Enterprise AI workshop

Jumpstart your knowledge of Enterprise AI – Workshop two full days – Saturdays/Remote

Only 10 places – Enterprise AI – 2-day workshop in September

Introduction

Launched for the first time, jointly delivered by Ajit Jaokar and Cheuk Ting Ho – Enterprise AI workshop is a two-day workshop targeting developers and strategists. Conducted in London on Saturdays in Sep and Oct in a conference room setting, the workshop has currently limited places. You can also participate remotely

Here is some more information

  • Our philosophy and approach to Enterprise AI is outlined below
  • Remote participation is possible for the live sessions
  • The sessions will be recorded
  • We may include more dates in future
  • Each session is two hours long – hence the workshop is a total of 16 hours for 8 sessions spanning 2 Saturdays
  • The workshop is ideal for strategists/ senior executives
  • We work with code through ‘code walkthroughs’ hence some knowledge of Python is good, but it is not mandatory. We can provide an overview session for Python/TensorFlow and Keras as an online video before the workshop
  • Our bios Ajit Jaokar (https://www.linkedin.com/in/ajitjaokar/) and Cheuk Ting Ho (https://www.linkedin.com/in/cheukting-ho/)
  • Workshop outline presented below. (Note It is subject to change)
  • Please contact ajit.jaokar at futuretext.com for pricing

 

 

Philosophy

The workshop is based on the following guidelines

Emphasis on the full AI pipeline

In this workshop, we explore the real world / large scale deployment of AI. The professional deployment of AI in Enterprises differs from the content in a typical training course. In larger organisations, the Data Science function typically spans three distinct roles: The Data Engineer, the Data Scientist and the DevOps Engineer. The Data Scientist is primarily responsible for developing the Machine Learning and Deep Learning algorithms. The Data Engineer and The DevOps Engineer roles work in conjunction with the Data Scientist to manage the product/service lifecycle.

To understand how these roles work together, it is important to understand the philosophy of DevOps. DevOps is concerned with managing rapid, frequent delivery which lends to the idea of Continuous Innovation and Continuous Delivery(CICD). DevOps creates a culture of increased collaboration, ownership and accountability enabling teams to create large-scale applications.

 

In this workshop, we take the approach of managing the AI pipeline using the philosophy of CICD. The approach suits a professional deployment of AI.

 

CICD (Continuous Improvement/Continuous Delivery). CI/CD can be seen as an evolution of Waterfall and Agile methodologies.

 

The business case for AI

Broadly, we can consider an AI process as one which improves with experience

The business case for Enterprise AI is a moving goalpost. It is driven by some of the considerations below which we discuss in the workshop

a)       Improving performance of existing business processes (ex: fraud detection, loan approval etc)

b)      Create a new business model / process

c)       Create barriers to entry by building superior models

d)      Develop IPR

e)      Bring non-transactional interaction in the Enterprise

f)        Re-engineering existing processes in the Enterprise

g)       Enhancing job functions where humans and AI can work together

 

Taking a pragmatic approach

The workshop takes a pragmatic approach to balance against the media hype

Competitive advantage of AI

It is no secret that AI creates competitive advantages and creates a ‘winner takes all’ situation.

Use case driven approach

We take a use case driven approach discussing deployment scenarios

How to build your own Enterprise AI portfolio/profile?

The overall focus is based on how you can build your own Enterprise AI focus/profile

 

Approach

Enterprise AI can be a vast topic. Considering the philosophy listed above, our approach involves taking a two-sided view.  We first cover the end-to-end implementation of two examples with code walkthroughs. We then consider these examples from an enterprise pipeline standpoint.

Session One: Introduction and Overview

Cover topics like

  • Why use AI in enterprises
  • Industry landscape of enterprise AI
  • Understanding different issues and strategies in Enterprise AI
  • Architectural design considerations for AI in enterprises
  • Understand what problems Enterprise AI solves
  • Understand the issues behind deploying Enterprise AI applications in scale for Enterprises
  • Study the strategies of large scale Enterprise AI vendors (Azure, Amazon and Google)
  • Use cases

 

Session Two: Unique considerations for Enterprise AI

Here, we cover specific drivers for Enterprise AI

  • Explainable AI
  • Productivity (AutoMl)
  • New regulatory structures responsible for AI ex GDPR, Payment regulation

 

Session Three: Understanding AI and the Enterprise

Enterprise AI does not exist in isolation. The word ‘Enterprise’ can be seen in terms of Enterprise workflows. We also consider the core Enterprise (a non-manufacturing company ex Insurance) and the Wider enterprise (including supply chain). This section is concerned with understanding how these workflows could change when AI is deployed in the Enterprise.  We first define Enterprise workflows using a typical ERP process ex from SAP as: Procure to Pay – Plan to Product – Order to Cash – Support customer-focused processes – Request to Service – Core Human Resources – Core Finance. We then explore how AI could potentially impact each of these functions both with the core and the wider Enterprise.

 

Session four: AI models used in the Enterprise

In this section, we study AI / Deep learning algorithms. These include:

  • Overview of Machine learning and Deep Learning
  • Deep-learning techniques. MLP CNN RNN Autoencoders

Session Five: End to End – Enterprise AI(Transactional)

Code walkthrough with a end to end example for Enterprise AI (Credit approval)

 

Session Six: End to End – Enterprise AI (Natural Language Processing)

Code walkthrough with a end to end example for Enterprise AI (Natural Language Processing)

Session seven: Deployment considerations

For the end to end examples discussed above, this session covers the deployment perspective considering CICD, DevOps etc (refer the Approach and Philosophy sections above)

Here we also cover the strategies for three vendors

ERP – SAP
Cloud – Microsoft
Data Integration – Talend

Session Eight: The Enterprise AI business case

This interactive session will explore the Business case for AI building on the above ideas. We also explore other areas such as Advances in Hardware, Expectations, Social Controversy and backlash, Need for Data, Limitations of Deep Learning, the war for Talent etc

 

Questions and contact details

Conducted in London on Saturdays in Sep and Oct in a conference room setting, the workshop has currently only limited places. You can also participate remotely

Please contact ajit.jaokar at futuretext.com for pricing and any other questions

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