MLOps is a term that you get to hear more frequently these days. And that is for a special reason. As organizations are incorporating more AI into their businesses, operationalizing AI becomes crucial to get the most out of it. Therefore, this article will attempt to clarify if industrializing AI with MLOps is the right way to go.
Everything is increasingly being modeled now using AI/ML – from processes to consumers. ML models help organizations discover patterns, reveal anomalies, make predictions and decisions, and generate insights that would have otherwise been very difficult or nearly impossible.
[ Read also: Azure ML – 4 Key Benefits For Organizations ]
As the number of models increases within an organization, end-to-end management of them builds up a huge burden which eats up a big chunk of business budget and resources. And it is for a reason that about 28% of AI/machine learning projects fail.
What Is MLOps & Why Is It So Popular?
Also known as ModelOps, ML DevOps, and ML CI/CD, MLOps is the application of DevOps approaches to machine learning to enable the efficient movement of ML models from development to production and management.
In other words, it is directly contributing to industrializing and scaling machine learning in an organization by helping to consistently develop, validate, deploy, monitor, and retrain models at scale. The result is Industrialized AI.
This is what led to the success of MLOps. It has presented a common solution (DevOps) to very commonly experienced large organizational problems.
As DevOps helped to transform the way technology teams used to develop, test, and release software quickly reliably, in the same way, MLOps aims to accelerate the entire model lifecycle process.
The core value it brings to the table is controlled speed.
Industrializing AI with MLOps – Is It Enough?
By now it should be clear to you that MLOps has a strong promise. But is it really enough for AI industrialization?
Contrasting DevOps with MLOps may seem obvious, as you may ponder that a model code is a piece of software as well at the end of the day. But, there are many different aspects that are typically not applicable to any software but ML models such as:
- Complex data-related issues
- Need for accountability for a given model
- Regulatory and compliance nature of models
- Ethics incorporated in a model
- Transparency of a model
- Key inputs in model development (assumptions, business intent, etc)
IDC reports that about 28% of AI/ML projects fail primarily for three reasons:
- Lack of necessary expertise
- Production-ready data
- Integration development environments
Production-ready data not being centrally available is one of the big gaps in many organizations. Especially the ones, who have not yet gone through digital transformation. Deloitte states that enterprise data infrastructure is not designed to support rapid, consistent, streamlined development of machine learning models.
As you can see, industrializing AI is not as simple as it seems. Applying DevOps practice to bring consistency to ML development processes is addressing one angle. While testing, validation, deployment, monitoring of models can be easily automated, the real value comes from the developed model.
You cannot just automate model development. You can try to streamline it by making the data available, ensure easy spin-up and down of dev environments, etc.
Organizations looking to integrate AI and machine learning into every process and system must ultimately aim for deployment consistency at scale. Deloitte expects the MLOps market will expand to nearly US$4 billion by 2025.7. Looking ahead, MLOps seems to have a good share of helping organizations in scaling, but the road is full of challenges as well.