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Development of AI Applications without Machine Learning Skills

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Slides of the session "Development of AI Applications without Machine Learning Skills" at the new.New Festival: https://www.newnewfestival.com/program/

Demos:
1) Anki Cozmo Visual Recognition of Toys with TensorFlow: https://youtu.be/kS873xLoq9o?t=5s
2) Visual Recognition on iOS and Android Devices via TensorFlow Lite: https://youtu.be/kS873xLoq9o?t=39s
3) Object Detection of Anki Overdrive Cars via TensorFlow: https://youtu.be/kS873xLoq9o?t=1m17s
4) Augmented Reality Demo using Watson Assistant and Speech to Text: https://youtu.be/kS873xLoq9o?t=1m57s
5) AutoML with Watson Studio: https://youtu.be/kS873xLoq9o?t=4m5s
6) IBM Code Model Asset Exchange: https://youtu.be/kS873xLoq9o?t=6m42s

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Development of AI Applications without Machine Learning Skills

  1. 1. Development of AI Applications without Machine Learning Skills Niklas Heidloff Developer Advocate, IBM @nheidloff heidloff.net 09.10.2018
  2. 2. Building new Deep Learning Models is too hard for Developers
  3. 3. Sample AI Applications Visual Recognition TensorFlow Lite iOS / Android Edge Deployment Visual Recognition TensorFlow Python App Cloud Deployment Object Detection TensorFlow iOS Edge Deployment
  4. 4. iOS Object Detection Sample
  5. 5. Usage of AI Functionality without ML Skills Standard AI Services AutoML Open Source Models
  6. 6. Standard AI Services Conversations, Speech to Text, Visual Recognition, Translations, Natural Language Understanding, Discovery, Personality Insights, ... Conversation Speech to Text Out of the box usable Services and trainable Services
  7. 7. Automatic Machine Learning ML and DL, Hyper Parameter Optimization, Genetic Algorithms IBM Watson Open Source Libraries and commercial Offerings
  8. 8. Open Source Models Different Catalogs/Model Zoos from Frameworks and Cloud Providers Model Asset Exchange IBM Model Asset Exchange: Consumability, vetted IP, various ML/DL Libraries, various Domains
  9. 9. Transfer Learning “Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.” (via Wikipedia)
  10. 10. 10 Thank you developer.ibm.com ibm.com/watson/developer @nheidloff github.com/nheidloff heidloff.net

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