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  1. 1. Artificial Intelligence & Cloud Computing Presented By Deepak Sharma
  2. 2. Agenda • Introduction of Data Science • Various Roles in Data Science • Artificial Intelligence • Machine Learning • Natural Language Processing • Impact of AI on Business • AI Use Cases • Cloud Computing • Delivery Models • Deployment Models • Data Management on Cloud • Steps of Cloud Migration • Cloud Migration Challenges and Strategies • Success Stories
  3. 3. DataScience Copyright © 2019 RecogX.AI 3 It is a study to understand the logic behind the behaviourof data Logic couldbe pattern, mathematical formula or equation Bring hidden insight on surface and help to take informeddecision
  4. 4. Structure of Data 4 Copyright © 2020 RecogX.AI  Structured – Most traditional data sources.  Semi-Structured – Many sources of Big Data  Unstructured – Video data, Audio Data, Images
  5. 5. VariousRoles in DataScience DATAANALYST The person gather information from various sources and does first levelof analysis STATISTICIAN The person apply statistical test on sampledata set and helpsdata scientist to take decision whether to proceed on problemstatementornot DATA SCIENTIST The person apply machine learning/deep learning models on sampledata and explainthe results to the management/client DATA ENGINEER The person apply model suggestedby data scientist on large volume ofdata DATAARCHITECT The person setup the scalable workflow process pipe line such as data source to data process engine to resultdisplay. BI EXPERT The person displaythe result in the form of charts, dashboards tothe client/management 5 Copyright © 2019 RecogX.AI
  6. 6. ArtificialIntelligence When machines start thinkinglike humans and help us in taking decision Copyright © 2019 RecogX.AI 6 Machine Learning Deep Learning NaturalLanguage Processing Reinforcement Learning Robotics Rule Based
  7. 7. ArtificialIntelligenceContd… Copyright © 2019 RecogX.AI 7 MACHINE LEARNING It deals with system or algorithms tolearn relationships between input and output and identify hidden patterns DEEP LEARNING It’s a special branchof Machine Learning where machine learns relationships between input and output using process used by human brain NATURAL LANGUAGE PROCESSING It’s abranch that deals with understanding the human language REINFORCEMENT LEARNING For a specific situation what best possible path or behaviour a program will take getresult ROBOTICS Transformers movie – when machines behave like human beings RULE BASED “If this than this” approach
  8. 8. Types of Machine Learning • Unsupervised - Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses • Supervised - Supervised learning is inferring from labelled training data
  9. 9. Which customers are most likely to buy the product 1. Problem Identification C Collect data from Social Media, product reviews, purchase trends, shopping cart behaviour 2. Data Collection Whether the customer has bought the product in last 6 months 3. Data Labelling How to rank the result in terms of strong lead or weak lead 6. Model Evaluation Criteria Randomly split data into train and test 5. Split Dataset Transform raw data into features 4. Feature Extraction Train the model 7. Model Training Explore the results 8. Analyse Model Output I Identify the customer who are likely to buy the product 9. Model Deployment ML Process Steps
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  11. 11. Use Cases of ML • Automotive – Driverless cars • Banking – Opportunity to product new market, balance risk and detect fraud • Government – Enhance security and threat detection • Manufacturing – Product defects, Predictive Maintenance, Operational Efficiencies. • Retail – Continuous monitoring of consumer behaviour, Micro segmentation, product recommendation • Education – Career selection, Online teaching • E-commerce – Recommendation System, Last Mile Delivery, Customer engagement
  12. 12. Challenges with Machine Learning • Data Integration and Preparation • Feature Extraction - Combining the original features (e.g., variables) into a smaller set of more representative features • Feature Selection - Selecting only the most meaningful original features to be used in the modeling algorithm • Feature Engineering - Combining pre-existing features in a data set with one another or with features from external data sources to create new features that can make models more accurate
  13. 13. Artificial Neural Networks • It’s a supervised learning system built of neurons or perceptrons • Neurons, connected with each other via interconnected layers, process data to drive information • A shallow neural network has only three layers • An Input Layer • Hidden Layer • Output Layer
  14. 14. Types of ANN 14 FeedForward • Unidirectional information flow • No feedback loop • Fixed number of inputs and output FeedBack • Bidirectional information flow via feedback loop FeedForward Feedback
  15. 15. Application of AI using Neural Network 15 1. Automobile guidance system - In the case of self-driving cars, AI can help with being the brains of the cars doing things like automatically detecting people and other cars around the vehicle, staying in the lane, switching lanes, and following the GPS to get to the final destination 2. Target tracking - Using AI to Recognize Objects in Milliseconds. Intelligence, surveillance, and reconnaissance platforms such as satellites, UAVs, and autonomous systems must process data in milliseconds to transmit decision-making information to the warfighter in real-time 3. Computer vision - A field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. 4. Loan Advisor - An AI-powered mortgage advisor tracks market changes in real time to ensure that all the information available is fully up to date. An automated advisor can provide with realistic estimates of maximum loan amount, the expected down payment, potential closing costs, and other expenses 5. Credit Application Evaluators – An AI powered credit application evaluator system will do risk assessment of issuing the loan, Data handling from a very wide range of touch points, such as income sources, purchase patterns and overall financial behaviours of customers, and early warnings to reduce the impact of bad loans.
