DevOps dramatically changed the game when it first appeared. Placing IT operations within the realm of software development has created an environment that significantly shortens all aspects of the software development life cycle (SDLC).
Machine learning (ML) then rose to prominence shortly after this change, and MLOps was the next logical step in its evolution. Basically the same thing applied DevOps principles Move to the Machine Learning Lifecycle (MLLC) and take your AI model deployment to new heights.
But as AI systems continue to grow rapidly and expand into regulation-critical healthcare, finance, and other industries, MLOps is slowly but surely reaching its limits.
I can’t wait to see what the future holds for MLOps, as security and auditability are now as important as system performance. And that’s exactly what this article is all about.
What is MLOps? Why do I need it?
In layman’s terms, MLOps is essentially an extension of DevOps practices to ML. The goal here is to automate and manage the entire MLLC, which primarily includes data collection, training, and model deployment via a CI/CD pipeline.
Let’s say you run a financial services company. MLOps allows you to build real-time fraud detection systems that continuously monitor performance and automatically retrain as new data becomes available.
[Source: Pixabay]
In fact, this is exactly how modern identity theft protection tools work. They use MLOps services to process and analyze large amounts of data in real time. This allows you to track millions of transactions simultaneously and detect anomalies in login attempts, so you can stop fraud before it happens.
Ultimately, such an approach will bridge the gap between the management of isolated AI models and their reliable use in the real world. And that’s exactly why businesses need MLOps in the first place.
Benefits of implementing an MLOps strategy for businesses
The business value of MLOps lies in how it transforms siled ML experiments into integrated systems that extend proven DevOps practices across MLLCs. As a result, you receive countless benefits, including:
- Accelerate model deployment: MLOps dramatically reduces the amount of manual effort required for training and validation by enabling CI/CD pipelines for ML models. This level of automation allows you to get your business up and running faster.
- More reliable ML models: MLOps also adds version control for datasets and code. This makes it much easier to track changes and revert in case of errors. It also ensures that everything runs exactly as expected, producing high-quality models that businesses can trust.
- Improved overall collaboration: One of the main benefits of DevOps is enhanced team communication, but MLOps takes it to another level. Essentially, it provides tools that support seamless collaboration between data scientists, ML engineers, operations personnel, developers, and even stakeholders.
Although MLOps has many benefits, not all decision makers are comfortable making such a drastic change. In that case, you can always hire a dedicated person. MLOps Consulting Service Like Stack Overdrive.
Potential limitations of the MLOps approach
MLOps already optimizes resource usage through automation. However, these ML models still require large amounts of computational power, and require significant GPU horsepower to successfully run something complex.
When you consider the cost of modern AI-focused GPUs, you can quickly see how expensive implementing MLOps can be. As you can imagine, this is not the case for all companies, especially smaller ones that cannot justify such an investment.
However, this is not a problem for companies. These large enterprises can easily allocate hundreds of thousands of dollars to deploy MLOps. In fact, like many industries, they are doing just that industry-wide, and the benefits easily outweigh the costs.
The future of MLOps: The rise of multi-model MLOps infrastructure
As companies across industries move away from a single AI system, a multi-model MLOps approach has clear advantages over previous approaches.
This approach relies on multi-model serving (MMS) to get the most out of your infrastructure. As the name suggests, multi-model MLOps allows enterprises to deploy multiple models into one container. Add a little bit of intelligent scheduling to it, and it’s easy to see why it’s poised to be the next big step in the game.
[Source: Unsplash]
That doesn’t mean MLOps will soon become obsolete. In fact, this is still the heart of multi-model MLOps. Therefore, rather than completely replacing older approaches, multi-model MLOps essentially removes one of the main drawbacks of previous approaches.
Multi-model MLOps offers great potential to save both cost and energy by allowing enterprises to deploy multiple models on a single shared server and keep the most frequently used models in memory. Therefore, we address one of the most common pain points: implementation cost. That’s really the next logical step in the evolution of MLOps.
conclusion
In the less than 10 years since the term was first coined, MLOps has achieved significant adoption and fundamentally changed the way machine learning models are deployed. Applying proven DevOps principles throughout MLLC brings automation to AI models, making them much easier to deploy.
However, the benefits of the MLOps approach do not end there. With the rise of multi-model serving, the next generation of MLOps practices, aptly named multi-model MLOps, is poised to significantly reduce implementation costs and greatly facilitate scaling of complex ML model deployments.
