Unraveling AI Entrepreneurs Tips is a new video series brought to you by Sandeep Uttamchandani, Chief Product Officer, for engineers and product managers. In Unraveling AI Entrepreneurs episode #4, Sandeep answers the question: What is the operational complexity of your ML model?

Unraveling AI Entrepreneurs Tips is a new video series brought to you by Sandeep Uttamchandani, Chief Product Officer, for engineers and product managers. In Unraveling AI Entrepreneurs episode #3, Sandeep discusses how to measure the success criteria of your AI solution.

Do you have a billion dollar idea that you can solve using AI? Unraveling AI Entrepreneurs Tips is a new video series brought to you by Sandeep Uttamchandani, Unravel Data Chief Product Officer, for engineers and product managers. Learn More About Unravel Data

Unraveling AI Entrepreneurs Tips is a new video series brought to you by Sandeep Uttamchandani, Chief Product Officer, for engineers and product managers. In Unraveling AI Entrepreneurs episode #2, Sandeep frames the AI problem by covering the Journey Map.

Databricks Monitoring, Observability, Optimization and Tuning using AI Unravel for Databricks

Are you looking to optimize costs and resource usage for your Spark jobs on Amazon EMR? Then this is the webinar for you. Overallocating resources, such as memory, is a common fault when setting up Spark jobs. And for Spark jobs running on EMR, adding resources is a click away - but it’s an expensive click, so cost management is critical. Unravel Data is our AI-enabled observability platform for Spark jobs on Amazon EMR and other Big Data technologies. Unravel helps you right-size memory allocations, choose the right number of workers, and map your cluster needs to available instance types.

Are you looking to optimize costs and resource usage for your Spark jobs on Databricks? Then this is the webinar for you. Overallocating resources, such as memory, is a common fault when setting up Spark jobs. And for Spark jobs running on Databricks, adding resources is a click away - but it’s an expensive click, so cost management is critical.

Spark jobs require resources - and those resources? They can be pricey. If you're looking to speed up completion times, optimize costs, and reduce resource usage for your Spark jobs, this is the webinar for you.For Spark jobs running on-premises, optimizing resource usage is key. For Spark jobs running in the cloud, for example on Amazon EMR or Databricks, adding resources is a click away - but it’s an expensive click, so cost management is critical.

Amazon EMR is growing in popularity, and is emerging as the leading platform for big data processing on AWS. EMR is the preferred platform for “lift and shift” migration of existing Hadoop and Spark workloads to the cloud, with minimal refactoring. You get better control, enhanced flexibility, and greater responsiveness.

The popularity of Databricks is rocketing skyward, and it is now the leading multi-cloud platform for Spark and analytics workloads, offering fully managed Spark clusters in the cloud. Databricks is fast and organizations generally refactor their applications when moving them to Databricks. The result is strong performance. However, as usage of Databricks grows, so does the importance of reliability for Databricks jobs - especially big data jobs such as Spark workloads. But information you need for troubleshooting is scattered across multiple, voluminous log files.