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Emerging engineering issues for building large scale AI systems By Srinivas Padmanabhuni Consultant – Manipal ProLearn, Chief Mentor at Tarah Technologies at Cypher 2018

  1. FOR THE WIN PG Certificate in Artificial Intelligence & Deep Learning Online Instructor led 6 month program
  2. | Manipalprolearn.com3
  3. | Manipalprolearn.com4
  4.  Award from MAHE, an Institution of Eminence  Real life case studies with real data from different domains (Marketing, Healthcare, Media) ◦ Marketing data – Leads and conversion data from Manipal Global ◦ Audio scripts – for RNN and NLP ◦ CCTV footage – for object detection, face recognition ◦ Healthcare data – real x-ray images  Highly hands on to prepare the learners to be job ready  GPU based training environment  Content delivery by industry experts/practitioners  Strong Industry collaboration 5
  5. Tarah Technologies, 6  Wikipedia ◦ “intelligence exhibited by machines”
  6. Tarah Technologies, 7  "The application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software"—IEEE Standard Glossary of Software Engineering Terminology. "an engineering discipline that is concerned with all aspects of software production“ —Ian Sommerville. (Source: Wikipedia)
  7. Tarah Technologies, 8
  8.  Plain stress on ML/AI algorithms diverting attention from big picture  Need for solid systems and software engineering principles for AI /ML systems  Need to develop skills for end to end process for understanding, designing, building and evaluating AI/ML systems  Tools for enhancing productivity of every aspect of engineering of AI/ML systems needed  Non Functional requirements like security are important and so are additional needs like explainability
  9. A computer program is said to learn from experience if its performance at tasks improves with experience . -Can be Unsupervised, or Supervised or Reinforcement
  10. 1. The myth of glorified data scientist – Only stress on Algorithms is a bad idea 2. Spend time in Problem Identification – Use Design thinking for right requirements – Get a domain guy for sure in the team 3. Spend a good amount of time in data acquisition and storage needs (Without a robust big data infrastructure this is meaningless) 4. Spend countless hours on data schemas, data understanding and data cleaning (Junk In Junk out) 5. Don’t ignore the architecture 6. Last but not the least there is a process (JIJO) – A robust data science process is crucial much like CMM is root to success of IT services success Practical Issues in Building ML systems
  11. 1. Data acquisition 2. Data Understanding 3. Data Preparation 4. Hypothesis and modeling 5. Evaluation and Interpretation 6. Deployment 7. Optimization Process: Follow Data Science Development Life Cycle – CRISP DM Method for Machine Learning
  12. Tarah Technologies, 13 Google AutoML removing drudgery of navigating several hyperparameters for ML IBM Research recently launched a framework for simplifying deep learning programs authoring End to End Pipelines are arising for automating ML processes like Like ML developer productivity deployment productivity is crucial example NicheAI is working on simplifying deployments of AI ML workloads and optimizing costly GPU needs It is important to have a good tools for diagnosing ML programs – Like What if tool from Google, LIME etc
  13.  Today some domains are critical of AI/ML due to legislative compliance issues  Can we bring explainability to our models to help compliance ◦ LIME (Locally Interpretable Model Agnostic Explanations) ◦ Google ‘s What if Tool ◦ Guided backpropagation  GDPR poses significant constraints on data acquisition for AI/ML  Need to be able to visualize and explain models is becoming important for business stakeholders
  14.  Several innovative architectures like GPUs TPUs FPGA s bein worked on  Even Big Data Infrastructures like Spark are gearing to support PySpark and SparkR  However Performance optimization of programs for target infrastructures still a far cry (Startups like NicheAI, PipelineAI )  Cost is an important issue So Cost Effective GPU etc for target customers becomes imperative, hence solutions emerging like NimbleBox AI (Heroku for AI)  Last but not least deployment productivity is turning out to be a key innovation target just like development productivity
  15. Tarah Technologies, 16
  16. Tarah Technologies, 17 Several examples of AI technology of Computer Vision with deep learning to help in UxD Google's AI Doodle Bot enables completion of low fidel doodles. Autodesk partners with Airbus to enable generative design of airplanes Examples of Deep Learning based Vision for Web Design solutions emerging GUI automation and testing
  17.  Sketch2CODE – Transforms HTML to code  Nvidia Project Holodeck  Autodesk Dreamcatcher  Pixel2Code
  18. Tarah Technologies, 19 Root Cause Analysis – Automation of root cause analysis Automated Defect Prediction - Identifies high-risk areas in the application which helps in risk-based prioritization of regression test cases Test Prioritization Smart Regression Test Selection - Use AI to match test cases that need not be retested
  19.  Test.AI  Moolya uses AI for testing  Applitools does visual testing with AI based vision  Appvance - User behavior based test case generation  – test case authoring
  20. Tarah Technologies, 21 Automated Concepts extraction from Requirements via NLP •Actors identification •Class Identification •Use case extractor Generating test cases from Textual Requirements Design artifacts for Agile from User Stories
  21. Detailed Malware analysis via ML based pattern matching Fuzz testing designed to find vulnerabilities in software via ML Security Risk understanding via AI based decision support system Intrusion Prevention Systems
  22. Tarah Technologies, 23 Ticket Analytics to help cluster tickets Automating RCA via Machine Learning Text Mining of support tickets for clustering End to End automation of simple / rule based processes Bayesian Network based intelligent automation of infrastructure support Data Centre Automation Self Service Infra Process Automated Help Desk Resolution Process Gap Analysis via Analytics
  24.  Important to look at broader software and systems engineering issues for AI / ML systems  Important to look at data as an important asset in software engineering  Look for opportunities for automation and process improvement in Software engineering with AI/ML/Analytics
  25. Thank you For Manipal PGD AI DL premjith.alampilly@manipal m