This session looks at how data science workbenches and machine learning automation tools can help business analysts to become data scientists and so meet the demand of business.

Target Audience: CDO, Head of Analytics, Data Scientist, Business Analysts, CIO
Prerequisites: Basic understanding of Data Science
Level: Advanced

Extended Abstract:
The demand for analytics is now almost everywhere in the business. Analytics are needed in sales, marketing and self-service, finance, risk, operations, supply chain and even HR. However, the current shortage of data scientists and the reliance on detailed skills such as programming, has led many corporate executives to question current approaches to development of high value analytical models and ask if they can be accelerated in any way to improve agility and reduce time to value. This session looks at this problem in detail and at how emerging data science workbenches and machine learning automation tools can help reduce the reliance on highly skilled data scientists and allow business analysts to become data scientists and so meet the demand of business.

o            The explosion in demand for analytics

o            Data science and the modern analytical ecosystem

o            Challenges with current approaches to analytics

o            Requirements to reduce time to value and accelerate development of analytical models

o            Improving productivity by integrating Information catalogs and data science workbenches, e.g. Amazon SageMaker, Cloudera CDP Machine Learning, IBM Watson Studio Microsoft, Azure ML Service,

o            Accelerating model development, monitoring and model refresh using ML automation tools, e.g. DataRobot, SAS, Dataiku Data Science Studio, Big Squid

o            Facilitating rapid analytics deployment via analytics as a service to maximise effectiveness and competitive edge