  16. 16. Deep Learning Neural Network (DNN) • Deep learning is the extension of ANN with added hidden layer to solve complex problems with high accuracy 16 Types of DNN • Convolution Neural Networks - CNN • Recurring Neural Networks - RNN • Long Short-Term Memory - RNN
  17. 17. CNN – Full Architecture 17 Reference -
  18. 18. Application of CNN 18 • Image Tagging –This has important role in visual search as well by comparing the input image with database search for other photos that have comparable credentials. • Recommender Engines – This is the business application based on object identification and image classification. When we visit Amazon, we see a message “you might also like” with list of suggestions by recommendation system of Amazon. • Facial Recognition – The application deals with recognition of complex images such as faces, animals, insects etc. The first step in facial recognition is object recognition. Then extract features of the object such as shape of nose, skin tone, presence of scars, hair, eyes distance etc. • Medical Image Computing – CNN helps in detecting anomalies in X-ray and MRI images with better accuracy. The technique helps doctor with information which help them in taking quick informed decisions. The algorithm is trained on Pubic Health Records of Patients. • Number Plate Recognition – The technique is applied in controlling the traffic as well as road disturbance by capturing and reading the number plates of vehicle whose drivers are not obeying traffic rules. The same technique is applied on road surveillance, toll plazas to capture vehicle movement and give important information to traffic police in case any crime happens
  19. 19. Recurring Neural Network 19 • RNN is a deep learning algorithm used for processing sequential data • It has short term internal memory to remember its input • The information cycles through a loop • It has two input (Present and Recent Past) • It applies weights to both present and recent past inputs • The most famous application of RNN is Siri from Apple and Google Translation service
  20. 20. Application of RNN 20 ©2021 ExlService Holdings, Inc. All rights reserved. • Language Modelling and Generating Text – The model is used to learn from the sequence of words as input and then predict the possibility of the next word. • Machine Translation - RNN is trained to translate text from one language to another using encored rand decoders. The Google Translate is one of the popular application what run using RNN. • Speech Recognition – This is a technique that is used to predict the phonetic segments of sound waves. • Text Summarization- The technique is used to summariese a large document with all important information stay intact. The technique works on ranking system where the algorithm read each sentence and generate a score against it. • Call Center Analysis - RNN process and synthesize actual speech from the call for analysis purpose. Such synthesized speech can be further taken as an input to a tone analysis algorithm to measure the emotions and sentiments related to various parts of the conversation.
