Machine learning is no longer just for the AI experts of the world. With ongoing initiatives to democratize the space, it’s for business users and domain users now, too. Users no longer need to have any programming language knowledge to build and deploy machine learning models.

But democratizing machine learning does not mean data scientists and machine learning engineers are now obsolete. When machine learning becomes simplified, it means less time spent  acquiring the data, transforming the data, cleaning the data, and preparing the data to train and retain models. Instead, they can focus on the core aspects of machine learning like unlocking valuable data and enabling business results.

In this podcast, we talk to machine learning solution provider MindsDB about why machine learning is crucial to a business’ data strategy, how democratizing machine learning helps AI experts, and the meaning of in-database machine learning.

Join us as we explore these ideas with: Erik Bovee, Vice President of Business Development, MindsDB Christina Cardoza, Associate Editorial Director, insight.tech

Erik answers our questions about

• The state of machine learning adoption
• Giving businesses the confidence to create machine learning models
• Machine learning challenges beyond skill set
• Benefits of democratizing machine learning for data scientists
• The importance of in-database machine learning
• How data scientists can leverage MindsDB’s platform
• Use cases for in-database machine learning
• The best places to get started on a machine learning journey