Forecasting Crypto Currency Prices with Cloudera’s Applied ML Prototypes (AMPs)


Cloudera’s library of Applied ML Prototypes (AMPs) provides Data Scientists with pre-built reference examples and end-to-end solutions, using some of the most cutting edge ML methods, for a variety of common data science projects. Every AMP includes all required libraries and their dependencies, industry best practices, prebuilt models, and a business-ready AI application — All deployable with a couple clicks, allowing Data Science teams to start a new project with a working example that they can then customize to their own needs.

In this session, Cloudera will demonstrate how an AMP can be used for structural time series analysis. An Auto ML approach will be employed to forecast future cryptocurrency prices. To facilitate easy application usage, a Web-based, RESTful endpoint will be exposed to retrieve model predictions.


Ade Adewunmi is responsible for Machine Learning Services at Cloudera Fast Forward. She spends her days advising clients on the data-enabled transformation of their organisations with a particular focus on the systematic integration of machine learning into their business operations.

Prior to joining Cloudera Fast Forward Labs, Ade worked as a consultant, advising organizations on the development and delivery of their data strategies. Before that, she led the UK-based Government Digital Service’s Data Infrastructure programme.

Outside of work, Ade’s interests in the application and impact of data are broader - beyond the boundaries of corporate organisations; she volunteers with civil society organisations such as Datakind UK, mySociety and Foxglove Legal.

She blogs about the ways in which data can be made useful for organisations and wider society as well as the leadership and organisational cultures that make this possible. When she’s not advising, blogging or speaking about these things, she’s almost certainly watching too much TV and justifying it on the grounds of maintaining cultural relevance (as if any justification were needed!).

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