Machine Learning Automation
IoT in combination with Robotics, Artificial Intelligence, Augmented Reality and Edge Computing is fundamentally transforming production systems. Simple assembly lines up to large manufacturing plants take advantage of deployed sensors and data analytics to optimize their production & supply chains.
Composing and deploying workflow automations that combine data retrieved and ERP systems, real-time streams coming from IoT sensors along with modern Machine Learning algorithms are tedious procedures needing strong data engineering expertise.
In addition, scalability, multi-site management, limited compute power infrastructure at the edge and possible off-line mode are some more issues that complexify workflow deployments.
Ryax offers a data engineering platform that facilitates creation and deployment of data analytics workflows by proposing features such as integrations to traditional protocols and systems, offering low overhead orchestration for edge deployments, seamless edge-cloud execution and multiple supported machine learning frameworks.
Ryax applied to Machine Learning
Complete user walkthrough
Connect data sources
With RYAX
Seamless data sources connection. Integrated & automated event-driven procedures.
- Data Scientist can connect data sources at their own level
- Functions to deal with incidental events are already pre-coded, Data Scientists can configure them in a few minutes with no specific IT knowledge
- Ryax provides high-level visualisation of these functions, enabling full transparency and easy reporting to clients and/or hierarchy
Manage complex stream processing tasks
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With RYAX
Integrated & automated management of complex, asynchronous live data from multiple industrial sources.
- Stream Processing functions are already pre-coded, Data Scientists can configure them in a few minutes with no specific IT knowledge
- Ryax provides high-level visualisation of these functions, enabling full transparency and easy reporting to clients and/or hierarchy
Integrate the Predictive Maintenance workflows
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With RYAX
Data Scientists can drop their Predictive Maintenance algorithms in the treatment chain. They can focus on what they are good at.
- Data Scientists can use pre-established standard functions for all main actions (send to, filters, loops…etc) that are already interfaced
- Functions are evolutive and get richer over time
Manage multi-language frameworks
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With RYAX
Data Scientists can expand to multiple interconnectable frameworks.
Data Scientists can focus on what they do best, thanks to Ryax’ precoded functions:
- Data Scientists can use pre-established standard functions for all main actions (send to, filters, loops…etc)
- Functions and frameworks interfacing is already provided
- Function libraries are evolutive and get richer over time
Support for frameworks Scikit-Learn, TensorFlow, PyTorch, and all pretrained computer vision models
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