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MLOps: Why Observability Matters

Observability is a crucial component of ML Ops and AI inference.

My previous post focused on a new phase of CI/CD known as CT "Continuous Training" and its significance to MLOps. Now, I'd like to turn the focus to observability. 

Observability is a crucial component of MLOps and AI inference. It gives the capability to track and comprehend the behavior and performance of machine learning models in production. Observability ensures that the models are producing accurate and reliable data, as well as to detect and resolve any anomalies that may occur.

Some of the advantages of observability for MLOps and AI inference are:

  • It enables faster and more efficient deployment of models, as well as continuous improvement and optimization.
  • It mitigates the risk of model drift, bias or degradation, which can impair the quality and trustworthiness of the model outputs.
  • It provides insights into the impact and value of the models, as well as the feedback from the users and customers.
  • It facilitates collaboration and communication among the stakeholders involved in the ML lifecycle, such as data scientists, engineers, business analysts and managers.

To achieve observability for MLOps and AI inference, some of the best practices are as follows:

  • Define clear and measurable objectives and metrics for the models, such as accuracy, latency, throughput, availability, etc.
  • Collect and store relevant data and logs from the models, such as inputs, outputs, predictions, errors, exceptions, etc.
  • Use tools and platforms that enable easy and comprehensive visualization and analysis of the data and logs, such as dashboards, charts, alerts, etc.
  • Implement feedback loops and mechanisms that allow for quick and effective actions and interventions, such as retraining, updating or retiring the models.

Observability is not a one-time activity, but a continuous process that requires constant attention and improvement. By applying observability principles and practices to MLOps and AI inference, you can ensure that your models are delivering optimal results and value for your organization.