Unlocking the Power of MLOps: Scalable and Sustainable Machine Learning
Details
Outline
As organizations scale up their use of machine learning models, it becomes critical to manage the ML lifecycle in a standardized and streamlined way. MLOps provides a framework for operationalizing machine learning, from model development through deployment, monitoring, and continuous improvement.
In this discussion, we’ll explore the key factors that determine an organization’s need for MLOps. We’ll delve into how the number of models, model complexity, associated risks, and rate of change in the environment impact the necessity of adopting MLOps practices. Discover how implementing MLOps can help organizations lower production costs, iterate on models faster, increase data science team satisfaction, and govern ML initiatives more effectively.
Throughout the session, we’ll highlight the critical capabilities of MLOps platforms, including model deployment processes, specialized monitoring, lifecycle management, and governance controls. We’ll also discuss the importance of starting the MLOps journey early to prevent the costs and technical debt of unmanaged models from overtaking the value being generated.
Attendees will gain valuable insights into transforming complex and bespoke ML processes into scalable ones that consistently deliver value as organizations expand their use of AI and ML.
Presenter
Grant Case is the Regional Vice President for Sales Engineering for Dataiku in APJ. He holds an MBA from Indiana University with concentrations in Finance, Decision Support Modeling, and Marketing and has served as adjunct lecturer at Columbia University in the Master in Applied Analytics program where he has taught multiple courses. Grant's expertise spans AI, machine learning, data in organisations, executive management, and driving innovation across the APJ region.
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