Is your organization prepared for analytics at scale? Find out with MLOps

Across every industry, organizations are investing in artificial intelligence (AI) and machine learning (ML) to unlock business insights from their data. Unfortunately, many leaders struggle to scale their AI/ML models into production across the enterprise.

Worse still, organizations spend precious time and resources monitoring and retraining models. Teams often can't replicate successful ML experiments, and data scientists don't have access to the technical infrastructure they need to innovate.

But there is a solution: machine learning operations (MLOps). MLOps is a set of industry best practices that unify people, processes, and technology for analytics at scale.

Here, we outline three steps toward MLOps maturity in the enterprise.

Every organization is at a different stage when it comes to MLOps maturity. Therefore, it's essential to explore how your organization compares to industry averages and benchmarks to understand its potential for scalable ML solutions.

There are five phases in the ML project lifecycle:

1. Business requirement determination
2. Data preparation
3. Model development
4. Model monitoring
5. Model output and deployment

With a clear view of people, processes, and technology, assessing these phases can help you determine how far along you are in the journey.

When you understand all the areas of MLOps maturity, you can develop a questionnaire to evaluate your organization. All questions must carry a weighting that, when collated, will help you establish if your organization is of low, medium, or high maturity – in other words, a laggard, early adopter, or leader. Here's how:

• To assess people maturity, question how well you include the necessary data science and business leaders across all stages of the ML project lifecycle
• For process maturity, determine how you've organized your data fabric – is it scattered and inaccessible, centralized but with limited access, or centralized and accessible with robust governance? Also, consider how well you connect data insights to business goals. These factors paint a picture of your models' maturity
• And finally, to assess technology maturity, look at how your organization approaches data preparation and ML experimentation. You want to see if your organization has a central tool to give the right insight to the right person at the right time – or if you have a siloed approach wherein data scientists use local computers or servers that are difficult to obtain and set up

The answers to all these questions will lead you to your MLOps readiness score. With this data, you can create a visual representation to benchmark externally against competitors or internally across various business functions.

Now that you know where you are, it's time to think about where you want to go. You can begin to build a future-state operating model for your ML projects and change management strategy. But remember: MLOps is a team sport that requires different activities performed by many individuals but with multiple handshakes along the way.

At the same time, when you deploy an ML model to a live environment accessible to clients, you must adhere to high standards of ethics and integrity. Therefore, you must incorporate a culture of collaboration and responsible AI practices when building your roadmap for MLOps adoption across the enterprise.

There is no doubt that data scientists are the backbone of ML and deep learning projects. However, as business leaders, employees, and customers expect more from these undertakings, the role of ML engineers becomes critical to continuously validating the efficiency and effectiveness of ML models.

Thankfully, by following the three steps above, MLOps can help organizations build a sustainable framework for achieving analytics at scale – the type of framework that boosts productivity, ensures compliance, and fuels innovation.

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