DSDT Meetup June 2018

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AI, Machine Learning, Deep Learning: Stop talking and start acting! Agenda : 6:00 - 6:15 PM: Welcome & Networking 6:15 - 6:30 PM: News and Introduction 6:30 – 7:15 PM: How to Get Started With AI 7:15 - 8:00 PM: Commit Assistant – AI to predict and prevent bugs in Ubisoft’s code 8:00 - 8:30 PM: Networking

Technology

Agenda
3
6:15 - 6:30 Meetup News
6:30 - 7:15 (JDA Labs)
How to Get Started With AI
7:15 - 8:00 (Ubisoft)
AI to predict and prevent bugs in code
8:00 - 8:30: Q&A + Networking

Meetup News
4
• June 7: IT Connect Conference - IoT (Seaway Park - St Lambert)
• June 8: Hands-on IoT Workshop (Catallaxy)
• June 12: DataViz User Group #3 (Microsoft)
• June 14: RDV Techno: IoT + AI + Health (Desjardins Labs)
• June 14-15-16: Frontière IA (Le Virage MTL)
• June 18: HackerNest Tech Socials (Spaces Mile End)
• June 19: Google Cloud Platform Fundamentals (Free, women only, Centre Phi)
• June 19: Data Driven Montreal: Data Warehousing vs Data Visualization (OVH)
• June 21: Google Cloud Platform Meetup (Shopify)

5
DSDT Meetup
is 1-year old.
Happy Birthday !!
(More to come next month…)

Topic #1
How to Get Started
With AI
Data Science | Design | Technology 6

Tools to Get Started with Machine
Learning / AI
Christian Hudon | JDA Labs
Twitter: @christian_hudon and @JDALabsMTL
1 / 27

You think machine learning is cool

2 / 27

But what about after that?
Maybe you have a rst project idea...
5 / 27

But what about after that?
Maybe you have a rst project idea...
(Or maybe not.)
6 / 27

Classical machine learning:
Scikit-learn
For NLP: SpaCy
14 / 27

Deep learning:
Pytorch (and Skorch)
* Keras (with Tensor ow) also a reasonable choice.
15 / 27

Visualization (simpler / pre-canned):
Pandas & Seaborn
16 / 27

Visualization (expressive):
HoloViews (or Altair)
17 / 27

How to continue learning?
Do a project!
18 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
19 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
Kaggle
20 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
Kaggle
Your own life! (FitBit, your bank)
21 / 27

Dataset Sources
The Open Data movement (Bixi, etc.)
Kaggle
Your own life! (FitBit, your bank)
The Internet!
22 / 27

Project Ideas
Forecast Bixi usage
23 / 27

Project Ideas
Forecast Bixi usage
Cat vs. dog detector
24 / 27

Project Ideas
Forecast Bixi usage
Cat vs. dog detector
Automated categorization of credit card bill items
25 / 27

After your rst project
Getting better at Python: https://www.youtube.com/playlist?list=PLu5kChXP9Hipqr1gIROxGlvb3YVadOjUB
(14 videos currently)
Learning the basics of classes and object-oriented programming:
http://www.greenteapress.com/thinkpython/html/thinkpython016.html (and next two chapters)
The matrix algebra you need for machine learning: http://parrt.cs.usfca.edu/doc/matrix-calculus/
Scaling with Dask and Dask-ml and Dask-Kubernetes
Learning the basics of SQL
26 / 27

Thank you!
Slides and notebooks:
http://christianhudon.name/talks/#DSDT-2018-06
Tools
Trying things out: Jupyter Lab
Numerical computations: Numpy
Data Exploration: Pandas
Classical machine learning: Scikit-learn (and
SpaCy)
Deep learning: Pytorch (and Skorch)
Visualization: Pandas and Seaborn
More Visualization: HoloViews (or Altair)
27 / 27

Topic # 2
AI to predict and
prevent bugs in
code
Data Science | Design | Technology 1
Ubisoft
La Forge

Merci / Thank You
1
@jdalabsmtl
Data Science | Design | Technology
(Check for next DSDT meetup at https://www.meetup.com/DSDTMTL)

A number of recent milestones in AI have rekindled the faith that human-grade computer intelligence can fuel the next technological revolution. In parallel and almost independently, the job role of Data Scientist rose to one of the hottest tickets in the technology sector. Despite the obvious overlap in the domains of Data Science and Artificial Intelligence, the two approaches are sufficiently distinct that choosing the wrong one might trigger a product to fail or a hiring process to go wrong. This presentation will offer some clarity and best practices with regards to understanding what data analysis requirements you really have, as what opposed to what you think you have.

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Charles Cai has more than two decades of experience and track records of global transformational programme deliveries – from vision, evangelism to end-to-end execution in global investment banks, and energy trading companies, where he excels at designing and building innovative, large scale, Big Data systems in high volume low latency trading, global Energy Trading & Risk Management, and advanced temporal and geospatial predictive analytics, as Chief Front Office Technical Architect and Head of Data Science. He’s also a frequent speaker at Google Campus, Big Data Innovation Summit, Cloud World Forum, Data Science London, QCon London and MoD CIO Symposium etc, to promote knowledge and best practice sharing, with audience ranging from developers, data scientists, to CXO level senior executives from both IT and business background. He has in-depth knowledge and experience Scala, Python, C# / F#, C++, Node.js, Java, R, Haskell programming languages in Mobile, Desktop, Hadoop/Spark, Cloud IoT/MCU and BlockChain etc, and TOGAF9, EMC-DS, AWS CNE4 etc. certifications.

