The document discusses trends in mobile application development and artificial intelligence. It notes that consumers expect quick access to personalized experiences across channels while protecting privacy. It also discusses the growth of edge machine learning using frameworks like TensorFlow and TensorFlow Lite to perform tasks like classification, prediction, recognition and other functions on devices with limited resources.

1. Modern Development
Information age

2. Yossi Elkrief
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@elkriefy
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● GDG Beer Sheva
● Google Launchpad Mentor
● Mobile lead , Nike TLV Innovation hub
● LFC supporter

4. https://appinventiv.com/blog/mobile-app-trends-2020/

5. https://appinventiv.com/blog/mobile-app-trends-2020/

6. https://appinventiv.com/blog/mobile-app-trends-2020/

7. https://appinventiv.com/blog/mobile-app-trends-2020/

8. https://appinventiv.com/blog/mobile-app-trends-2020/

9. https://appinventiv.com/blog/mobile-app-trends-2020/

10. https://appinventiv.com/blog/mobile-app-trends-2020/

11. https://appinventiv.com/blog/mobile-app-trends-2020/

12. https://appinventiv.com/blog/mobile-app-trends-2020/

13. https://appinventiv.com/blog/mobile-app-trends-2020/

14. https://appinventiv.com/blog/mobile-app-trends-2020/

15. https://appinventiv.com/blog/mobile-app-trends-2020/

16. https://appinventiv.com/blog/mobile-app-trends-2020/

17. https://appinventiv.com/blog/mobile-app-trends-2020/

18. Radical interoperability
Let’s break down the barriers

21. https://cheesecakelabs.com/blog/android-architecture-components-viewmodel-leak/

23. https://images.app.goo.gl/HFaa9QoqAqPdBEjTA
https://images.app.goo.gl/ekpjzLth1ToKZEDd6https://images.app.goo.gl/d5RaqASjQ4nza9Ze9

24. https://images.app.goo.gl/1g71rWYxqiXzfjKD8

25. https://images.app.goo.gl/epPXTrEsrK3qa8Mu6

26. https://images.app.goo.gl/XE2kdU2YYmgAGX2a6 https://images.app.goo.gl/VzhkfXcmyHmRxUGUA
Hobbies

27. Friends

28. Family

29. https://images.app.goo.gl/d5mZpd5N8gBFLatC6

30. Opinionated development

33. https://proandroiddev.com/android-components-architecture-in-a-modular-word-7414a0631969

34. https://medium.com/androiddevelopers/a-patchwork-plaid-monolith-to-modularized-app-60235d9f212e
Plaid

35. Don’t over use patterns…

36. Users don’t care about
patterns
¯_(ツ)_/¯ 🤷

37. https://images.app.goo.gl/rrcvhyvDT6eBzF2s5

38. Consumers expect more
● Quick access to what they need, when they need it.
● Consistent experience across multiple engagement channels
● Privacy seekers : hesitant to share too much personal information
● Personalization , ease, and flexibility in their interactions

39. Digital identity era

40. Mobile Application Development
https://business.linkedin.com/talent-solutions/blog/trends-and-research/2018/the-most-in-demand-hard-and-soft-skills-of-2018

41. https://business.linkedin.com/talent-solutions/blog/trends-and-research/2018/the-most-in-demand-hard-and-soft-skills-of-2018
Artificial Intelligence

43. Neural Network
A type of algorithm in machine learning
Artificial Intelligence
The science of making things smart
Machine Learning
Building machines that can learn

44. What is TensorFlow?
End-to-end open source ML platform
TensorFlow is a machine learning platform that
enables you to develop, train and deploy machine
learning and deep learning models anywhere

45. TensorFlow Distributed Execution Engine
CPU GPU TPU Mobile ...
Neural Nets
C++
K-Means
Loss Functions,
Metrics
Linear Algebra Decision Trees SVM
Gaussian
Mixture Models
Regression Random Forests
Probabilistic
Methods
Signal
Processing
Lattice
Python Java ...
A comprehensive ML toolkit

46. What is
TensorFlow Lite?

47. TensorFlow Lite is a
framework for deploying ML
on mobile devices and
embedded systems

48. Edge ML Explosion
● Lower latency & close knit
interactions
● Network connectivity
● Privacy preserving

49. New opportunities
● Uses little compute power
● Works on limited memory
platforms
● Consumes less battery

50. Convert once, deploy anywhere
Simplifying ML
on-device

51. Have now deployed TensorFlow Lite in production.
More than 2B devices
globally.
Source: https://medium.com/tensorflow/recap-of-the-2019-tensorflow-dev-summit-1b5ede42da8d

52. Classification
Prediction
Recognition
Text to Speech
Speech to Text
Object detection
Object location
OCR
Gesture recognition
Facial modelling
Segmentation
Clustering
Compression
Super resolution
Translation
Voice synthesis
Video generation
Text generation
Audio generation
AudioImageSpeechText Content

53. Thank you !
Yossi Elkrief
@elkriefy