Machine learning operations is the key to improve organizational performance

Machine learning operations (MLOps) has become a part of the company’s operations as it helps to recognize patterns, reveal anomalies, make predictions and decisions, and generate insights.

MLOps is an approach that marries machine learning (ML) model development and operations, aiming to fasten the entire model life cycle process. It helps to increase business value by driving the experimentation process, development, and improves quality of model production.

What is it?

MLOps aims to place and maintain machine learning models in production efficiently. It can also be termed as the continuous development practice of DevOps in the software field.

“MLOps is an intersection of DevOps, data engineering, and machine learning. It’s a process of deploying, managing and monitoring ML models in production. It enables the org to deliver higher quality results in a lower time, by facilitating better communication within the team and make them agile in their approach to AI/ML,” Arjun Rao, Partner, Speciale Invest, explained in an exclusive interview.

Now, it is easier to monitor and maintain production models and manage regulatory requirements. The MLOps market is expected to expand to nearly US$4 billion by 2025, as per Deloitte’s Tech Trends 2021 report.

What can knock you down?

International Data Corporation (IDC) reports, 28% of AI/ML projects fail, with lack of necessary expertise, production-ready data, and integrated development environments cited as the primary reasons for failure. Many more projects (47%) fail to even make it out of the experimental phase and into production.

“Talent! Undeniably, the biggest blocker to using AI/ML is trained folks that can convert business problem statements into data driven solutions. This is harder than we think,” Dr. Sarabjot Singh Anand, Director, Computer Science & Engineering, BML Munjal University, said.

Experience teaches us why unexpected results are obtained initially and what changes must be done to enable improvements to the Machine Learning pipelines, he added.

Most enterprise’s data infrastructure does not support rapid, consistent, machine learning models.

Tackle business issues

MLOps can encourage rapid delivery which helps these enterprises to industrialize machine learning. For example, an approach supported by better data which is used by machines can reduce processing time from hours to even days.

“AI and ML practices are no longer a luxury for companies, and they are becoming an integral ingredient in any data-driven company building modern applications. Implementing AI and ML techniques can bring down errors, new lines of revenue and attract new customers. It takes away the repetitive tasks performed by employees and optimize costs by data-driven automation,” Arjun Rao said.

It is easier route to follow which gives you the most accurate outcomes. This form of automation can be used to create, manage, curate data and algorithms, and models at the heart of machine-driven decision-making.

“MLOps can encourage experimentation and rapid delivery, helping enterprises industrialize machine learning,” the report said.

Once these models are placed within a company and while it churns out data, monitoring its performance can help increase business value. If it goes under the radar, there will be a lot of unpredicted bugs which will harm the model’s accuracy.

Model drift, a common issue which occurs when the accuracy of predictions produced from new input values “drifts” from the performance during the training period. This drift can be solved with MLOps as it helps by standardizing alignment of artificial intelligence (AI) models with growing business and customer data.

Tackle emerging issues

Despite the many similarities between DevOps and MLOps, machine learning spawns complex, data-related issues not commonly faced in the software development process, such as accountability and transparency, regulation and compliance, and AI ethics, as per Deloitte’s Tech Trends 2021 report.

“Just look at what has happened during the pandemic. Given the large volumes of data that is available for organizations to understand their customers/stakeholders and internal operations such as manufacturing or order fulfilment, the opportunity is ripe for organizations to use this new asset,” Dr. Sarabjot Singh said.

For example, ML models are used to predict medical diagnosis, banking applications, prison sentencing, and other sensitive outcomes. This requires model and algorithm transparency to understand how these decisions are made.

Meanwhile, privacy and consent issues related to both training and production data sets arises ML systems often use sensitive personal information, and data protection may further need to meet regulatory compliance standards, such as HIPAA, PCI, or GDPR.

The world is heavily dependent on MLOps today and it will create, curate, sustain, and help you run a profitable business.