  21. 21. Natural Language Processing Branch of Artificial Intelligence that deals with interaction between human and machine. NLP Objective Read Decipher Understand Make Sense Reply
  22. 22. Use Case Of NLP – Around us Language translation applications such as Google Translate Grammatical accuracy of texts (Microsoft Word and Grammarly) Interactive Voice Response Personal Assistant – OK Google, Siri, Cortana and Alexa
  23. 23. Relation Between AI, ML, Deep Learning & NLP Deep Learning Artificial Intelligence Machine Learning NLP
  24. 24. ImpactofAIonBusiness Copyright © 2019 RecogX.AI 24 Automate Routine Process Increase productivity & operational efficiencies Predict customer preference HumanError minimized Cost Savings Personalized Experience Increase Revenue Faster Business Decisions Fast Customer Acquisition
  25. 25. Automotive − Automobile guidance systems – DriverlessCars Military − Weapon orientation and steering • Target tracking Object discrimination • Facial recognition • Signal/image identification Electronics − Code sequence prediction • IC chip layout • Chip failure analysis Machine vision • Voicesynthesis Financial − Real estate appraisal • Loan advisor • Mortgage screening Currency value prediction • Document readers • Credit application evaluators Applicationsof AI 25 Copyright © 2019 RecogX.AI
  26. 26. Telecommunications− • Bots • Image and datacompression, • Real-time spoken languagetranslation. Transportation − • Number platerecognition • Truck Brake systemdiagnosis, • Vehicle scheduling, routingsystems. Software− • Pattern Recognition in facialrecognition, • Optical character recognition,etc. Anomaly Detection − Prediction ofunusual activity. Industrial− • Manufacturing processcontrol • Quality inspection systems • Machine maintenance analysis, project bidding,planning, and management. Medical− • Lifestyle disorderanalysis • Xray image analysis • Patients footfall prediction Speech − • Speech recognition, • Speech classification • Textto speechconversion. Applicationsof AI Contd… 26 Copyright © 2019 RecogX.AI
  27. 27. AI Application inSupplyChainManagement 27 Copyright © 2019 RecogX.AI Chatbot for Operational Procurement Speak to suppliers during trivial conversations. Set and send actions to suppliers regarding governance and compliance materials. Place purchasing requests. Research and answer internal questions regarding procurement functionalities or a supplier/supplier set. Receiving/filing/documentation of invoices and payments/order request
  28. 28. 28 Copyright © 2019 RecogX.AI Supply Chain Planning Warehouse Management using Robotics Demand and Supply forecasting Inventory Forecasting Track, Locate and Move Inventory within the warehouses Autonomous Vehicles Reduce lead time Reduce Transportation Expenses Reduce Labor Costs Route Optimization Highway autopilot, Lane-Assist and Assisted Breaking
  29. 29. Promising Use Cases of AI in Logistics 1. Automated Warehouses • Demand Prediction of particular product • Modify orders and deliver in-demand items to local warehouse • Lower transportation cost • Ocado Success Story – Supermarket in United Kingdom. It has developed an automated warehouse. The system is based on a robot called ‘hive-grid- machine.’ This robot can execute 65,000 orders per week. ‘Hive-grid-machines’ main task is to move, sort, and lift items inside the warehouse. Ocado’s automated warehouse dramatically cuts labor and the time for orders to be executed.
  30. 30. Promising Use Cases of AI in Logistics 2. Autonomous Vehicles • Save time & money • Reduce chances of accidents • Warehouse ground vehicles and drones • Rolls-Royce Success Story– Rolls-Royce is working with Intel to develop self-driving ships. It released the Intelligence Awareness system that is able to classify all the nearby objects under the water. It can also monitor the engine condition and recommend the best routes.
  31. 31. Promising Use Cases of AI in Logistics 3. Back Office • Artificial Intelligence with Robotic Process Automation (RPA) provides the workers with an opportunity to increase their quality of work • It lowers costs and improves the accuracy and timeliness of data for logistics companies. • UIPath Success Story– Developed a robot that is able to conquer approximately 99% of back office tasks
  32. 32. 32 Copyright © 2019 RecogX.AI
  33. 33. 33 Copyright © 2019 RecogX.AI
  34. 34. 34 Copyright © 2019 RecogX.AI
  35. 35. 35 Copyright © 2019 RecogX.AI
  36. 36. CloudComputing 36 Copyright © 2020 RecogX.AI Cloud computing is a recent technology whose aim is to deliver IT services to masses. Cloud computing essential key characteristics are:  On-demand self-service  Ubiquitous network access  Resource pooling  Rapid elasticity  Pay-per-use
  37. 37. Delivery Model 37 Copyright © 2020 RecogX.AI  SaaS (Software as a service) - Software distribution model. Host application and eliminates the wave of hardware and software acquisitions, as well as provisioning and maintenance and software licensing. For Example - Gmail, Microsoft Teams, Zoom, Office 365  PaaS (Platform as a service) - Provides complete development, testing and deployment platforms. And reduce complexity of development and testing. For Example - Azure, Google App Engine, AWS Elastic Beanstalk  IaaS (Infrastructure as a service) - Host hardware, software, servers, storage and other infrastructure components. It has private, public and hybrid type of cloud deployment. For Example - AWS, AWS, Digital Ocean, Cisco Metacloud, Google Compute Engine
