From the course: Azure Spark Databricks Essential Training

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Use Databricks Runtime ML

Use Databricks Runtime ML

From the course: Azure Spark Databricks Essential Training

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Use Databricks Runtime ML

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- So today, in this course, we've been focusing on the notebook as the driving interface for our workflows. We've been running notebooks, which have been training machine learning models for most of our workflows, although that's not required, there are Spark workflows that don't include machine learning models. In some cases, we've been creating streams, and we haven't got yet to the point of serving models. Now, in the previous section, we looked at scaling machine learning models that we wrote. Of course, distributed machine learning is really a key workload for Spark and Databricks. So we're going to dive a little bit deeper into very complex machine learning workloads. So as of this recording, Databricks offers the Databricks runtime for machine learning as a cluster type, and what this is is an environment for machine learning and data science. And I would add the word advanced. Now this is an area of active development on the platform and what I'm going to be showing you here…

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