Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor., Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor., Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor., Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor.
