Developing models with a Databricks Community Edition environment
In many scenarios of small teams and companies, starting up a centralized ML environment might be a costly, resource-intensive, upfront investment. A team being able to quickly scale and getting a team up to speed is critical to unlocking the value of ML in an organization. The use of managed services is very relevant in these cases to start prototyping systems and to begin to understand the viability of using ML at a lower cost.
A very popular managed ML and data platform is the Databricks platform, developed by the same company that developed MLflow. We will use in this section the Databricks Community Edition version and license targeted for students and personal use.
In order to explore the Databricks platform to develop and share models, you need to execute the following steps:
- Sign up to Databricks Community Edition at https://community.cloud.databricks.com/ and create an account.
- Log in to your...
