Systems | Development | Analytics | API | Testing

In this video, enterprise data and machine learning experts Sam Charrington (TWIML) and Sushil Thomas (Cloudera ML) discuss what is required to effectively operationalize ML in the enterprise — from requirements across the ML lifecycle to enabling decision-makers.

In this video, we’ll go over how you can use both Cloudera Public Cloud to both Ingest data through Cloudera Data Engineering as well as explore it through Hue and Impala within Cloudera Data Warehouse. You'll see how easy it is to run queries that give you insight into your data and how you can use a built in data visualization tool to then create a dashboard to share your results.

In this video, we’ll go over how to use Cloudera Machine Learning (CML) to validate a complex predictive model. Using a publicly available NASA dataset that simulates how jet engines degrade over time, we’ll use machine learning concepts in a cloud environment to go from simulation data to a cost benefit analysis in just a few steps. We’ll also show how we can run experiments to track specific metrics from many different scenarios that our predictive model could possibly be implemented in.

IT is no longer relegated to the IT group. Lines of business are building new business applications that can drive their business’s top and/or bottom lines. These applications are increasingly stateless -- meaning that they rely on their underlying operational database to manage their state and work with IT to build, deploy and manage the database infrastructure. The application development lifecycle is accelerating with the broad adoption of cloud and the rise of dbPaaS where the database is fully managed and self-optimizes for the applications. In this session, we will show you how the Cloudera Operational Database offers an accelerated on-ramp to app development by offering a modern multi-model database that eliminates infrastructure management.

In this video we're going to go over some more advanced features of the Cloudera Data Engineering Experience. Using some publicly accessible Paycheck Protection Data, you'll see how to automatically setup Spark jobs to deploy by using the CDE CLI, making development and deployment times much quicker and painless. We'll also take the development cycle through to the end and get some visualization of the finished reports using the aforementioned PPP data.