Machine learning Operations (commonly known as MLOps) is nothing but a culmination of people, processes, practices and underpinning technologies aimed at automating the deployment, monitoring, and management of Machine learning models. Simply put, the aim is to increase the number of machine learning and data science projects that ultimately result in successful production. With MLOps in place, different teams involved in data, development, and production can work collaboratively and leverage automation to deploy, monitor, and govern machine learning services and initiatives within an organization.
Now, addressing the most important question – Do companies really need MLOps? Can they not work without it? Well, getting things straight – with the advancement in technology and the rising demands of the consumers, the majority of the organizations have already implemented machine learning and Artificial intelligence. As a result of this, the companies have seen a significant surge in the figures of sales and profits. Ultimately, what everything boils down to is that majority of the projects that aren’t successful on board is because of a lack in deployment, monitoring and management of machine learning models. Thus, MLOps serves to be no less than a blessing here as there is a significant increase in successful production.
Talking about MLOps, there’s this area called “mature MLOps” that are gaining immense popularity all across the globe. However, the definition of MLOps maturity changes over time. Here is everything you need to know about Mature MLOps.
To start with, MLOps can be broken down into four key areas – deployment, monitoring, lifecycle management, and governance. These areas throw light on what mature MLOps is all about.
Many tend to believe that MLOps is all about models. However, these models have no meaning without data to train and run them. Simply put, MLOps is a combination of DataOps and ModelOps together with DevOps practices. On that note, mature MLOps deployment must include both data pipelines and models. This is because deploying without data pre-processing would result in a lot of work. Thus, the need to deploy the data pipelines and models together on production data comes into play. Also, this deployment phase to the production phase should be easy and seamless. Lastly, mature MLOps takes advantage of DevOps best practices.
When data pipelines and models are deployed into production, one doesn’t really expect the facts to change. However, there is a possibility that changes occur. Thus, the need for systems to monitor pipelines, models, infrastructure, and services is always there. Talking about mature MLOps, monitoring includes pipeline monitoring, data monitoring, infrastructure monitoring, and service monitoring. Continuous monitoring helps in finding out the issues and fixing them on time.
This is one of the most critical aspects under mature MLOps as this is the area where maturity really shows. After having information about issues (as a result of monitoring), the question that arises is – what is to be done with that information and how to update the projects? Mature MLOps teams have tools for troubleshooting like access and event logs and are also ready with failover and fallback options like a failover model or rules. Additionally, when the new version is rolled into production, the process is seamless beyond imagination.
No wonder having the operational best practices, visibility, and control in place to ensure that your organization is doing what needs to be done is of utmost importance. Thus, having AI governance makes every possible sense and mature organizations and MLOps teams know it.
An advantage of mature MLOps is that business stakeholders, data scientists, and other experts are all in the loop thereby paving the way for maximizing the success of their production projects.
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