AI Architecture : Computer Vision

AI is becoming popular in real life. Many applications are using computer vision by implementing Convolutional Neural network algorithms. Agriculture apps are using CNN based techniques to analyze the crop images for  crop’s health and viability of seeds. Self driving cars are using them in moving car and other vehicle detection and classification. Video analysis software uses CNN for finding the automobiles, road blocks, and human beings on the road. Security software also uses it for crime and violence detection. Breast Cancer, pneumonia and other diseases can be diagnosed based on the medical images by using CNN algorithms.

CNN technique consists of two steps convolution and pooling. These steps help in image reduction to basic features for image classification. Convolution helps in viewing the image in breaking it into small images. A CNN can have multiple convolution and activation layers. Convolution layer acts like a filter  by applying dot product of the actual pixel input values and weights  assigned. The sum of the output is used for filtering the image pixels. Activation layer which is part of CNN creates a matrix  smaller than the actual image. The matrix is executed using activation layer which helps training the network by back propagation algorithm. Activation layer uses the function ReLu. Pooling step helps in filter size reduction and downsampling.  Max pooling is the term for filtering the results of the last layer.  Pooling helps in training the network using all features of the image.  Fully connected layer in a Convolutional Neural Network is a multilayer perceptron. The input for fully connected layer is a one dimensional vector. One Dimensional vector is the result of the last layer. The output  is a set of probabilities of different feature labels. Each label represents the class and the one with highest probability will be the classification decision.

Enterprise AI has neural network techniques such as ANN, CNN, and RNN. Machine learning algorithms use neural network methods for data analysis and predictive analytics. AI Architecture will have machine learning components and neural network algorithms.  For an Enterprise Architect, AI Architecture skills  will be very important to create AI Architecture Practice within the organization.Artificial Intelligence  Architecture certification adds a step in career path of Enterprise Architect. AI Architecture  creation is an important skill needed for a qualified Enterprise Architect.

AI architecture has key factors such as the selection of machine learning frameworks and scalable solutions for automation. The AI reference architecture typically shows a workflow for automation solutions. Many AI frameworks such as Google Tensor Flow,IBM Watson, Scipy, Azure ML, Keras, Google AI, NTLK, Pytorch, and AWS Sage Maker are evolving and changing features rapidly. The AI architecture needs to have the flexibility and adaptability of handling the change. AI architecture helps in scaling, delivering speed and automating processes in the organization.

The AI architecture course explains the machine learning workflows and features such as the following feature derivation, model training, data analytics, data collation, data analysis & selection, project packaging, machine learning model tuning, evaluation, model inference, model validation, and deployment. The course will help in architecting AI applications for the below:

  • Recommendation Engine
  • Forecasting Algorithms
  • Document,Video and Image Analysis
  • Text analytics
  • voice to text, speech recognition
  • search engine
  • NLP/NLU, Conversational agents and Intelligent Assistants
  • Deep Learning, Knowledge studio and machine learning
  • knowledge mining, cognitive search and decision-making applications

In daily life, we come across many applications while working with customers and enterprises. The typical use cases where AI Architecture will help are:

  • Spam & Email – Filtering & User preferences based content analysis
  • Predictive Analytics – Credit Worthiness and Loan Applications
  • OCR : Pattern Recognition – Text, Images, Video and Audio
  • Biometrics: Identity Management & Security
  • Machine Learning Models: Life Insurance – Mortality rates, life expectancy
  • Medical Expense Prediction Model: patient history & medical claim history
  • Coverage Risk model: Liability & Property Insurance
  • Fraud Detection: Credit Card usage and activity patterns
  • Social Network Analysis: Relationship & Influence Analysis

Ecommerce websites use AI techniques and methods in their implementation . They have the below features related to AI:

  • Historical data related to customer transactions analysed for customer demographics
  • Shopping carts of the customer analysed for abandoned
  • Price analysis of the products using the historical data
  • Next Best action for the customer based on his preferences and previous purchases
  • Web page analytics related to customer browsing time for a product
  • Customer information related to profile, billing, and shipping addresses analysed for demographics 
  • Referral websites tracked by the customer views and click stream analysis
  • Patterns related to customer rating and reviews of the products
  • Marketing campaign effectiveness based on email, sms and web channels
  • Recommendations based on customer history related to browsing, usage and behavior.
  • Conversion of the shopping from view to a buy – analysis 

The recommendations of the customer and the merchant to the customer are analysed using various approaches mentioned below:

  • Collaborative Filtering
  • Content based Filtering
  • Train Matchbox Recommendation
  • Score Matchbox Recommendation

AI Modeling and Architectural development involves identifying modeling techniques, selecting algorithms, designing tests, developing models, assessing models and training the models. The other methods like Ensemble techniques help in combining and selecting multiple approaches based on scenarios. The AI model is validated and tested before using for unseen scenarios.

Enterprises are keen to evaluate AI & Machine learning techniques and develop models for decision making using Data science and algorithms. Leadership in enterprise is interested in getting their Architects trained based on experiential learning and avoid failures by using reference architecture and patterns & anti patterns. RPA is another area which enterprises want to evaluate and implement in the enterprise with AI & Machine learning, Voice and Natural language processing algorithms. Leadership is interested to know domain specific use cases where RPA is successful.

IASA Architect- AI Architecture Training Program is a basic course related to AI Enterprise architecture. This program is a defined baseline for successful IT architects who are implementing AI in enterprises.This initiative involves the advancement of best practices and education while delivering AI Enterprise programs and services to IT architects of all levels around the world.

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