This document provides an overview of machine learning systems and their business applications. It discusses chatbots, healthcare, and autonomous driving as key areas where machine learning is currently used. For each application area, the document outlines the current capabilities and limitations of machine learning as well as the business values and potential challenges. It emphasizes that machine learning is a tool that can help automate tasks, improve processes, and predict outcomes to help businesses but that careful consideration is needed regarding implementation costs, legal issues, and other potential blockers.
3. - Machine learning is just a tool.
- The tool that may help you and your business.
- ML may not be easy, but at least it’s possible.
- It’s interesting.
- And in any case ML is very popular.
Main ideas
7. Industry Overview. What is the reason of ML?
- ML market - 1.41 Billion in the end of 2017.
- Expected on 2022 - 8.81 Billion (report)
- Company engaged:
- Toyota, VAG group, Daimler AG
- Walmart, Target, Amazon
- AIG, PayPal, Zappos
- ...
- How?
- Personalize
- Automate
- Predict
- Improve
- ...
19. What does exist now?
- Digital medical records
- Disease identification/Diagnosis
- Drugs discovery/Manufacturing
- Epidemic outbreak prediction
20. What can be done?
- Wearable continuous monitoring devices
- Single database
- Personalized medicine
- Automatic treatment or recommendation
- Automated handling of medical records
- Treatment of disabled people
- People modifications
21. How is it possible?
- Objects classification
- Objects detection
- Prediction systems
- Speech and text recognition
22. Business values
- Increased life expectancy
- Reduction of insurance payments
- Improvements in the one of the most huge markets
23. Potential problems
- Data availability
- Personal data handling and
protecting
- False positive or false negative
results
- Certification, medical clearance
- Bureaucracy and conservatism
35. Existed resources
- Udacity Self Driving Cars nanodegree
- Open Source Self Driving Car Initiative
- MIT 6.S094: Deep Learning for Self-Driving Cars
- Autonomous Driving CookBook
- Nvidia end-to-end training paper
37. Are you need it?
- What benefit?
- What are implementation costs?
Take a look at the possible blockers:
- Is such task implementable with the help of ML at all?
- Legal issues
- Datasets existence
First steps:
- Consult with domain expert
- Define clear requirements(minimum and maximum)
- Speed
- Accuracy
- What should be considered as "done"?
- Check available open sourced solutions
Later:
- Measure real profit
- Decide, should your solution be updated or not
