Azure Machine Learning: Proof of Concept 4-6 weeks
In 4-6 weeks, T-DAB will deliver an Azure Machine Learning Proof of Concept to accelerate your analytics & machine learning projects.
T-DAB is a data science and engineering innovation company specialising in the development and delivery of bespoke, high-end machine learning solutions.
T-DAB is your key AI & Analytics partner, for guiding you on your data & analytics journey through our proven development process; bringing industry and domain best practice and insight to your business.
T-DAB will deliver a Proof of Concept using 3 key components to help you to:
identify valuable opportunities for analytical investigation through a collaborative Discovery Exercise; specifying key insights and objectives, researching the latest advances in academia and industry, prioritising your solutions and presenting a technology roadmap and action plan.
assess the suitability of your data and architecture through a Data & Architecture Review; assessing data structure, format and quality, the suitability for applications, and designing target architecture to accelerate your project with the right Azure services, and,
validate the possibilities and insights from your chosen analytical concept through an Exploratory Data Analysis; wrangling & munging your data, analytical exploration; data mining with unsupervised ML and basic statistical modelling to present key insights for you utilise in your business.
T-DAB will also review your current architecture and recommend services you can utilise to effectively and quickly return value for your project. If you are not currently using Azure services, this will include an Azure architecture roadmap for your future concepts.
To conclude the study, we will devise an action plan to include recommendations and an initial data & AI roadmap to help you take your next steps with iterative sprints to build and pilot your initial data concepts in Azure.
The cost can vary depending on the scope/scale of the use cases and services selected and number of use cases selected to investigate.