For many people, machine learning was considered a fantastical idea from science fiction until recently. That makes sense; the field has seen a lot of advancements recently! But there is quite a lot more to ML than simply teaching a machine how to do something.
For all the profound impact it has on technology and the use cases it has, ML isn’t as easy as it looks. There is a lot of trial and error involved and a whole lot of time and computing power required to make a single model capable of deployment.
That’s where MLOps comes in.
MLOps & DevOps
Machine learning operations (MLOps), a fairly new discipline, is quickly gaining traction among experts in the field, and for a good reason! Using the same structure as DevOps (development operations), MLOps aims to streamline the process of creating an ML model and bringing it to completion so it can eventually be deployed and used in the industry.
Let’s consider DevOps first: this is the process of creating an application – suppose, a mobile app for a bank. DevOps isn’t a one-person job. There will be multiple people involved in the design, development, testing, fixing, operations, and deployment.
While it can be a one-person job, this approach is not very convenient or efficient.
With DevOps, development and operations teams can work together better through the entire software life cycle. This usually works through Docker containers and Kubernetes, and as containerization technology develops, the process becomes easier to go through.
MLOps is inspired by DevOps but exists somewhere at the crossroads of data engineering and machine learning. The concept is the same – using containers and sharing them between developers to aid the process of development and operations – but the execution is different.
Because ML systems require a lot more trial and error and experimentation, they’re trickier to build and operate on.
What Is MLOps?
MLOps stands for Machine Learning Operations and is the process known for developing, testing, and deploying ML systems and applications. ML has a unique need to establish new life cycles since there is a constant need to train the models on new data. DevOps usually don’t see this as an issue. It results in the requirement of a different workflow that uses existing production at scale and CI/CD procedures to work with these life cycles and bring better ML results.
Machine learning and deep learning models require constant improvement in quality, which requires data scientists and operations teams to collaborate on the applications. Without constant improvement, these models wouldn’t be suitable for deployment, nor would they meet business demands or regulatory requirements.
The fact that these applications have to be looked over and improved as time constantly passes means that communication is necessary, and MLOps allows the ML development and operations teams to do just that.
Why Do You Need MLOps?
So, why do you need MLOps specifically instead of simply using a DevOps approach?
Organizations frequently misunderstand why ML applications are so complex and treat them like regular software. This mistake results in lost opportunities as well as lost money and resources. Machine learning isn’t like other software because, once again, it requires its life cycle and strategy.
Just the code isn’t enough for a good ML program. To get insights out of any ML model, you don’t just need the right code. You need the right training data, validation data, testing methodology, optimizers, and so on.
In fact, for different types of data, you need entirely different ML processes, which complicates things much further. Also, consider that your application doesn’t end once you’ve deployed it – the training data needs constant updating and additions to stay relevant – and you can understand why it’s so difficult to deploy an ML model.
The heart of an ML model is the data, which is what differentiates it from regular software, which is instead centered around application management. On top of that, ML isn’t a closed loop – unlike other software, you don’t go through the process once and find yourself at the end of it.
Consider an application you’re making for a bank: you create the app, test it, monitor its performance, and once you’re sure it’s working properly, that’s all you have to do. Maybe you’ll be asked to fix bugs every once in a while. However, for the most part, your job is done once an application is deployed.
ML applications are not like that. Deployment is one of the earliest stages of an ML application due to it being centered around data rather than around the application. Once you deploy an ML application, you have to regularly monitor it for its performance, making sure it’s retrained regularly, errors due to new training data are fixed, and the newer version of the model is redeployed.
Rinse and repeat.
MLOps has three stages in its lifecycle: development, deployment, and operations. Each stage works in a loop, passing new information back to adjust how the model works and redeploying it to test for errors. Funnily enough, this is exactly how machine learning models themselves also work.
At every stage of this lifecycle, there is a need for governance and security to make sure that every stage of the process meets the organization’s standards.
Benefits of MLOps
Machine learning operations come with several benefits for organizations.
For one thing, the speed at which you can build, deploy and iterate on models becomes much faster. This isn’t just because the process becomes streamlined but also because there are usually multiple people working on the same application. MLOps provides a better and more efficient way for them to combine their contributions.
Regarding the model and infrastructure, MLOps allows for a much quicker and more cost-effective way to monitor and adjust infrastructure. In fact, without MLOps, this may not even be possible. With the ability to constantly make adjustments and improvements, infrastructure costs go down, and profit margins increase.
Business is all about being on top of the trends, and if you want to stay ahead of your competitors, you will need to constantly rework and tune your models with all the latest info and the newest data. With a one-person show, this becomes difficult because you have to do it all by hand, and there’s a lot to do! From data gathering to pre-processing to processing, training, testing, and monitoring – there’s a lot of work involved!
With MLOps, you get to automate your workflow so that your overall cost of using the ML model itself goes down, but you don’t fall behind your competitors either.
With machine learning, it’s all about using the latest and most cutting-edge techniques to minimize the cost of running the model, maximizing accuracy, and even making the process faster! But the field is advancing so rapidly that it’s difficult to stay on top of all the newest techniques and adjust your model for these new techniques each time there is a new ‘best’ one.
With MLOps, this problem is minimized because integrations become more flexible, and you can use several tools to adjust your model for newer techniques without incurring technical debt.
Organizations may have trouble figuring out how to begin implementing machine learning models into their systems. Remember: machine learning does not occur in isolation – it needs to be constantly up to date with your internal infrastructure, development tools, and applications to be of any use to your specific circumstances. It also needs constant data input for training and retraining.
Your MLOps platform should be integrated with the existing system.
Also, avoid trying to build one from scratch. ML models are very complicated to create, but the infrastructure on which they are deployed will be a lot more complex. Not only will this be extremely costly even if you know what to do, but there will also be a lot of trial and error before you even get to that point. That just pushes your costs up higher.
Instead, it’s easier to opt for commercial MLOps platforms that are more cost-effective and stable. Not only do you have certainty that they will work, but it’s much faster to implement them, and they only need a few minor adjustments to fit your specific model into the infrastructure.
If you’re looking for a system integration service company to help you implement ML into your enterprise infrastructure, Vates can help you. Contact us for more information.