Leverage Azure ML and Empiric’s data modelling techniques and experience to solve your complex business problems
Why undertake an Azure Machine Learning (ML) project? - The application of AI & ML converges analytics, data science and automation to accelerate and drive successful business outcomes such as increasing organisational performance, automation and efficiency, reducing operational costs and driving better customer relevance and profitability. This is achieved by mimicking human cognitive functions of learning and problem solving (AI), together with the process of using mathematical models of data to help a computer learn without direct instruction (ML) – with ML being a subset of AI.
Empirics provide advanced Data Science and Analytics services and solutions for a wide range of Industry sectors to drive customer growth, retention, and engagement strategies using Azure ML. With our heritage in financial services, our Data Science and Analytics teams are specialists in a wide range of Machine Learning and AI data modelling techniques, modelling languages including Python and R, predictive analytics, data visualisation and sector specific problem solving.
An Empirics lead Azure ML project is completed via the following core phases: • Requirements Gathering – Defining the business problem to solve using Azure ML • Empirics Workbench Set up – Configuration of the Azure ML toolset for your project • Exploratory Data Analysis - Review, analysis and oversight of available data for project using PowerBI • Model Selection & Preparation – Defining the modelling technique that best suits the project using Azure ML • Model Training – Passing data through the model to calibrate the model performance using Azure ML • Model Debug & Tuning – Refining the model to optimise performance • Model Validation – Model pattern identification • Model Testing – Running the model and testing results • Output Visualisation – Surfacing the results of the modelling using PowerBI
Key Benefits • Use the Azure ML toolset to model your data to answer complex questions and test hypothesis that drive your strategies • Leverage Azure ML technologies to easily deploy data models into production and operationalise your insights to teams across your business • Get the information into the hands of your business stakeholders using live and refreshed data via PowerBI dashboards • Establish the best methodologies and approach surrounding data modelling options and techniques to solve your business challenges and to maximise your return on investment from Azure
Example Azure ML projects can include: • Project Consultancy & Proof of Concepts (POC’s) • Predictive Modelling (e.g. churn models, likelihood to invest/purchase a product or service) • Customer Projected Value • Customer Segmentation and Clustering • Investment Switching Analysis and Forecasting • Next Best Interaction modelling