Basics of MLOps
MLOps is the concept of solving developmental concerns in a machine learning application.
It is the combination of machine learning in a business environment. In other words, it is a process of applying machine learning to operations to critical areas in your business.
MLOps involves the collection of things needed to implement machine learning in your organization. The collection could be people, mindset, culture, technology, processes and systems.
In this post, let us understand what is MLOps and how it can be useful for your organization.
Table of Contents
- What is MLOps?
- What does it take to build an MLOps culture in your organization?
- The right infrastructure for implementing MLOps
- The right plan to automate processes
- The right analytics tools to measure the quality of your application
- Best practices to achieve an MLops culture in your organization
What is MLOps?
Before we explain what MLops is, we need to understand the basics of DevOps.
DevOps is the process of improving an organization's ability to deliver applications at high speed. DevOps focuses on not just the speed of delivery, it also focuses on improving the quality of the services/ products offered by the organization.
While traditional organizational models move at turtle speed towards development, DevOps promotes faster testing, optimization and execution. The speed of DevOps allows organizations to create products/ services for their customers. The speed of DevOps also allows innovation and forces the organization to deliver what the customers want, not the other way around.
MLOps is a concept built on the foundation of DevOps. It forces organizations to continually innovate their offerings to match the wants of the customers. Just like DevOps, MLOps also focuses on optimizing and innovating at breakneck speeds.
But the only difference here is that it includes Machine Learning to ramp up the process of innovation. MLOps require data scientists who curate datasets in your business and build models around them.
It involves the collaboration between Data Scientists, ML engineers and IT professionals inside the organization. They all work together to implement Machine Learning right from development to deployment.
What does it take to build an MLOps culture in your organization?
MLOps is not something that can be achieved by one person in your organization. It is a culture shift that all your employees must take into account. Here are some things you need to implement an MLOps culture in your organization.
- Infrastructure and resources for training employees on the MLOps model
- A dedicated analytics platform measuring the quality of your application over various stages in the process.
- A well laid out plan to version, validate and deploy the MLOps application
- Automated processes to look over the delivery and deployment of the application.
- Tools and digital equipment to help employees implement the MLOps culture.
- A flexible team that can design and develop applications in an MLOps environment.
The right infrastructure for implementing MLOps
The infrastructure required for a machine learning environment is very dynamic. There will be times when you’ll need a massive amount of memory to complete processes in batches. Other times you might need a processing unit with very low latency. The basic infrastructure in an MLOps environment changes very quickly.
The software required for different versions might also be different in an MLOps environment. Each version may be dependent on a different language. So you need to make sure that you have the right infrastructure and IT software to cultivate an MLops environment. However, you can rent computing power whenever it is required in your environment.
The right plan to automate processes
MLOps is focused on deploying new versions with a high frequency. This requires a set of rules and testing policies in place. These rules make sure that you don’t break the system when it is running at full efficiency.
After rules, you need automated processes to help with the deployment of the application. By automation, we don’t mean anything too complex. Automation could yet again mean a few desired steps or a checklist that needs to be followed at each stage. Let’s say that your application has an issue or an error. Here are some steps that can be followed systematically -
- Analyse the root cause of the problem
- Fix the problem by reworking on the root cause
- Deploy the code into the final versions
- Validate with a new version with a set of predetermined tests
- Check if the error still persists
- If error/ problem persists - Go to step 1
- If error/ problem is over - Launch it LIVE
This could be an example checklist to ensure that errors are rectified in the program.
The right analytics tools to measure the quality of your application
In an MLOps environment, it is crucial to measure the performance of each version consistently. There are a hundred things that can go wrong when the model has been deployed. So it’s necessary to implement an analytics tool to understand how the version is working.
Here are some questions you can answer with a dedicated analytics tool -
How does the performance of the version compare to the predicted performance in testing?
- If the performance is good, what tests can we run to improve it?
- If the performance is bad, what can we do to fix it?
Does the overall output of the model within the bounds of the predicted output?
Does the version work and react as it did in the training/ testing period?
- If it does, what are the next set of tests we can run on it?
- If it does not, what can we change/ update to make it work like the training period?
There are more such questions that you can always answer with an Analytics tool. The more you measure the performance of your application, the better it will get over time.
Best practices to achieve an MLops culture in your organization
- A successful ML product requires a team effort.
- The ML product must have a clear goal in mind
- It should have a set of predetermined KPIs to measure the success of the product
- Identify the resources required to validate datasets early on
- Start and proceed with an experimentation mindset
- Containerization is your best friend in the development process
Deploying MLOps is more than just hiring a bunch of data scientists onto your organization. It’s a step that your organization needs to acknowledge as a whole. That is what will truly set you up for success with MLOps.