News•Oct 15, 2022
Credera Supports Denver Startup Week 2022 and Talks Machine Learning Operations
Denver Startup Week is a celebration of everything entrepreneurial in Denver and the largest free event of its kind. This year’s event was the 11th anniversary of Denver Startup Week (DSW) and returned to an in-person format for the first time since 2019.
The weeklong event is intended to unite the entrepreneurial community with established businesses in Denver to celebrate great companies, innovation, ideas, and people. It included learning sessions, presentations, workshops, and many other happy hours and celebratory events. The 2022 event reached over 10,000 attendees with over 230 sessions.
This year, the Credera team was proud to be a sponsor of Denver Startup Week 2022. DSW is completely funded by sponsors, and as a member sponsor of the event, we had the opportunity to live out Credera’s mission to make an extraordinary impact on our communities.
This year, Credera also had the opportunity to present content as well as attend sessions. There were over 45 leaders and experts from our Credera Denver office with expertise in management consulting, innovation and product, experience design, data and insights, MarTech and commerce, and cloud and engineering.
Those who attended engaged with the community on topics in product, tech development, design, business growth, innovation, and people and culture. We walked away from the event with new knowledge and perspectives as well as meaningful new connections. We are grateful to have attended sessions and events aimed at equipping entrepreneurs, business leaders, and established businesses for success.
Credera’s Denver Startup Week Session
Credera’s session covered machine learning operations (MLOps) and was presented by Amanda Aschenbrenner, a senior architect in the Denver office, Matt Pattermann, an architect in the Dallas office, and Cole Harrison, a senior consultant in the Dallas office.
Did you miss the session, but want to learn more? You can watch the session here.
Session Description: Machine learning has evolved into a necessity for organizations that wish to improve customer experience, reduce costs, and build innovative solutions to complex problems, with a 2021 Forbes survey finding that 76% of enterprises prioritize machine learning (ML) over other IT initiatives.
However, even companies with an abundance of data scientists are struggling to deliver on the promises surrounding the buzz of ML for three major reasons:
No method to efficiently monitor and improve production models as they get stale over time.
Data scientists spend more time on maintenance than innovation.
Data product teams start from scratch on new ML applications.
The common threats posed by these three pain points have led to the emergence of a new discipline in data science, MLOps. MLOps is a set of practices to combat issues with productionizing and maintaining models in machine learning. It borrows the ideologies of version control, automation, and continuous integration/continuous delivery (CI/CD) from DevOps, but has an added layer of complexity due to the use of data and artificial intelligence. One of the chief issues addressed by MLOps is ensuring that models continue to work as expected in production.
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