Are you ready to take your smart solutions to the next level? During this session, you will learn how to integrate AI and ML platforms for analysis and processing of digital twin data in systems based on FIWARE. In this session, we'll cover all aspects of ML Ops, including how to deploy ML in edge systems, and show you how to use AI and ML to turn data into actionable insights and business value. You'll discover how to leverage the power of AI and ML to optimize your smart solutions and gain a competitive edge. You'll learn how to implement ML Ops in solutions powered by FIWARE, enabling you to easily deploy machine learning models and update them in real-time. We'll also discuss how to deploy ML in edge systems, allowing you to process data locally and avoid the latency of sending it to a central server. This session will invite participants to discover how to put data to work and turn it into wisdom that drives business value. Moreover, different experiences on how to automatize ML, especially regarding training and deploying ML models into solutions powered by FIWARE in different real scenarios. If you are interested in learning how to turn machine learning models towards perfection and deliver ML solutions more easily as part of solutions powered by FIWARE, to extract the most out of data, this session is for you.

1. Vienna, Austria
12-13 June, 2023
#FIWARESummit
From Data
to Value
OPEN SOURCE
OPEN STANDARDS
OPEN COMMUNITY
The need of effective data flows for
Machine Learning and Deep Learning;
from Structure, Learning to Performance
on Applications
Miguel González Mendoza

2. AAI – Group description
► AAI – RG aims to conduct research on all aspects of
Artificial Intelligence, including machine learning,
computational intelligence, hyper-heuristics and
computer vision.
► We use our methods or processes to Data Science,
including video content analytics and business
process mining.

3. Advanced Artificial Intelligence
Group Members
Raúl Monroy Julieta Noguez
Hugo Terashima
Miguel González César Torres Santiago Conant Luis Trejo
José Cantoral
Luciano García
Gilberto Ochoa
Iván Amaya
Edgar Covantes
José Carlos Ortiz
Guillermo Falcón
Jorge Cruz Salvador Hinojosa
Rajesh Biswal
Gildardo Sánchez
V. Sosa

4. AAI – RG: Part – Time Members
A. Aguilar M. Alfaro J. Alvarado R. Brena F. Cantú
H. Ceballos L. Falcón J. Miranda L. Neri J. Nolazco
J. Rodríguez C. Santiesteban A. Santos

5. Why data flows in ML?
Evolution from Scientific to Technical Impact

6. Only a small fraction of real-world ML
systems is composed of the ML code
As shown by the small black box in the middle. The required
surrounding infrastructure is vast and complex
Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural
information processing systems 28 (2015). https://dl.acm.org/doi/10.5555/2969442.2969519

7. Machine Learning main key issues
Aparatus (Hardware) Algorithm scheme Computing Methods
DM, ML, PR
Algorithms
High level abstraction
Super Computing Parallel Analytical methods Density based
approaches
Deep Learning
GPU/TPU Computing Distributed Numerical methods Bayesian approaches Meta Learning
Personal Computing Synchronous Optimization Border based approaches Auto ML
Embeded systems
computing
Asynchronous Search Entropy based
approaches
…
< Quantum Computing > Computational complexity … … …
… … … … …
Hw-Sw Design Application requirements
Application
Areas

8. Deep Learning Architectures
Fatsuma Jauro, et al. “Deep learning architectures in emerging cloud computing architectures: Recent development, challenges
and next research trend,” Applied Soft Computing, Volume 96, 2020. https://doi.org/10.1016/j.asoc.2020.106582.

9. Context of learning in CNN
Learning Algorithms and Aparatus: Case in Image Processing

10. Price/Performance
Moore’s Law
Processor
Technology
2000 2020
Firmware / OS
Accelerators
Software
Storage
Network
We are here
44 zettabytes
unstructured data
2010 2030
structured data
Data
Growth
Today’s challenges demand innovation

11. Enterprise Deep Learning Distribution
Auto Parameter
Tuning and
Monitoring
Image
Classification
Deployment of
Inference
Image
Labeling
Object
Detection for
Videos
Object
Detection
i.e. IBM PowerAI Vision

12. Computer Vision applications: expansion of
capabilities
Autonumous Driving
Object Recognition [1] Protein Folding [2]
[1] You Only Look Once: Unified, Real-Time Object Detection, Joseph Redmon et. al, 2015; https://arxiv.org/abs/1506.02640
[2] Highly accurate protein structure prediction for the human proteome, Kathryn Tunyasuvunakool et. al, 2021;
https://www.nature.com/articles/s41586-021-03828-1

13. Binarization
13

14. i.e. Binarized algorithms:
Bop2ndOrder vs Bop
• Biased à 15-22% more training
times.
• Unbiased à 20-28% more.
CIFAR10 Comparison
• ImageNet 2012
Comparison
• Biased à Better for fine-
tuning.
• Uniased à More stable.
Suarez-Ramirez, C. Gonzalez-Mendoza M et al. “A Bop and Beyond: A Second Order Optimizer for
Binarized Neural Networks.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR) Workshops, 2021, pp. 1273-1281

15. Some Applications
Videosurveillance

16. Smart Security: Facial Recognition
Detection + tracking + identification (State of the art
Techniques, +10 fps)
Video surveillance
1
• realtime dataflow for video and
metadata
2
• low computational cost and
continuous improvement
3
• a complete R&D + DevOps
scenario for students &
professors

17. i.e. Digital me
• The computer animation industry
has been wanting to capture
facial performance
• keyframe animation to motion
capture the approaches to
expressing emotions
• SoA approaches for:
• Facial recognition
• Landmark detection
• Expression classification

18. i.e. Towards an Edge Real-Time Detection of
objects and behaviours in Video
MA Duran-Vega, M Gonzalez-Mendoza. “TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent
Neural Networks for Real-Time Handgun Detection in Video” arXiv preprint arXiv
Camarena, Fernando, et al. "Action recognition by key trajectories (Jan, 10.1007/s10044-021-01054-z,
2022)." PATTERN ANALYSIS AND APPLICATIONS 25.2 (2022): 485-485.

19. Some final toughts

20. Potential Directions
1. Bridging Data Gaps
• Gap between small data and big data, and non-perfect data
• In the absence of perfectly labeled training data, possible alternatives are to use unsupervised
learning, self-supervised learning
2. Developing Algorithms
• we are still expecting novel ML algorithms or schemes to further improve system modeling and
optimization, with respect to scalability, domain knowledge interpretability, and so on.
3. Improving Implementations and Deployments
• to consider practical implementations, appropriate selection of deployment scenarios, and post-
deployment model maintenance

21. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
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22. Vienna, Austria
12-13 June, 2023
#FIWARESummit
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