  38. 38. Delivery Models Contd… 38 Copyright © 2020 RecogX.AI
  39. 39. DeploymentModel 39 Copyright © 2020 RecogX.AI  Public  Private  Hybrid  Community
  40. 40. PublicCloud a) The infrastructure in this cloud model is owned by the entity that delivers the cloud services, not by the consumer b) There is no substantial upfront fee. It works in the model of pay-per-use service c) Infrastructure on cloud is run and maintain by cloud service provider d) Cloud resources are available on demand basis e) Customer has less control on infrastructure setup on cloud PrivateCloud a) Its direct opposite to the public cloud model b) Infrastructure is not shared with any other user c) Customer has the best control over services, IT operations, policies etc d) Suitable for storing corporate information to which only authorized staff have access e) User can deploy license software
  41. 41. HybridCloud a) It gives best of both public and private cloud model b) Customer can host the application on safe private cloud environment and take advantage of public cloud’s saving. c) The model provide both flexibility and control on cloud environment CommunityCloud a) It allows systems and services to be accessible by a group of organizations b) Works on integrating the services of different clouds to address the specific needs of a community, industry, or business c) The infrastructure of the community could be shared between the organization
  42. 42. Data Management on Cloud Easy accessibility of data on cloud must be balanced with protection to ensure maximum business value. We need to take care of 5 important points to maintain that balance ─ Resting Data – When data rest in storage is considered as Resting Data. The data should be behind layers of security along with proper encryption. Employees are the soft target for hackers. Encryption saves data not only from malicious intent but also from careless action ─ Accessing Data – The access of the data must be under control environment. Any person requesting access to data must be authenticated and every data transaction should be recorded so you can audit later if necessary. ─ Data in Transit - Create a secure, authenticated and encrypted tunnel between the authenticated user and device and the data they’re requesting. For ex Virtual Private Network ─ Data Arrival – Setup mechanism to check data integrity and clear audit trail. This will protect the system from malware and phishing attack ─ Backup and Recovery - Do not store your backups in the same cloud account where your production data resides. Leverage multiple cloud accounts to segregate your backup data from your production data.
  43. 43. Steps of Cloud Migration ─ Outline Reasons for Moving to the Cloud – Outline business objective such as reduce costs, gain new features, leverage real- time data, analytics, scalability ─ Determine Organizational Cloud-Readiness - Conduct a comprehensive business and technical analysis of current environment, apps, and infrastructure. ─ Choose a Cloud Vendor - The right platform for your business will depend on specific requirements, the architecture of the applications moving to the cloud, integrations etc ─ Create Cloud Roadmap - Outline which components will make the move first based on business priority and migration difficulty. ─ Get Application Cloud Ready – Choose between Lift and Shift (Rehost) and Rearchitect (Refactor) ─ Migrate Data - Audit the data to prevent any unexpected issues, clean-up of any identified concerns, putting controls in place to ensure data quality, and proper governance through tracking and monitoring. ─ Ongoing Upkeep – Flip the switch from testing to production. Migrate a set of test users over to the new environment before a full launch to identify any issues that were missed through deployment and initial testing. ─
  44. 44. Cloud Migration Challenges ─ Lack of Strategy–The organization must have a clear business case for each workload it migrates to the cloud. ─ Cost Management– Cloud environments are dynamic and costs can change rapidly as new services are adopted and application usage grows. ─ Vendor Lock-in–There is a high switching cost to migrate workloads from one cloud to another cloud service provider. A detail due diligence is required before finalising the cloud hosting environment. ─ Data Security and Compliance– In the shared responsibility model cloud service provider take responsibility of securing the cloud infrastructure and customer is responsible for securing data and workloads.
  45. 45. Cloud Migration Strategies – 5 R ─ Rehost– Also called “Lift and Shift” which means redeploy existing data and applications on the cloud server. ─ Refactor – Also called “Lift, tinker and Shift” which means tweak the applications as per the new cloud environment. ─ Rearchitect–This strategy is best suited for companies who decide to migrate on cloud at later stage. This allows company to divide the application into several functional components that can be individually adapted and further developed. ─ Rebuild–Rebuild involves removing existing code and redesigning the application in the Cloud, after which you can utilize innovative features on the Cloud provider’s platform. ─ Replace - A “Replace” migration strategy completely replaces an existing application with SaaS.
