Differences

This shows you the differences between two versions of the page.

--- products:ict:ai:model_registry [2022/04/15 16:28] – created wikiadmin
+++ products:ict:ai:model_registry [2022/04/26 14:19] (current) – external edit 127.0.0.1
@@ Line 1: / Line 1: @@
+[[https://neptune.ai/blog/ml-model-registry|ML Model Registry: What It Is, Why It Matters, How to Implement It]]
+Why do you have to know more about model registry? If you were once the only data scientist on your team you can probably relate to this: you start working on a machine learning project and perform a series of experiments that produce various models (and artifacts) that you “track” through non-standard naming conventions. Since the naming conventions you used for your model files were unclear, it took you a while to find the most optimal model you trained. When finally you did, you decided to either hand the raw artifacts over to the operations team or worse, deploy it yourself.
+[[https://www.phdata.io/blog/what-is-a-model-registry/|What is a Model Registry?]]
+A model registry is a repository used to store and version trained machine learning (ML) models.  Model registries greatly simplify the task of tracking models as they move through the ML lifecycle, from training to production deployments and ultimately retirement.
+In addition to the models themselves, a model registry stores information (metadata) about the data and training jobs used to create the model.  Tracking these requisite inputs is essential to establish lineage for ML models.  In this way, a model registry serves a function analogous to version control systems (e.g. Git, SVN) and artifact repositories (e.g. Artifactory, PyPI) for traditional software.
+Another way to think about model lineage is to consider all of the details that would be necessary to recreate a trained model from scratch.  Establishing lineage through a model registry is a vital component of a robust MLOps architecture.
+[[https://cloud.google.com/vertex-ai/docs/model-registry/introduction|Introduction to Vertex AI Model Registry]]
+The Vertex AI Model Registry is a searchable repository where you can manage the lifecycle of your ML models. In a single view, you can access the data of your models in the Vertex AI Model Registry regardless of model type—custom models, AutoML models, or imported models that were trained outside of Vertex AI. From the Vertex AI Model Registry, you have an overview of your models so you can better organize, track, and train new versions. When you have a model version you would like to deploy, you can assign it to an endpoint directly from the Vertex AI Model Registry.
+[[https://www.verta.ai/platform/model-registry| Manage Your AI‑ML Models in One Place ]]
+ Verta Model Registry is a central repository to manage and deploy production-ready models.
+[[https://databricks.com/product/mlflow-model-registry|MLflow Model Registry]]
+MLflow Model Registry is a collaborative hub where teams can share ML models, work together from experimentation to online testing and production, integrate with approval and governance workflows, and monitor ML deployments and their performance.
+[[https://databricks.com/blog/2019/10/17/introducing-the-mlflow-model-registry.html|Introducing the MLflow Model Registry]]
+[[https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html|Register and Deploy Models with Model Registry]]
+[[https://analyticsindiamag.com/step-by-step-guide-to-building-ml-model-registry/|Step By Step Guide To Building ML Model Registry]]
+[[https://docs.determined.ai/0.12.13/tutorials/model-registry.html|Organizing Models in the Model Registry]]