Azure Data Science: 4-weeks Proof of Concept

What do we do during adoption? We evaluate the chance to use data science in your organization. • We find use cases and choose the one that is most promising for the customer. • We evaluate data sources, their quality and integration processes. • We assess competences and identify gaps. • We transfer knowledge on the use of machine learning tools (Machine Learning): ◦ familiarizing with the environment Azure Machine Learning, ◦ familiarizing with the issues related to the preparation of data for analysis, ◦ using of available econometric / machine learning models for a specific business area, ◦ assessment of the quality of the models developed, ◦ testing and implementation of models, ◦ industrialization of models. • We transfer knowledge about integration and automation processes: ◦ familiarizing with the Azure Data Factory work environment, ◦ data flows and their triggers, ◦ automation. • We run and configure Data Science Sandbox: ◦ we run data warehouse and data integration processes (Azure Data Lake, Azure Data Factory), ◦ we launch machine learning service (Azure Machine Learning). • We prepare the selected model of advanced analysis (machine learning model). • We visualize the results in the Power BI report. • We present ways to industrialize the model, promising the highest return on investment. • We present in the form of a workshop the results of adoption data science together with further development recommendations.

Pilot project products • Knowledge transfer related to Data Science Sandbox. • Configured Data Science Sandbox. • A sample machine learning model. • Visualization in the form of a report Power BI. • Final data science adoption assessment report: ◦ business case, ◦ data science cycle, ◦ vision of development, ◦ environmental costs, ◦ team competencies gap, ◦ data science implementation plan.