  46. 46. Best Practices of Migration to Cloud ─ Understand the scope and life cycle of the application ─ Review and choose an IT partner that can meet SLAs. ─ Properly manage software licensing ─ Monitor the Cloud usage periodically ─ Leverage service provider support
  47. 47. Success Stories – NSW Health NWS Health Pathology Solution - It is one of Australia’s largest public health sector pathology providers. It operates over 60 laboratories and conducts more than 61 million tests each year. Business Problem – The lab does Covid-19 test of patients. The lab was taking 10 days to deliver the result to the patients, which was elongating the isolation time and increase patient anxiety. The lab wanted to automate the SMS notification service so that the results of Covid-19 test can be shared with patient with in 2 hours. Solution – The company applied Amazon Redshift cloud data warehouse service and Amazon Connect omnichannel cloud contact center to help users provide superior customer service at a lower cost because of its pay-as-you-go pricing. Benefits of Migrating on Cloud ─ Provides COVID-19 test results in less than 2 hours ─ Saved over 1 million clinical hours for frontline workers ─ Delivered over 4.25 million COVID-19 test results ─ Built solution and registered first patient in 2 weeks ─ Registered 87% of patients who were tested at state-run facilities in NSW
  48. 48. Success Stories – Paytm Paytm - Paytm is the consumer brand of India’s leading mobile internet company, One97 Communications. The brand is one of India’s largest financial services companies, offering full-stack payments and financial solutions to consumers, offline merchants, and online platforms. Today, the company serves millions of merchants and customers on its platform in India. Business Problem – The company wanted to develop further capabilities in tech solution by adding facial recognition and other machine learning capabilities on OCR problems, and gradually replace existing third-party software with AWS solutions. Solution – By using Amazon Textract, Paytm extracts user data from images of complex identity documents with 97 percent accuracy. Once the information is captured, Amazon Textract helps to identify image noise in real time, allowing Paytm to immediately notify onsite agents to retake users’ identity document pictures when necessary, saving both parties the inconvenience of repeat visits.. Benefits of Migrating on Cloud ─ Reduced time required for user KYC from days to minutes ─ Deployed KYC solution in one hour ─ Reduced costs by 75 percent ─ Better customer and merchant experience
  49. 49. Success Stories – ERGO Insurance ERGO Insurance Singapore - a registered general insurer in Singapore and a wholly owned subsidiary of ERGO Group AG, which in turn is a fully owned subsidiary of Munich Re Group. With a background in commercial insurance, the company offers innovative new solutions for commercial and private insurance customers. Business Problem – Monetary Authority of Singapore (MAS) mandated stricter cyber hygiene requirements for financial institutions (including insurance providers) that ERGO had to follow. In order to comply the new regulations, the company need to upgrade on-premise hardware which incur huge cost for the company Solution – The company migrated on AWS cloud to achieve secure yet cost-efficient infrastructure without making huge investment in hardware. The company chose Blazeclan as its AWS Partner to help with the migration and ongoing operations in the AWS Cloud. Benefits of Migrating on Cloud ─ Complies with the Monetary Authority of Singapore cyber hygiene and technology risk-management requirements ─ Achieves at least 99% uptime and low website latency ─ Receives 24x7 support from AWS Partner ─ Saves 4 hours daily with automated database backups ─ Improves efficiency with seamless data transfers and integrations
  50. 50. Success Stories – Thompson Reuters Thomson Reuters- It is a news and information services company that provides solutions for tax, law, media, and government across 100 countries. Its business unit ONESOURCE GTM helps clients meet compliance for their imports and exports through its software. Business Problem – The had a time-consuming, manual DR process in place that required a full team of engineers to manage two separate on-premises data centers, and it did not provide the data protection and recovery times the company wanted. Solution – The company adopt AWS Elastic Disaster Recovery (CloudEndure Disaster Recovery), which minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery. Benefits of Migrating on Cloud ─ Replicated over 120 TB of data from 300 servers ─ Set up a recovery site in the cloud ─ Eliminated its manual DR process ─ Optimized spending on its disaster recovery process ─ Enhanced its security and compliance
  51. 51. Major Trends in Cloud Computing Cloud First Approach Will Reign - Due the challenges companies have faced during Covid-19, we see a strong adoption of cloud-based setup for both business critical and heavy workloads. Cloud Native Application Will Thrive Cloud is coming with plenty of pre build softwares that companies will no longer have incentive to build a solution from scratch. For example AWS or Google Facial recognition services are way more advanced in comparison to inhouse build solution. Data analysis will grow on Cloud In order to extract insights from data companies need a supporting infrastructure and platform that will scale as need increases. This makes Data Analysis as a good service to be offered by Cloud Service Providers.
  52. 52. Thank You