The newest buzzword after Big Data is AI. From Google search to Facebook messenger bots, AI is also everywhere. • Machine learning has gone mainstream. Organizations are trying to build competitive advantage with AI and Big Data. • But, what does it take to build Machine Learning applications? Beyond the unicorn data scientists and PhDs, how do you build on your big data architecture and apply Machine Learning to what you do? • This talk will discuss technical options to implement machine learning on big data architectures and how to move forward.

1. From Big Data to AI
Building Machine Learning Applications
Maloy Manna

2. Abstract
• The newest buzzword after Big Data is AI. From Google search to Facebook
messenger bots, AI is also everywhere.
• Machine learning has gone mainstream. Organizations are trying to build
competitive advantage with AI and Big Data.
• But, what does it take to build Machine Learning applications? Beyond the
unicorn data scientists and PhDs, how do you build on your big data
architecture and apply Machine Learning to what you do?
• This talk will discuss technical options to implement machine learning on
big data architectures and how to move forward.

3. Your presenter
Maloy Manna
Data engineering PM, AXA Data Innovation Lab
I build data products & services.
- Solution architecture | Design | Lean-Agile
linkedin.com/in/maloy maloymanna.github.io

4. About you
How many of you are:
• Business ?
• Data scientists ?
• Data analysts ?
• DevOps or IT ?
• Data engineering ?

5. What we’ll talk about today
• Evolution of Data Analytics … and product strategies
• Big Data architectures
• Machine Learning
• ML Use Cases
• Deep Learning
• APIs & Chatbots
• Challenges & Takeaways

6. Search

7. Messenger Bots

8. Google Assistant

9. Tesla Autopilot

10. Home automation platform

11. Don’t look at AI in isolation

12. Data Analytics Evolution
• Automate data feeds | ETL
• Structured analytics | Data warehousing & BI
• Big data capabilities | Data lake
• Real-time insights | Streaming data analytics
• AI | Machine learning

13. Data Science

14. Data science to build data products
• What is the problem we are trying to solve?
• How do we know when we’ve won? (key success metrics)
• Is it useful?
• What data do we have?
• What data should we have?
• Which assumptions can I verify?
• Ask questions
• https://www.datasciencecentral.com/profiles/blogs/the-data-science-project-
lifecycle

15. Where will you integrate this prediction?
• Strong business objectives and goals
• Specific problems
• Targeted benefits

16. Data lake architecture components
• Platform
• Data acquisition
• Data processing
• Data access
• Security
Influences:
• Objectives: Agile / Fail-fast and open-source
• IT strategy - infra
• Interplay of factors
Data platform
Data acquisition Data processing Data access
Security & Governance

17. Streaming use cases
• Clickstream analysis
• Process point-of-sale transactions
• Mobile gaming
• IoT data

18. Machine Learning
How many of you have:
• Used an open-source ML?
• Built / plan to build your own ML?
• Used ML in the cloud?
• Use / plan to use ML on-prem?

19. Machine Learning

20. Machine Learning
• At its core, a set of algorithms
• Can learn from and make predictions based on recorded data
• ML model is a set of explanations of the relationships between input
features and output predictions
• deployed where explicit programing is too rigid or is impractical
https://biguru.wordpress.com/2015/02/05/a-gentle-introduction-to-
machine-learning/

21. Machine Learning
• At its core, a set of algorithms
• Can learn from and make predictions based on recorded data
• ML model is a set of explanations of the relationships between input features and
output predictions
• Predicts future outcomes based on past trends and transaction
• Deployed where explicit programing is impactical
https://biguru.wordpress.com/2015/02/05/a-gentle-introduction-to-machine-
learning/

22. Machine Learning Use Cases
• Healthcare – Virtual assistants, wearables, lifestyle management and monitoring, ER & surgery,
drug discovery, mental health, patient data and risk analytics, hospital management
• Fintech – credit scoring/direct lending, regulatory compliance, fraud detection, personal finance
assistants, robo-advisors, insurance
• Security – cybersecurity, data security
• Marketing & Advertising
• Facial recognition – Image & Video classification / Segmentation
• Speech recognition
• Natural Language Understanding – NLP
• Personalization - Recommendation engines

23. Deep Learning
• Subfield of ML that uses several layers of algorithms called neural
networks (algorithms that mimic the human brain)
• Algorithms not limited to create an explainable set of relationships
• Convolutional NNs are good for image recognition
• Long short-term memory networks are good for speech recognition
• Steep learning curve because of low-level APIs

24. Deep Learning

25. Neural networks

26. Neural network architectures
https://medium.com/@xenonstack/overview-of-artificial-neural-networks-and-its-applications-
2525c1addff7

27. Using deep learning
What would interest you more:
• In-depth tutorials on deep learning
• How to use ML frameworks
• How to use ML APIs in practice

28. APIs
• Vision
• Speech
• Language

29. ML frameworks
• MLLib – Apache Spark ML library
• Scikit-Learn (Python math and science)
• Tensorflow - data flow graphs, neural networks
• Apache MXNet
• Caffe – deep learning
• H20.ai – extend to Hadoop and Spark (Sparkling water)
• Microsoft Cognitive Toolkit (vision, speech, language, search apis)
• Amazon services
• Google services

30. Spark & Deep learning pipelines
https://databricks.com/blog/2017/06/06/databricks-vision-simplify-large-scale-deep-learning.html

31. Spark & Deep learning pipelines
https://databricks.com/blog/2017/06/06/databricks-vision-simplify-large-scale-deep-learning.html

32. Chatbots
• Bot frameworks – Microsoft Bot Framework, Api.ai, Facebook bot
engine
• Bot platforms – Chatfuel, Beep Boop, Botengine.ai

33. AI in product strategy

34. AI across industries

35. Takeaways
• Product strategy with AI in your product
• Specific business goals and success metrics
• Agile model not always suitable for data /AI products
• People and process as important as technology
• Invest in user-centric design