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As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.

20240607 QFM018 Elixir Reading List May 2024

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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...

James Anderson

Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management. The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM). Speakers: Bob Boule Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle. Gopinath Rebala Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.

Securing your Kubernetes cluster_ a step-by-step guide to success !

KatiaHIMEUR1

Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster. However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks. In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.

Epistemic Interaction - tuning interfaces to provide information for AI support

Alan Dix

Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024 https://alandix.com/academic/papers/synergy2024-epistemic/ As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.

Large Language Model (LLM) and it’s Geospatial Applications

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By Design, not by Accident - Agile Venture Bolzano 2024

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DSDT Meetup June 2018

1. Data Science | Design | Technology (June 05, 2018) https://www.meetup.com/DSDTMTL 1

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3. Agenda 3 6:15 - 6:30 Meetup News 6:30 - 7:15 (JDA Labs) How to Get Started With AI 7:15 - 8:00 (Ubisoft) AI to predict and prevent bugs in code 8:00 - 8:30: Q&A + Networking

4. Meetup News 4 • June 7: IT Connect Conference - IoT (Seaway Park - St Lambert) • June 8: Hands-on IoT Workshop (Catallaxy) • June 12: DataViz User Group #3 (Microsoft) • June 14: RDV Techno: IoT + AI + Health (Desjardins Labs) • June 14-15-16: Frontière IA (Le Virage MTL) • June 18: HackerNest Tech Socials (Spaces Mile End) • June 19: Google Cloud Platform Fundamentals (Free, women only, Centre Phi) • June 19: Data Driven Montreal: Data Warehousing vs Data Visualization (OVH) • June 21: Google Cloud Platform Meetup (Shopify)

5. 5 DSDT Meetup is 1-year old. Happy Birthday !! (More to come next month…)

6. Topic #1 How to Get Started With AI Data Science | Design | Technology 6

7. Tools to Get Started with Machine Learning / AI Christian Hudon | JDA Labs Twitter: @christian_hudon and @JDALabsMTL 1 / 27

8. You think machine learning is cool 2 / 27

9. You've taken a rst online class 3 / 27

10. But what about after that? 4 / 27

11. But what about after that? Maybe you have a rst project idea... 5 / 27

12. But what about after that? Maybe you have a rst project idea... (Or maybe not.) 6 / 27

13. Let's Get Started! 7 / 27

14. 8 / 27

15. 9 / 27

16. Who is this presentation for? 10 / 27

17. Trying things out: Jupyter Lab 11 / 27

18. Numerical computations: Numpy 12 / 27

19. Data Exploration: Pandas 13 / 27

20. Classical machine learning: Scikit-learn For NLP: SpaCy 14 / 27

21. Deep learning: Pytorch (and Skorch) * Keras (with Tensor ow) also a reasonable choice. 15 / 27

22. Visualization (simpler / pre-canned): Pandas & Seaborn 16 / 27

23. Visualization (expressive): HoloViews (or Altair) 17 / 27

24. How to continue learning? Do a project! 18 / 27

25. Dataset Sources The Open Data movement (Bixi, etc.) 19 / 27

26. Dataset Sources The Open Data movement (Bixi, etc.) Kaggle 20 / 27

27. Dataset Sources The Open Data movement (Bixi, etc.) Kaggle Your own life! (FitBit, your bank) 21 / 27

28. Dataset Sources The Open Data movement (Bixi, etc.) Kaggle Your own life! (FitBit, your bank) The Internet! 22 / 27

29. Project Ideas Forecast Bixi usage 23 / 27

30. Project Ideas Forecast Bixi usage Cat vs. dog detector 24 / 27

31. Project Ideas Forecast Bixi usage Cat vs. dog detector Automated categorization of credit card bill items 25 / 27

32. After your rst project Getting better at Python: https://www.youtube.com/playlist?list=PLu5kChXP9Hipqr1gIROxGlvb3YVadOjUB (14 videos currently) Learning the basics of classes and object-oriented programming: http://www.greenteapress.com/thinkpython/html/thinkpython016.html (and next two chapters) The matrix algebra you need for machine learning: http://parrt.cs.usfca.edu/doc/matrix-calculus/ Scaling with Dask and Dask-ml and Dask-Kubernetes Learning the basics of SQL 26 / 27

33. Thank you! Slides and notebooks: http://christianhudon.name/talks/#DSDT-2018-06 Tools Trying things out: Jupyter Lab Numerical computations: Numpy Data Exploration: Pandas Classical machine learning: Scikit-learn (and SpaCy) Deep learning: Pytorch (and Skorch) Visualization: Pandas and Seaborn More Visualization: HoloViews (or Altair) 27 / 27

34. Topic # 2 AI to predict and prevent bugs in code Data Science | Design | Technology 1 Ubisoft La Forge

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37. Fixes Fixes Fixes Fixes Fixes Fixes Fix

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