Azure Machine Learning Implementation: 6-Wk Implementation

Jarmany Ltd

Using your data, we can create insights using AzureML to help predict trends and help make all your data-based decisions.

Are you:

  • Wanting to get more out of your data?
  • Using a data model to get current measures, but wanting to establish trends and different outputs from your data?
  • Searching for the answers to any future questions, and wanting to use data to do it?

Machine Learning and AI are hot topics in the world of data. Getting more from your data, just makes sense and at Jarmany we can help you to do this. From forecasting models to predictive applications, we know how to create efficient Machine Learning architecture to get the outcome you want. We know what statistical model and learning type you need to help make those predictions as accurate as possible. AzureML provides a great platform for all your Machine Learning needs, and it is fully integrated into the Azure environment. It can connect to a variety of Data Warehouses and can handle as much, or as little data as required. Therefore, we recommend it as the best service to use when dealing with Machine Learning and AI.


  • Initial review of your current data and investigating what outcome you want
  • Evaluation of the best approach to modelling and statistical methodology to use
  • Implementation of the decided approach following strict best practice to produce the most accurate output possible
  • Visualising the output in an easily accessible and understandable way, so you can answer your own predictive questions whenever you wish

About Jarmany: As a Microsoft Gold Partner with extensive Machine Learning experience Jarmany’s tried and tested methods of Machine Learning has helped many clients discover more from their data. We have created several ML models and have delivered them in effective ways so clients have solid, accurate predictions with which they can make great data-backed business decisions.

*The cost can vary depending on the scope/scale of the use cases and services selected and number of use cases selected to investigate.