MLOps articles

What is LLMOps? From prototype to production

LLMOps applies repeatable development, evaluation, deployment, and observability practices to production LLM applications and AI agents.

What is distributed learning?

Distributed learning splits model training across processors or machines. Learn the main strategies, tradeoffs, and how to run it on Kubernetes.

What is AI observability?

AI observability connects traces, metrics, evaluations, feedback, and runtime context so teams can understand and improve models, applications, and agents.

Observability for machine learning

ML observability connects logs, metrics, artifacts, infrastructure signals, and model behavior so teams can debug training and serving systems.

Data-centric AI and MLOps solve different problems

Data-centric AI can improve model quality, but it does not replace the operational discipline needed to run machine learning systems.

A fuller picture of model behavior during training

Metrics alone do not explain model behavior. Teams need artifacts, samples, images, logs, and lineage tied to each training run.

Experiment tracking in machine learning

Building machine learning models is an experimental process that requires several iterations. In this blog post we go over how Polyaxon manages experiment tracking.

What is a metadata store for machine learning

A metadata store is a central repository for storing all data generated in the process of building machine learning models. This data includes dataset versions, model versions, model parameters, model evaluation metrics, CPU and GPU utilization, just to mention a few.

How Polyaxon streamlines MLOps

At Polyaxon, we're always looking for ways to push the boundaries of what's possible with machine learning. Our MLOps platform makes it easy to manage the entire lifecycle of your machine learning models.

MLOps vs Dataops

How MLOps is different from DataOps.

MLOps vs DevOps

How MLOps is different from DataOps.

Effective data science

Effective data science and MLOps involve a number of different practices and approaches that aim to improve the efficiency and effectiveness of data science and machine learning (ML) workflows. These are the core principles and practices that can contribute to effective data science and MLOps.

What is GitOps?

GitOps is an operating model for cloud-native applications built on the principle that the source of truth for an entire application should be fully managed in a source control system.

Model development

GitOps is an operating model for cloud-native applications built on the principle that the source of truth for an entire application should be fully managed in a source control system.

What is MlOps?

Machine Learning Operations is a set of processes to automate and accelerate the machine learning lifecycle to go from exploration, to experimentation to deploying machine learning model to the production environment.

MLOps core principles

These are the core principles and practices organizations can use to improve the efficiency and effectiveness of their data science and ML workflows.

Streamlining the machine Learning lifecycle

The v1 release delivers some new functionalities and features to streamline the machine learning lifecycle.