How Machine Learning Model Registry Can Be Helpful for Your Business
In this tech-driven world, running a technology-based business without machine learning is virtually impossible. From service sectors to stock markets, everything is being run by artificial intelligence and machine learning. To implement automation into your next project, it’s important to know about the machine learning model registry and how it can be useful for your business.
What Is Machine Learning?
Machine learning is a branch of artificial intelligence that uses data and predefined algorithms to execute tasks that require increasing precision and repetition.
The term machine learning was first introduced by Arthur Samuel in his project surrounding the checkers game, which defeated Rober Nealey, a self-proclaimed checkers master.
Typically, machine learning needs to be trained before the model can be used to execute complicated tasks. To manage, deploy, and monitor the whole operation, ML model monitoring plays a huge part.
The decision process is where the machine learning model makes predictions about the data set and outputs a result depending on it.
Following the prediction, an error function evaluates the output and compares the accuracy of the algorithm with a known data set.
The model optimization process sets the data point better by adjusting the weights to minimize discrepancy with the training set.
This whole set of operations is repeated infinitely until the desired accuracy is achieved.
What Is Machine Learning Model Registry?
Machine learning model registry is a repository of trained ML models. All the metadata regarding the stored ML models like training data and predictions are also stored in the machine learning model registry.
It’s the responsibility of model registries to store and provide all the data required to recreate the model from scratch.
How Does Model Registry Work?
When you have a small manageable business, having ML models stored in files doesn’t seem much of an issue. But as time goes by, and you start scaling up, the new recruits start seeking information about changelogs, testing data, and versions of your model. This is where you realize that the filing system isn’t as efficient as it once was.
The model registry keeps all your machine learning models in place and derives any relevant data related to the respective models whenever required.
Now that we understand how the model registry works, let’s dive into how it can be helpful to your business.
1. A Repository for All Machine Learning Needs
If you’re a considerably large business, your machine learning model registry holds hundreds of ML models with every bit of information that you might need in the future, including the past testing data that you used to train the model.
Having a central repository with unique model numbers (UUID) for each program means that all of your employees can access the pool without needing to surf through hundreds of files before stumbling upon what’s required for their project.
It’s inefficient to recreate every ML model from scratch every time a new client requires it. You’d lose resources and money. The optimum way is to retain files of the previous models and fork what you require in order to customize it. That way, the production cycle goes shorter and less resource-intensive.
2. Versioning Control for Effortless Model Management
Version control is a standard operating procedure for repositories. ML model registry keeps track of the changelogs, versions, testing data, and any integral or customary changes made to your model. ML registry even keeps track of the UI or API changes.
But why is versioning control important?
Not every model would work for every situation. At least not every version of it.
With version control, you can retrain your models from an older version in comparison to recreating them from scratch without it. This saves a lot of time and resources.
There are three main aspects of version control. Code, data, and model.
The modeling code is used to implement the model. And implementation code is used for testing the model. Typically these two are coded in different programming languages, which version control helps to maintain.
Versioning control links testing data to appropriate metadata. Metadata is a fundamental information set that describes your model and data.
A model version control system helps collaborate teams and projects for smooth deployment and feedback loop – keeps track of changelogs that may be used in the future to fix a broken model – tests different branches of the same model, and many more.
3. Testing Automation
Testing is a critical part of any development process. The testing is done by breaking down the code into the smallest possible fragments, called units. Each unit is responsible for a single simple task that makes the whole complex code.
For the deployment of a product, you need to execute three kinds of testing. The UI testing, API testing, and unit testing. While each one of them can be tested manually, only the APIs are tested that way. You’re better off leaving UI and unit testing to the hands of ML and ML model registry to save on resources. Despite automation testings wielding false positives on several occasions, it’s faster and more efficient.
4. Faster Production Cycle
As the model registry keeps every bit of information about the testing, it’s easier to generate a feedback cycle faster than manual testing.
In manual testing, the testers generate a bug report, which is then sent to the developers, who, after verification, send the revised code for testing again, and the cycle continues for several days, even weeks.
With ML model registry and automation testing, the testing can be executed quickly to send feedback faster, saving time and resources.
5. Handling Big Data
In this data-driven world, everyone has several data points open on websites from search engines to social media. This huge set of data is termed big data.
A small company with 2 or 3 data points for each customer may be manageable manually, but having tens and hundreds of data points for each of the customers becomes unmanageable pretty fast.
ML model registry helps sort this problem out for you by analyzing the user pattern and by determining which sets have the most importance for each customer. For a business owner, office management software may be a selling point, but not a shiny new umbrella, even if they’ve shown interest.
The Bottom Line
Nowadays, it’s almost a necessity to implement a machine learning model registry for your business. The ML model registry can act as your central repository and a version controlling tool in addition to automating testing and handling big data.