IASA Global was established in 2002. IASA is an international, non-profit business association dedicated to the advancement and sharing of issues related to software architecture in the enterprise, product, education and government sectors. They are committed to improving the quality of the IT architecture industry by developing and delivering standards, education programs and developing accreditation programs and services that optimize the development of the architecture profession. IASA Global has created the world’s first and only ITABoK (IT Architecture Body of Knowledge) that contains 250 skill sets that are critical for every Business and IT professionals to possess in order to deliver strategic values of technology for the business.
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.
The AI Architecture certification helps in ensuring that you are on the Enterprise Architect path. This demonstrates that you are taking the necessary steps to become a fully qualified architect to create an AI architecture. AI architecture has important 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 Tensor Flow, Keras, NTLK, Pytorch, Google AI, IBM Watson, Microsoft Azure ML, 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 capabilities such as feature extraction, training, analytics, data collection, data analysis, data selection, project packaging, model tuning, evaluation, inference, validation, and deployment. The course will help in architecting AI applications for Recommendation, Forecast, Video Analysis, Image Analysis, text analytics, document analysis, voice to text, speech recognition, search, document analysis, conversational agents, translation, intelligent assistants, and transcription. NLP/NLU. Deep Learning, Knowledge studio, data refinery, IoT Platform, machine learning, natural language classifier, knowledge mining, cognitive search, decision-making applications, bots, robotic process automation.
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.
Course Outline
The Iasa AI Architecture Course for IT Architects gives you an architect knowledge of Artificial Intelligence frameworks and tools for developing AI Enterprise IT architectures that meet the demands of modern business. The curriculum is developed by AI Enterprise architects for IT architects, which is one of the basic ideas behind Iasa. The course focuses on:
- Data Requirements: discovering and understanding the demands and needs of the business.
- Data Modeling Principles: The principles followed by AI Architects related to data modeling.
- Business Case: To find the return on the investment and justify the need for AI Architecture.
- Machine Learning Solutions: Machine learning solutions for AI Architectural requirements – Predictive analytics, pattern detection, regression model, and recommendations
- AI Architecture Practice: How best to build the practice of architecture within your organization
Target Audience
Iasa’s basic course is aimed at IT Architects who want to become involved in AI architectural work.You should have basic knowledge and experience of system development and AI. You work as a developer, project manager, information model or process developer in AI Architecture projects.
Course Materials
The course will have around 20 to 30 Certification Questions. The workshop will have an assignment that will be included in the course. The assignment will have an application using machine learning algorithms. The course will have a workbook where the participant can apply the course concepts in their organization. By the end of the course, the workbook will help the participant to chart out the AI Strategy in the organization.
Course Modules
Module 00: Course Introduction
In the course introduction, we will cover the background and details of the Core Pre-Work and the basis of the ITABoK as well as working descriptions of AI Architecture and AI Architecture practices.
Content:
- Course Schedule Review
- Data Architecture Challenges
- Iasa Proposition
- What is AI architecture?
- Machine Learning Process
Module 1: AI Architecture – Data Requirements
This module covers data sources, data formats, data mapping and user options for data analysis.
Content:
- Data Sources
- Data Formats
- Data Mapping
- Data Analysis Options
For AI, data formats like image, video, text, and audio are covered. Different data sources like social media, blogs, news feeds, media websites, and other sources are presented in this module.
MODULE 2: AI Architecture – Data Modelling Principles
In architect engagement, we cover the data modeling principles and selection process for the right model. This module explains the training and evaluation of the data model.
Content:
- Data Model selection
- Training
- Evaluation
- Parameter Tuning
In this module, machine learning models, training, and validation parameters are discussed. Different machine learning algorithms such as classification, regression, neural network, decision tree, and random forest techniques. The learning methods covered are supervised, unsupervised, and reinforcement techniques.
Module 3: Business Case
Iasa fully covers the ‘demand’ or business drivers that underly AI architecture. This module explains the business value in driving AI Technology strategy through business decisions. We explore different business models, customer-driven architecture, and many other aspects of the business domain. The key aspects which are explored are innovation, automation, business value generation, and technology skills required by the engineering resources.
Content:
- Business Model
- Business Value Generation
- Business Capabilities
- Process Automation
- Creating AI skills-based Teams
In this module, AI technology benefits are discussed through business case modeling, value generation, capabilities, automation, and AI engineering topics. The takeaways for this module will be the business benefits of rapid analysis prediction & processing, accurate forecasts, cut down in the process time due to automation and improvement in compliance.
Module 4: AI Architecture – Machine Learning Solutions
When describing AI architecture, we work through the details of tracing the business value decisions related to the machine learning solutions. We look at the value streams, capabilities, applications affected by AI, design tradeoff analysis, architecture assessment methods, viewpoints, and architecturally significant requirements.
Content:
- Automated Advisors
- Conversational AI solutions
- Fraud Detection Solutions
- Compliance solutions
- Predictive Analytics
In this module, Predictive analytics, pattern detection, regression model, and recommendations are discussed in detail. The takeaways for this module will be real-time business decision making, eliminating manual tasks, enhancing security, reducing operating expenses, and improving business benefits through AI for your organization.
Module 5: AI Architecture Practice
The final module summarizes and expands on the AI Architecture process and the adoption of the set of programs and skills for the organization. The skill assessment provides a solid foundation for the student to review and understand their current AI skills and to create a growth plan for their future.
Content:
- Growing Competencies
- Skills Assessment
- Handling the Automation Issues
- Maturity Assessment
- Engagement Model
The takeaway for this module will be to create an enterprise AI roadmap with assessments of the current team and skills of required team members. The student will be able to chart out a center of excellence or competency center development strategy by training and developing the engineers of their organization. The skill requirements will be based on the AI architectural platform. After this course, the student will be able to pick the right AI platform from IBM, Microsoft, Amazon, and Google AI platforms.
Course Summary
Exam Information
The Certified IT Architect – AI Architecture (CITA-AIA) credential is awarded to those who qualify based on a combination of criteria including education, experience and test-based examination of professional knowledge of AI architectural skills and management.
The CITA-AIA credential is awarded by achieving a 70% or higher on the CITA-AIA examination. The exam consists of 75 multiple-choice/true/false questions.
The AI Architecture exam is available online, anytime, via Iasa’s Learning Management System. If attending an onsite course, the exam is proctored on the last day. If attending an online course, access is given on the last day of the course as well. Students will be given 2.5 hours each to complete the exam.
Watch out for the course announcements from IASA Global regarding this AI Course.
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