Designed for users who are interested in improving forecasting capabilities through AI applications
DESCRIPTION: Design an appropriate and scalable solution allowing you to obtain reliable business previsions (financial, demand, supply chain, quality, and other forecasting scenarios) by melting the most advanced analytical methodologies with the Azure AI-Machine Learning and data visualization ecosystem as ML Studio and Notebook. Within a well-designed time series analysis, the proposed AI forecasting PoC will be structured in order to perform a preliminary Exploratory Data Analysis (EDA) on a selected sample of data and summarize the main characteristics (e.g. pattern detection, spot outliers, hypothesis test) through an analytical notebook. The effective description of the underlying fundamental factors determining the observed cases represents an important added value of the solution.
PLANNING: DAY 1 - Briefing about the POC scope and high-level details of setting up the Azure analytical ecosystem. Data selection and understanding session with Business team DAYS 2–9 - Set up of AI and ML environment. Onboard datasets and configure model parameters. Train and test the forecasting models and run predictions. Develop a model explainability notebook highlighting the most important features (where applicable) DAY 10 - Roadmap based on POC results and insights
AUDIENCE: ICT Dept., Data Scientists, Business Analysts
LANGUAGES: Italian or English