What is Model Registry in Machine Learning? The Ultimate Guide in 2024
In modern software development, incorporating comprehensive systems for change management and version control have become a necessity rather than a luxury. Centralized version management and code persistence provide an easy way for developers to iterate, review changes, deploy at scale and rollback in the case of failure. In machine learning, rapid experimentation and evolution have often superseded traditional software practices, leading to fragmented systems of code management. In this blog post, we'll explore the necessity of model registries in your machine learning environment and how these tools can transform and elevate the cycle of machine learning development.
What is a Model Registry
A model registry is a centralized repository designed to tackle the specific challenges posed by ML model development. Unlike traditional software, ML models have multiple components that extend beyond just the code of the model - training data, hyperparameters, model weights, and the environment required for running the model. Model registries accelerate the journey from research to production by providing a consolidated platform for secure model storage as well as the metrics required for evaluating model performance, allowing you to easily tune parameters and select the best model variation. Model registries also offer a seamless transition from training to deployment, enabling faster development and greater flexibility in inference experimentation.
Why You Need a Model Registry
You’re probably thinking “Why not just use git? I store all my organization’s code in git, why would a machine learning model be any different”. While git based repositories should certainly be used in conjunction with model registries for semantic versioning, PR review, and team based collaboration, you have to consider that there’s more than just code management when it comes to machine learning development. When you create a version of a machine learning model or execute a training, there are several key aspects of that training that you need to keep track of:
- Code - the actual execution code of the model
- Environment - the python version, pip libraries, or driver configurations used throughout the build
- Training Data - the specific input data that was used during the training (dates, columns, filters)
- Hyperparameters - the external configuration settings used to control the learning process
- Metrics - the evaluation criteria used to measure the performance of the training
- Image - container that will be used to replicate the training environment for inference deployment
While git based archives or image repositories can manage some of the requirements listed above, transforming those tools to support all of these features in a usable, maintainable way would require significant development effort and go beyond the core competencies of these products. Whereas with a model registry, you can utilize a tool that is designed specifically for the problem you are trying to solve with no additional development work.
Features of Model Registry
So far, we’ve outlined some of the core distinctions between model registries and traditional code repositories. But what are some of the additional features and benefits you receive by using a model registry?
Benefits of Using a Model Registry
Centralized Model Storage in a Dedicated, Remote Environment
In machine learning development, it’s not uncommon for model training to occur on local machines, notebooks or in an ad hoc manner. While this may work for some teams in the short term, as the scale of models in production increases, this will become a nightmare to maintain and present significant challenges when it comes to time to make changes or updates to models. Model registries address these challenges and provide specific solutions that are catered to ML development.
First, and most importantly, it’s difficult to maintain and keep track of. When there’s a problem with your inference service in production, you don’t want to be hunting down a rogue developer to find their model artifact from three months ago to revert to. Having a clear, accessible historical record of your model variations and builds is necessary for creating a stable consistent production environment and will allow you to revert changes instantly.
Data Consistency and Security
Second, a model registry allows you to have consistency and control over the data used for training. In ad hoc training scenarios, datasets can float from machine to machine, which can present various concerns around security, data governance, and integrity. With a model registry, you know the exact data that was used for a training, and can ensure it always comes from a safe, secure location without any unintentional persistence.
Lastly, as the scale of data increases, training on local machines or static compute instances can present challenges around resource utilization. Having a dedicated environment that can dynamically scale up and down cloud instances of any size will allow for larger, more efficient training.
Model Performance Tracking
With any machine learning model, you will want to carefully define metrics and evaluation parameters that will gauge the performance of your models predictions. These metrics vary significantly depending on the type of model you’re building, and you’ll want to ensure that you select the metric framework that matches your development. For each training execution, you’ll need a way to store these metrics so you have a way of comparing the changes from run to run to ensure that the changes you make to a model actually improve performance. Without a model registry, your options for storing these metrics are left to raw text files, building a custom API endpoint, or blindly storing them in a database.
Model registries allow you to attach the evaluation metrics of your model directly to the training build. That way, you can easily compare across different variations, visualize metrics across experiments, and identify when models need to be retrained or updated.
