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Apache Spark and Agile Model Development

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. Apache Spark has quickly become a critical technology for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. Machine learning and AI has begun to unlock new possibilities that are creating a competitive advantage for companies. However, companies continue to struggle to increase the productivity of data scientists. The biggest hurdle to accelerate innovation has been the time to train, validate and deploy models.

Join us for this latest Data Science Central webinar and hear from Richard Garris, Principal Solutions Architect at Databricks, as he shares his various experiences across multiple industries assisting customers with best practices for building deep learning pipelines driving agile model development practices.

You will learn how to:

Quickly train, validate and deploy different models by leveraging Apache Spark and cloud technologies
Iterate through multiple models quickly by unifying the cloud infrastructure with big data processing capabilities
Integrate workflows with data engineering and data science teams simplifying augmenting data to iterate on models
Increase collaboration and agility within data science teams to improve quality and decrease time-to-production

Richard Garris, Principal Solutions Architect -- Databricks
Wayne Chan, Senior product marketing manager -- Databricks

Hosted by:
Bill Vorhies, Editorial Director -- Data Science Central

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