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Machine intelligence 4.0 public
This document discusses Machine Intelligence 4.0 and its role in enabling Globalization 4.0 based on Industry 4.0. It defines Industry 4.0 and Globalization 4.0 and notes they rely on digital transformation and collaboration. It describes data pipelines for acquiring, processing, storing and analyzing structured, unstructured and semi-structured data from various sources. It also outlines different types of machine learning including supervised, unsupervised and reinforcement learning. Finally, it discusses platforms, tools and languages used for machine learning and the skills needed for machine intelligence professionals.
Machine intelligence 4.0 public
- 1. MACHINE INTELLIGENCE 4.0 ForGlobalization 4.0 based on Industry 4.0 By Prasad Chitta
- 2. CONTEXT – WHATIS “4.0” ALL ABOUT? • Industry 4.0 - https://en.wikipedia.org/wiki/Industry_4.0 • Steam Engine, Electricity, Internet and 4.0 • Globalization 4.0 - https://www.weforum.org/globalization4 • Collaboration, Patriotism, Fair Economy, Environment, Work, social impact • Business 4.0 - https://sites.tcs.com/bts/digital-transformation-to- business-4-0-pov/ • Digital transformation for getting businesses ready for 4.0
- 3. DATA IS THENEW OIL – THAT NEEDS PIPELINES • From Acquisition to purging • transaction management (OLTP) • Analysis of data (OLAP) • Traditional before 4.0 Sensing (Manual, IoT) Acquiring, Validating, Munging Storing / Publishing / Streaming Update / Enrich / Tag Operational Reporting, Dashboards (MI) Transforming, Aggregating, De- normalizing OLAP reporting visualizing & Analytics Machine Learning, Deep Learning Archiving, Purging
- 4. SYSTEMS DEALING WITHTHESE PIPELINES
- 5. PIPELINES OF “LEARNING”AND “INTELLIGENCE” • Structured data comes from traditional systems of records • It can be originated in a data center, private or public cloud • Non structured data comes from mail, document / content management systems and systems of engagement like social media • Semi structured data comes from machine generated sources and IoT sources • Data that comes is processed on a batch, micro-batch or individual messaging level • Data pipelines gradually enrich the data for better insights
- 6. TYPES OF TRAINING(OR) LEARNING(?) https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861
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- 8. WHAT IS MACHINEINTELLIGENCE 4.0? Input •Program Machine 1.0 Output Input •Output • Rewards •Policies Machine AutoML + Model / data Repository 4.0 Auto Tuned Model Input •Configuration Machine With ERP Software 2.0 Output Input •Output (Labels) Machine + Data Scientists 3.0 Learning Model
- 9. MACHINE LEARNING –SKILL AREAS Statistics Story telling Artisti c skills Business Oper ations Archit ectur al Soluti on devel opm ent Tools Enabler Store streamProcess Business KPI Optimization Excellence Data Scientists ML Engineers ML Experts The challenge Analyze/ Visualize Describe Predict Prescribe Generate
- 10. TWO MODELS OFLEARNING “MACHINE LEARNING” Statistics / Probability / Linear Algebra Python, Tensorflow, Pytorch Business domain / Problem domain Business Problem and solution understanding Auto ML and Search for APIs integration into solutions on cloud / platforms
- 11. DO IT YOURSELF Arecent competition on kaggle… (A kernel walk through!) https://www.kaggle.com/c/two-sigma-financial-news
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