Accelerate your Retail Data Science
If you are a retail data scientist, your time is probably consumed preparing data and performing multiple repetitive tasks before you even get the chance to apply algorithms and evaluate their performance.
Now you can use dunnhumby Model Lab to automate most of those time-consuming steps, allowing you to focus on what you do best - modelling and delivering customer insights that drive profitability and customer loyalty. All faster and more cost effectively. dunnhumby Model Lab is now available on Microsoft Azure. This enables you to take advantage of Model Lab through a simple subscription and get up-and running virtually instantly.
Intuitive GUI and dashboard gets you started quicker
Real-time progress updates keep you informed
Automatically tune machine learning algorithms
Parallel computing and resource optimisation boosts performance and reduces runtime
Model Lab makes it easy to organise and manage your machine learning projects. The project-oriented structure helps data scientists keep track of their work and select the best results for your business, increasing efficiency and reducing time-to-value.
You don't need to move your data to use Model Lab. Model Lab is extremely flexible and will fetch the data on your behalf, using best-in-class protocols to read your data securely from your favourite cloud provider.
Model Lab provides many different options to clean your data, and you can create several cleaned versions from the same original dataset. You can choose from a selection of predefined cleaners that have been optimised for retail problems or you can customise the process to match your needs.
Model Lab gives you access to many pre-defined experiments tailored for retail machine learning problems. It takes care of repetitive tasks for you with highly optimised modules, so you can focus on the design of your experiments and improving your results. The machine learning platform leverages parallel computing and automated hyper-parameters tuning, delivering results in no-time.
Caveats of Machine Learning by Prateek Khushalani and Dr. Victor Robin
Partnership Further Enables the Democratisation of Customer Data Science by Making It Accessible in the Cloud