical methods and tools analyze unstructured data.
  • Analyzing unstructured data with semantics and NLP
  • What part does Deep Learning play in identifying patterns in unstructured data?

12:05PM

Panel: Leveraging AI & ML in Data Analytics

  • Understand how machine learning and AI in data analytics can benefit your business
  • Incorporating modern machine learning techniques into your data infrastructure
  • Insights, examples and use cases

12:45PM

Networking Break

01:45PM

Presentation: Bridging the Gap between Data Science and Data Engineering

  • Enabling Data Scientists and Engineers to Work Collaboratively
  • Cross-training to both data scientists and engineers
  • Principles and practices that guide where applied data science technologies need to converge

02:05PM

Presentation: Automating workflows with BPM

  • Aligning processes with an organisation’s strategic goals
  • Designing and implementing process architectures
  • Establishing process measurement systems that align with organisational goals, and
  • Educating and organising managers so that they will manage processes effectively.

02:25PM

Presentation: Your next co-worker could be a robot

  • What does the workforce of tomorrow look like?
  • What’s the difference between a bot and a digital worker?
  • What roles can be automated?
  • Creating efficiencies and empowering the human workforce
  • Blending virtual and human employees

02:45PM

Presentation: Challenges and opportunities of distributed machine learning

  • How to develop ML models or training algorithms that are more suitable for distributed settings
  • How to build large-scale DML applications
  • Problems in distributed machine learning

03:05PM

Prashant Shindgikar

Principal Solution Architect of Cloud Big Data Platform

Macy's

Associated Talks:

03:05PM - Day 1

View Presentation: Building a feature engineering framework

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Presentation: Building a feature engineering framework

  • Why Feature Engineering Matters
  • What’s in my dataset
  •                 structured vs unstructured data
  •                 identifying missing values
  •                 Data Visualization
  • Features Construction
  • Features Selection and Transformation
  • Features Improvement and Learning with AI
  • Case Studies
. Prashant Shindgikar, Principal Solution Architect of Cloud Big Data Platform, Macy's

03:25PM

Presentation: Strategies for productionising ML models

  • How to deploy your model so that the business can benefit from the model and make better business decisions.
  • The role of MLOPS
  • Incorporating machine learning into production applications

03:45PM

Presentation: Potentials and pitfalls of Re-inforcement Learning

  • Basic concepts in reinforcement learning
  • Core challenges and approaches
  • Supervised vs. unsupervised vs. reinforcement learning
  • Achieving human level intelligence

04:05PM

Spotlight Session: Start-ups and innovators

This session will explore some of the latest technologies available within the AI, ML, DL technology ecosystem, and showcase how they are being used in the real world.

04:35PM

Panel: Team work makes the dream (model) work

  • Getting models in production so they are robust and strong
  • Building collaboration between Data Engineers and Data Scientists
  • Upskilling software engineers skills in the team

05:15PM

Chair’s Closing Remarks

Day 2 - 5 November 2020

08:30AM

Registration, Coffee & Networking in the Exhibition Area

10:30AM

AI Technology & Implementation Day 2 – Chair’s Opening Remarks

Quantum & Edge Computing

10:45AM

Presentation: The art of applying data science

  • Key disciplines needed for real-world applications
  • What organisational changes will you need to make?
  • What obstacles will you need to overcome
  • Getting over the last mile

11:15AM

Presentation: The next frontier – Quantum computing & AI

  • How does Quantum computing mean for AI?
  • Moving from the theoretical to practical.
  • What industries will be disrupted?

11:35AM

Presentation: Moving AI to the Edge

  • Will AI continue to move to the edge?
  • What are the benefits and drawbacks of AI at the edge versus AI in the cloud?
  • Centralized intelligence versus decentralized intelligence
  • Bridging the gap between the data center at the edge

11:55AM

Presentation: High Performance Computing

  • Why is it important?
  • What benefits can it bring? Can it save you money whilst streamlining business processes?
  • Use cases and examples

12:15PM

Panel: Succeeding with Intelligent Automation

  • Getting from task automation to end-to-end flow automation
  • How to overcome legacy systems
  • Dealing with outdated customer commitments
  • Democratising of automation of IA
  • Scaling IA and transforming business processes

12:55PM

Networking Break

Deep Learning

01:55PM

Presentation: Autonomous vehicles in 2020

  • Reflecting on the reality check of 2019 – what has the industry learnt?
  • What are current industry expectations moving forward?
  • What advanced driver-assistance systems can consumers expect to see in their cars?
  • Are we any closer to ‘robotaxis’ or fully driverless cars?

02:15PM

Presentation: The Rise of Auto ML

  • Common AutoML uses cases
  • How can AutoML make data science teams more productive
  • Democratising AI initiatives with Auto ML

02:35PM

Presentation: Unsupervised Learning

  • What benefits can be gained from unsupervised learning and why is it important?
  • How to apply techniques such as clustering, Anomaly detection, Association mining, Latent variable models

02:55PM

Presentation: Deep Learning in Computer Vision

  • A look at new applications of computer vision
  • New advances in algorithm and technology advances
  • Image classification and annotation
  • Object recognition, image search and detection techniques

03:15PM

Presentation: Designing for Voice

  • How do you create a seamless voice interface?
  • Design principals when designing for voice
  • Choosing the right speech recognition engine
  • Measuring performance

03:35PM

Panel: What’s next for Conversational AI?

  • Key advancements in NLP
  • Best practices for conversational design
  • Implementation strategies
  • Scale up so your chatbot is production ready

04:15PM

Chair’s Closing Remarks

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