CI/CD and Scheduled Trainings
Once a machine learning model is built and in production, many teams will want to retrain their model based on new incoming data on some sort of a cadence or schedule. Without a model registry, this process becomes far trickier as you will need to stitch together several different tools and services such as code repositories, scheduling tools, image repository, semantic versioning, etc. With a model registry, your entire build environment is in one place, and you can easily integrate it with your scheduling tool to pull the model artifact and execute your training process.
Automated Model Evaluation
In addition, you can also utilize some of the other attributes stored in the model registry to enhance your scheduling capabilities. For instance, Qwak Build Metrics allow you to take advantage of the evaluation metrics stored alongside your model artifact to decide if you want to deploy your inference or not. Let’s say you want to train your model on a daily basis, but only push inference into production if your model’s F1 score is above .7, with a model registry in place, you can accomplish this with very little development or integration work.
Qwak Model Registry
In Qwak, the model registry is one of the core components of our end-to-end machine learning platform. Every model training or inference deployment initiates through the model registry. You don’t need to do anything to set it up, and it’s entirely managed for you!
When you create a model using the Qwak Model Class and execute a training using the Qwak CLI, the model is automatically packaged, stored, and cataloged in Qwak Model Registry. For each machine learning model, you can see the full log of historical builds, runtimes, hyperparameters, evaluation metrics, and success statuses.
Within a specific model build, you can see the step by step process that takes place during a machine learning - provisioning a node from the cloud provider, building the model environment, executing the training, running unit or integration tests, and pushing the artifact into the repository.
Metrics and hyperparameters are simple to include with your build. Using Qwak’s Log Metric and Log Parameter feature, you can easily define the specific evaluation parameters that relate to your machine learning model directly in your python application with one line of code. The metrics and hyperparameters are stored directly alongside your artifact in the build repository, so you can clearly see them when comparing builds or inspecting a build individually.
You can also compare up to four builds directly against one another to see what changed in the code, performance, or evaluation parameters to each other. When something breaks in production or a service isn’t performing as expected, you can very easily go to the model registry, see what change was made, and fix the code or revert to a previous build.
Keep track of the specific datasets that were used for each training. You can compare training datasets against one another and gain valuable insights into the distribution and quality of the features that make up your model.
When you want to deploy a new variation of a model, or revert back to a previous build in the case of a production issue, it’s one click, or one CLI command, and your previous artifact will be pulled from the model registry, and replace the existing inference deployment.
Qwak Model Registry Vs. SageMaker
The Qwak Model Registry is a feature-rich machine learning solution that requires very little setup, works out of the box, is intuitive to navigate and use, and fits seamlessly into the rest of your machine learning pipeline. While other services may offer similar model registry capabilities, they require far more in terms of set up, service hours, and development time.
For instance, if you were to utilize the SageMaker model registry, the process is far more involved. You would first need to create a model group and register a specific model within that group. Next, you would need to build and store a Docker image that can support your machine learning model. You’ll need to store your model code in an S3 bucket, as well as set up a process between your git repository and the S3 bucket to ensure that the model updates after a code change. From there, you’ll need to create an IAM role and policy so the registry can read from the ECR repository to retrieve docker images. You’ll also need to do this for the S3 bucket. When it’s time for deployment, you’ll need another bucket and set of IAM permissions to store your inference image and code base. Now, let’s repeat this process for every model we wish to deploy.
You can quickly see how these steps within SageMaker would become cumbersome and decrease the rate of development - and we haven’t even introduced versioning, change management, experiment tracking, metric storage, or CI/CD. With Qwak, you get a model registry that was built for machine learning engineers to be seamlessly integrated into machine learning environments.
As machine learning continues to play a more pivotal role in software development, the adoption of model registries becomes imperative for organizations seeking to streamline their ML lifecycle. By addressing the unique challenges posed by ML models, Model Registries pave the way for enhanced collaboration, version control, and reproducibility, ultimately empowering teams to build and deploy robust and scalable machine learning solutions. Qwak provides not only a comprehensive model registry, but an end-to-end platform that can support and enhance all of your machine learning use cases. Get started today.