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
TinyML: Machine Learning
in the edge with FIWARE
components
José Andrés Muñoz Arcentales, Javier Conde

2. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Introduction
▪ TinyML refers to the deployment of machine learning models on
resource-constrained edge devices at the edge of the network.
▪ Optimizing ML models to be compact, efficient
▪ Real-time Inference: Enables immediate responses and low-latency
decision-making, critical for time-sensitive applications.
▪ Model Optimization: Techniques such as quantization, pruning, and compression

3. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Introduction
▪ Learning vs Inference

4. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Introduction (Why we need TinyML at the edge)
▪ Latency and Real-time Processing: Some applications require immediate responses
and low-latency processing.
▪ Bandwidth and Network Efficiency: TinyML allows data to be processed locally,
reducing the need for continuous data transfer to the cloud.
▪ Privacy and Security: With TinyML, data remains on the edge device, minimizing the
risk of data breaches and preserving user privacy.
▪ Offline Functionality: Edge devices are not always connected to the internet or may
experience intermittent connectivity.
▪ Scalability and Cost Reduction: Processing data locally on edge devices reduces the
burden on cloud servers

5. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Challenges and Opportunities:
▪ Challenges:
• Limited computational resources on edge devices.
• Energy constraints for battery-powered devices.
• Lack of standardization and optimization techniques.
▪ Opportunities:
• Real-time and low-latency processing.
• Reduced network bandwidth and latency.
• Enhanced privacy and security.
▪

6. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
MLOps over TinyML at the Edge
▪ Definition: MLOps is the practice of applying DevOps principles to machine learning
workflows.
▪ MLOps for TinyML:
• Adaptation: Applying MLOps principles to
efficiently deploy and manage ML models
on resource-constrained edge devices.
• Key Components:
• Data Acquisition and Preprocessing
• Model Training and Optimization
• Model Deployment, Monitoring, and Updates

7. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
MLOps over TinyML at the Edge
Benefits of MLOps for TinyML:
▪ Streamlined Development and Deployment
▪ Reproducibility and Version Control
▪ Efficient Model Monitoring and Performance Optimization
▪ Scalability and Cost Efficiency

8. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Use case of TinyML training pipeline with FIWARE
Data Acquisition:
▪ FIWARE DRACO
▪ IOT Agents

9. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Use case of TinyML training pipeline with FIWARE
▪ Data and metadata Broker:
• FIWARE Orion
▪ Data Persistance
• FIWARE Draco
▪ MLOps Orchrestator
• Apache Airflow
▪ ML model version control
• MlFlow
▪ Model Deployment and Execution
• FIWARE COSMOS

10. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Use case of TinyML training pipeline with FIWARE
General Architecture:
▪ FIWARE component-based architecture for MLOps lifecycle management adapted
to the automatic irrigation system use case

11. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
General Architecture FIWARE for MLops at the edge
Hands on Workshop session:

12. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
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13. Vienna, Austria
12-13 June, 2023
#FIWARESummit
Thanks!