Discover the key differences between Model Ops and ML Ops, implementation strategies, and which approach is right for your business.

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Model Ops, short for Model Operations, refers to the practices and processes involved in managing and deploying machine learning models in production. It is a relatively new field that focuses on the operational aspects of machine learning, aiming to bridge the gap between data science and IT operations

Read More: https://www.giggso.com/model-ops-an-introduction/

Observability in ML is crucial because it helps data scientists, engineers, and operators gain visibility into the internal workings of complex ML systems and detect issues or anomalies that may affect their performance.

Read More: https://www.giggso.com/ml-observability-in-giggso/