• How energy efficiency can improve throughput and in turn profit
• Ways that a smart factory can contribute to net-zero
• The new normal “climate”

• How IoT in the lighting domain can be utilized to other domains to truly become “Internet of Things”
• Examples on how the lights with its embedded sensors intended for energy saving can also be used for space management and indoor navigation.
• This due to IoT technology and open API

• Importance of sensorization and Industry 4.0
• Reusable data model and architecture
• Advanced analytics engine for predictive maintenance
• Improvement of KPI by various Industry 4.0 solutions

• ML Predictive Technologies to improve Equipment Reliability and Performance
• Industrial Applications
• Success Cases and ROI

Machine Learning and AI are beginning to show more and more value across multiple industries for those organisations actively deploying them at scale. However, many are often trapped carrying out Proof of Concept projects, experimenting and developing models with teams struggling to implement solutions and reach production.

• The value of breaking the Proof of Concept (PoC) cycle
• How to retain the services in demand and deploy them through a development pipeline to reduce the costs of innovation.
• How you move between R&D, get out of the PoC loop, the criteria, tools and timescales should you apply, and how you assess value to achieve rapid deployment.
• Our lessons and a case study from working with academia and industry to move machine learning and deep learning models from R&D into production.

• Computer Vision technologies are now mature and heavily used, almost everywhere and for everything. You can develop an algorithm in a few lines of code.
• When it comes to face challenges like miniaturization (smaller and cheaper hardware), autonomy, industrialization (DevOps / MLOps) and scale-up, you have to deal with a large set of skills and technologies, far from Data Science.
• This session is a feedback of how we built Happy and Masky, hybrid smart cameras (edge / cloud) that detect emotions and masks. And how we have integrated it into an IoT platform to scale.