What is MLOps ?
MLOps stands for machine learning operations, and in this post, we’ll look at what they are and why they’re needed. The term MLOps is derived on the DevOps philosophy. Development and operations combine to form DevOps.
DevOps has enabled developers to ship software to production in minutes and maintain it consistently. Many businesses have already adopted this strategy due to its success. So why can’t we just do the same thing with machine learning? This is when MLOps plays a great role. It brings together two concepts: machine learning and operations. Despite the similarities between the two concepts, MLOps is a different path, so let’s have a look at it.
Let’s look at a textbook definition of MLOps before discussing the differences between DevOps and MLOps.
Machine learning operations is a practice that aims to make developing and maintaining production machine learning seamless and efficient.
Let’s look at how MLOps differs from DevOps now. Machine learning and traditional software are fundamentally different, despite the fact that they were both built on the same principle. So, there’s a key distinction between MLOps and DevOps that prevents DevOps from being instantly applicable to data science teams. The deployment of software code vs the deployment of machine learning models is fundamentally different.
With software, it can generally be developed locally with almost instant feedback and how a code change affects the end result. To see the impact of a code change in machine learning, a model must be retrained. Consider the difference between a code change saved by an application developer and a data scientist saving the same code change and then spinning up a cluster of GPUs, deploying code, data is transferred, and training a model before seeing any findings.
Because data is continuously changing, machine learning models must constantly learn and adapt. MLOps is a new and distinct field because of the complexity of this environment, which includes the fact that machine learning models are made up of both code and data, software code is relatively static.
Why MLOps?
MLOps are increasingly becoming a vital component of a successful enterprise data science project implementation. It’s a method that helps companies and business executives achieve long-term value and decrease the risk associated with data science, machine learning, and AI activities.
The focus of data should always be on the company. Operationalization closes the gap between gaining knowledge and converting it into actionable business value. It’s a simple idea, but it’s not easy to put into practice.
Using an MLOps method could benefit your company in a variety of ways, such as when the operations team has business knowledge and the data science team knows data, but what happens in the middle, or why the gap of mismatched expertise?
MLOps combines both sides’ knowledge for more effective machine learning that takes advantage of both sets of abilities. The regulatory side of operations is becoming increasingly important as machine learning becomes more ubiquitous.
MLOps keeps the operations team up to date on new rules and best practises. While the data team focuses on installing creative models, they can take control of regulatory processes. With a Transfer of knowledge and greater collaboration from operations and data teams, the bottleneck caused by sophisticated, nonintuitive algorithms can be alleviated. The loop is tightened by MLOps.
MLOps techniques are important in limiting the risk posed by the usage of machine learning models since they allow for safe and reliable operations; nevertheless, MLOps practises come at a cost, thus a proper cost-benefit analysis should be undertaken for each use case. Machine learning procedures are important not only because they help to reduce the danger of machine learning models being used in production. MLOps discipline is also required for massively deploying machine learning efforts, such as shifting from one or a few models in production to tens, hundreds, or thousands that have a beneficial business impact.
Finally, Good MLOps techniques will aid teams in keeping track of versioning, particularly during the planning phase of experiments. It aids in comprehending why the retrained models are superior to prior versions, as well as ensuring that model performance does not deteriorate in production at predetermined intervals.
In our next blog post we will look into MLOps Principles or the Major Pillars of Machine Learning Operations. Stay tuned..
Happy AnalytiX.