Enterprise AI Data Scientist: Implementing Enterprise AI Course



Enterprise AI Data Scientist is a niche course that targets developers who want to transition their career towards Enterprise AI

The course covers:

  • Design of Enterprise AI
  • Technology foundations of Enterprise AI systems
  • Specific AI use cases
  • Development of AI services
  • Deployment and Business models

The course targets developers and Architects who want to transition their career to AI. The course correlates the new AI ideas with familiar concepts like ERP, Data warehousing etc and helps to make the transition easier,


According to Deloitte: by the “end of 2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise.

The Enterprise AI Layer

The course is based on a logical concept called an  ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are domain specific for an Enterprise.  While this is not as ‘sexy’ as the original vision of AI, it provides tangible and practical benefits to companies. We could see such a layer as an extension to the Data Warehouse or the ERP system(an Intelligent Data Warehouse/ Cognitive ERP system). Thus, the approach provides tangible and practical benefits for the Enterprise with a clear business model. For instance,  an organization would transcribe call centre agents’ interactions with customers create a more intelligent workflow, bot etc using Deep learning algorithms.


So, if we imagine such a conceptual AI layer for the enterprise, what does it mean in terms of new services that can be offered by an Enterprise?  Here are some examples

  • Bots : Bots are a great example of the use of AI to automate repetitive tasks like scheduling meetings. Bots are often the starting point of engagement for AI especially in Retail and Financial services
  • Inferring from textual/voice narrative:  Security applications to detect suspicious behaviour, Algorithms that  can draw connections between how patients describe their symptoms etc
  • Detecting patterns from vast amounts of data: Using log files to predict future failures, predicting cyberseurity attacks etc
  • Creating a knowledge base from large datasets: for example an AI program that can read all of Wikipedia or Github.
  • Creating content on scale: Using Robots to replace Writers or even to compose Pop songs
  • Predicting future workflows: Using existing patterns to predict future workflows
  • Mass personalization:  in advertising
  • Video and image analytics: Collision Avoidance for Drones, Autonomous vehicles, Agricultural Crop Health Analysis etc


These  applications provide competitive advantage, Differentiation, Customer loyalty and  mass personalization for any Enterprise. They have simple business models (such as deployed as premium features /new products /cost reduction )


Course Outline

AI – A conceptual Overview

In this section, we cover the basics of AI and Deep learning. We start with machine learning concepts and relate how Deep Learning/AI fits with them.  We explore the workings of Algorithms and the various technologies underpinning AI. AI enables computers to do some things better than humans especially when it comes to finding insights from large amounts of Unstructured or semi-structured data. Technologies like Machine learning , Natural language processing (NLP) , Speech recognition, and computer vision drive the AI layer. More specifically, AI applies to an algorithm which is learning on its own. We explore the design and principles behind these Algorithms.


Understanding the Enterprise AI Technology Landscape

In this section, we focus on various implementations of Machine Learning and Deep Learning including   : linear models(GLM) , Ensembles (ex: Random Forest etc), Clustering(k-means), Deep neural networks(Autoencodes, CNNs, RNNs), Dimensionality reduction(PCA). We are cover the various Deep learning libraries i.e. TensorFlow, Caffe, mxnet, Theano, We also discuss the ancillary technologies like  Natural Language Processing Computer Vision

Enterprise AI Use Cases

Here, we discuss the following use cases

  • Healthcare
  • Insurance
  • Adtech
  • Fraud detection
  • Anomaly detection
  • Churn, classification
  • Customer analytics
  • Natural Language Processing, Bots and Virtual Assistants

Implementing Enterprise AI

Building on the above, we discuss the implementations of the use cases.

Deploying Enterprise AI

Here, we cover the actual deployment issues for Enterprise AI including

  • Acquiring Data and Training the Algorithm
  • Processing and hardware considerations
  • Business Models
  • High Performance Computing – Scaling and AI system
  • Costing  an AI system
  • Creating a competitive advantage from AI
  • Industry Barriers for AI


Course Logistics

Where:  London

When:  Jan 2017

Duration: Approximately six months

Online?:  Yes . Please contacts us

Contact :  info@futuretext.com

Fees:         contact us

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