We at Pluto7, a services and solution company focused on leveraging the power of Google Cloud Platform to build ML, Ai, Analytics, and IoT tailored solutions, feel proud to announce that we have published the first Kubeflow artifact demand ML solution on Google’s AI hub. This prebuilt model pipeline will enable time series forecasting for ubiquitous data using Tensorflow and Conv1D.
The model takes a ubiquitous time series data where the user needs to define the date and the target feature variable.
This will empower users to generate forecasts by uploading data for different use cases such as store traffic, revenue and scenarios where nature of data is historical and there is a need of time series prediction for future forecast. These forecastings will be performed on target feature value for upto 60 days.
Kubeflow helps orchestrate the deployment of Machine Learning workflows in the Kubernetes engine through the full cycle of development, testing, and production, while allowing for resource scaling as demand increases.
This solution helps supply chain and demand teams achieve highly accurate forecasts while optimizing supply chain planning and manufacturing operations. In addition, it accelerates cost reduction and help plan for desired demand levels.
Our Demand ML solution can be implemented for other workloads as well, since it creates a single step Kubeflow pipeline that orchestrates the machine learning workflow developed for demand forecasting.
This plug and play AI component is a great way to pilot test demand ML solutions on businesses.
This was just the beginning. Pluto7 is excited to publish the solutions for Marketing ML in coming days, which will be publicly available on AI Hub.
For more information or any other questions, feel free to write to us at email@example.com
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