Energy ML & MLOps Accelerator
By:
Data Reply GmbH
This Well-Architected Framework for DevOps and MLOps is based on best practices as defined by best-in-class companies and implemented using both AWS EKS and Sagemaker-based architectures. It accelerates the journey to production deployment and to becoming data driven. The accelerator is dedicated to supporting a large variety of use cases and is easily extensible and customizable.
Overview
Machine learning application development and the setup of data ingestion, pre-and-post processing, orchestration, monitoring, and visualization infrastructure can be time-consuming and expensive. The maintenance of ML applications necessitates the adaptation of complex MLOps processes and technologies.
By leveraging AWS Cloud services and pre-built models and templates, the Data Reply Energy ML and MLOps Accelerator offers a performant and cost-efficient solution to overcome these issues and to allow manufacturers to unlock the substantial benefits that machine learning can offer.
It is dedicated to complementing the traditional industrial monitoring, and demand and production forecasting tools to enable insights that you would not get from these solutions alone.
The concept is dedicated to loop in right from the beginning your experts on both sides of the process: On the one hand side the data experts that control the data sources, and on the other the end-user who needs insights and decision-making support to optimize the energy production & delivery process.
The accelerator implements essential elements of your digital feedback loop. It is intended to provide an integration between a solution running in the AWS cloud and on-edge, and your existing control room environment. It is dedicated to complementing the traditional industrial monitoring, and demand forecasting tools to enable insights that you would not get from these solutions alone.
| Sold by | Data Reply GmbH |
| Categories | |
| Fulfillment method | Professional Services |
Pricing Information
This service is priced based on the scope of your request. Please contact seller for pricing details.
Support
Steps to operate your dedicated accelerator instance:
Analysis: Use Case analysis, data pipelines analysis, historical data availability and quality analysis for model training, feedback loop analysis (supply chain integration analysis, customer integration analysis), end user specific UI (User Interface) requirements, systems analysis (edge and cloud), KPIs (key performance indicators), OKRs (Objectives and Key Results), path to MVP (Minimum Viable Product).
Infrastructure: AWS tech-stack template, scalable construction, deep collaboration and integration with data sources and dashboard users.
Fine-tuning: Training, deployment, and orchestration of pre-built models on Sagemaker or EKS, closing gaps via feedback loop and model optimization, process and front-end UX optimization.
Field test and adaption
To contact us and get a private offer for end-to-end integration, please contact d.smyth@reply.de