The accuracy and integrity of data science is still being questioned by some. Over the past few years however, the understanding of data science’s real value has deepened, and its applications have proven to be successful in various industry verticals. Interest toward flexible models and quick verification has grown. While investments in data science talent, technology and digital infrastructure continue to rise.
Lee Harper, Principal Data Scientist at Catapult Systems, discussed:
• Should I run my environment on-premises or in the cloud?
• Capital investment vs recurring operational expenditure
• The differences between cloud IaaS, PaaS and SaaS offerings
• Platform flexibility vs ease of use and low/no-code solutions
• Should your development and production environments use the same tooling?
Note – this webinar took place on Thursday 08 April 2021.
Lee Harper has 7 years of professional data science experience, including 3 years in consultancy roles.
As the data science lead at Catapult Systems, Lee has worked with multiple companies, in both the public and private sectors, to define tractable early use cases to get their data science practice off the ground, and to build Minimum Viable Product end-to-end data science solutions for them using Microsoft Azure.
Lee has also successfully worked with a Fortune 50 company to architect and standardize their cloud deployment and machine learning automation strategy across more than 10 federated data science teams. This involved developing architected solutions, building MVP demos, coaching data engineers and data scientists, and refactoring data scientists’ code to make it more efficient, testable, and productionable.