Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

In-Person Training
AI for managers

Angie Ma (ASI Data Science), Jonny Howell (ASI Data Science), Emanuele Moscato (ASI Data Science)
9:00–17:00
Monday, 8 October through Tuesday, 9 October
Location: Hilton Meeting room 3/4
Secondary topics:  AI in the Enterprise
Average rating: **...
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Participants should plan to attend both days of this 2-day training course. Platinum and Training passes do not include access to tutorials on Tuesday.

Angie Ma and Jonny Howell offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.

What you'll learn, and how you can apply it

  • Explore key AI and machine learning concepts and techniques

Angie Ma and Jonny Howell offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn’t) possible with these exciting new tools and how they can benefit your organization. You’ll learn a language and framework to talk to both technical experts and executives in order to better oversee the practical application of data science in your organization.

Outline

Introduction to AI

  • Course objectives: AI for everyone
  • Definitions: AI, machine learning (including deep learning, reinforcement learning), data analytics, customer science, etc.
  • Historical context: AI past and present
  • Present-day usage
  • AI within the corporate context
  • Significance and urgency: Why now, and why you?

AI in industry today: Practical applications and benchmark leaders

  • A selection of practical applications
  • Strategic approaches: Commissioning versus procurement (build or buy?)
  • AI maturity models and the enabling preconditions within your organization to use AI well (data, skills, tools, etc.)

Data in your organization: What are your raw materials?

  • What data assets do you have, where does it live, who owns those systems, where are the gaps, and how could you fill them (e.g., by buying data or installing sensors)?
  • What skills do you have access to from your part of the business, and what gaps might there be?
  • What tools do you use, where are the gaps, what other options are there?

Project selection: What makes a good AI use case?

  • Overview of the four key criteria for successful project selection and how to assess them
  • Group exercise: Create a project longlist (often categorized broadly within marketing, operational efficiency, operational effectiveness, and commercial optimization)
  • Scoring longlist against key criteria and plenary discussion

Managing an AI project: How is it is different from other projects?

  • Similarities and differences
  • Project lifecycle and timescales
  • Project delivery methodology
  • Typical staffing profiles
  • Cost estimates for external suppliers (for data science as a service and for consulting)
  • Performance metrics
  • Operations and maintenance
  • Governance and risk management

Leading or supporting a data transformation

  • What is data transformation (as opposed to gradual reform), and when might it be
  • necessary?
  • The ABCs of data transformation
  • Common pitfalls facing successful data transformation initiatives
  • Different operational models for data science within a modern organization
  • Next steps

About your instructors

Photo of Angie Ma

Angie Ma is cofounder and COO of ASI Data Science, a London-based AI tech startup that offers data science as a service, which has completed more than 120 commercial data science projects in multiple industries and sectors and is regarded as the EMEA-based leader in data science. Angie is passionate about real-world applications of machine learning that generate business value for companies and organizations and has experience delivering complex projects from prototyping to implementation. A physicist by training, Angie was previously a researcher in nanotechnology working on developing optical detection for medical diagnostics.

Photo of Jonny Howell

Jonny Howell is an associate on ASI Data Science’s consulting team. Previously, he was an innovation consultant at Santander UK, where he led the creation and implementation of the company’s data science strategy, a central part of the COO’s digital transformation program. Jonny has worked on a variety of different data science projects across multiple industries, from engagements focused on increasing operational efficiency to identifying new business opportunities. He holds a law degree from Bristol University. In his spare time, he loves playing the guitar. In a different life, he supported the one-hit wonder Toploader.

After finishing my PhD I attended the ASI Data Science Fellowship in data science and engineering and was subsequently hired by ASI with the double role of data scientist and developer advocate for the SherlockML platform.

Conference registration

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Comments

Zaher Sharifi | ANALYTICS LEAD
8/10/2018 17:17 BST

Hi,
we would like to have slides shared with us.
Thanks