What is MLOps, and why do we need it?

You can encounter a wide variety of MLOps definitions on the web. At ITRex, we define MLOps as follows.

Generally, the primary objective of MLOps is to streamline the process of deploying, maintaining, and tracking machine learning models in production environments by bridging the gap between data scientists, ML developers, and operations teams. As the statement above suggests, MLOps is a collaborative approach that amalgamates machine learning, data science, and software engineering into one cohesive practice.

More fundamentally, MLOps applies to the entire machine learning lifecycle—data collection, exploratory data analysis, data preparation, feature engineering, model training and development, model deployment, model monitoring, and model retraining. It offers a structured framework to support the seamless transition of machine learning models from the experimental to the live environment.

What is MLOps in terms of its key elements? While there may be more, the following are the most crucial components of MLOps that work together to streamline the end-to-end process of deploying and maintaining machine learning models, ensuring reliability, scalability, and efficiency:

The broad answer to the question “What is MLOps and why do we need it?” can be outlined as follows. Taking machine learning models to production is no mean feat. Machine learning lifecycle consists of many complex phases and requires cross-functional team collaboration. Maintaining synchronization and coordination between all of these processes is a time and resource-consuming task. Thus, we need some standardized practices that could guide and streamline all processes across the ML lifecycle, remove friction from ML lifecycle management, and accelerate release velocity to translate an ML initiative into ROI.

To explain this further, let’s explore the main reasons why organizations need MLOps.

There are a number of reasons for ML models to underperform in production environments. Failed productionized ML models mostly arise from data dismatch, model complexity, overfitting, concept drift, and operational issues. Operational issues relate to the technical difficulties of implementing and running a model in a dynamic environment, including compatibility, latency, scalability, reliability, security, and compliance. When a model has to interact with other systems, components, and users as well as manage changeable workloads, requests, and failures, it might not function as well in a real-world production environment as it would in a regulated and isolated one.

Addressing these challenges often requires a combination of careful model selection, reliable training procedures, continuous monitoring, and close collaboration between data scientists, ML engineers, and domain experts. MLOps is the newest field meant to prevent and tackle these problems with strict, automated monitoring throughout the entire pipeline, from collecting, processing, and cleaning the data to model training, generating predictions, assessing model performance, transferring the model output to other systems, and logging model and data versions.

The traditional way of deploying ML models into production is a disjointed process. After a model has been created by data scientists, it is passed on to the operations team for deployment. This transfer frequently leads to bottlenecks and challenges because of complex algorithms or disparities in the settings, tools, and goals.

MLOps promotes collaboration that weaves together the expertise of siloed teams and thus helps to lessen the frequency and severity of these kinds of problems. This improves the efficiency of machine learning model development, testing, monitoring, and deployment.

The desire to extract business insights from massive amounts of data is constantly increasing. This has led to the requirement for machine learning systems to be adaptable to changing data types, scale with rising data volumes, and reliably produce accurate results even in the face of uncertainties associated with live data.

Many organizations have a hard time utilizing machine learning in its more advanced versions or applying it more broadly. According to the McKinsey survey, only about 15% of respondents have successfully operationalized ML at scale. Another survey by Gartner found that only 53% of AI initiatives successfully transition from prototype to production. This mostly relates to the inability of ML solutions to be applied in a commercial environment with rapidly scaling data.

This mainly arises from different teams working on an ML project in isolation—siloed initiatives are hard to scale beyond a proof of concept, and crucial operational elements are often disregarded. MLOps serves as a standardized set of tools, culture, and best practices that involve a number of defined and repeatable actions to address all ML lifecycle components and ensure a reliable, quick, and continuous production of ML models at scale.

The MLOps approach helps shorten the ML development lifecycle and boost model stability by automating repetitive processes in the workflows of data science and engineering teams. In addition, by eliminating the need to repeatedly complete the same steps in the ML development lifecycle, automation allows different teams to become more strategic and agile in ML model management and focus on more important business problems.

A standard machine learning pipeline consists of multiple phases, including data collection, pre-processing, training models, assessment, and deployment. Conventional manual approaches frequently result in inefficiencies at each stage—they are time-consuming and labor-intensive. Fragmented processes and communication gaps impede smooth ML model deployment. Problems with version control can cause confusion and wasted effort. These inefficiencies lead to faulty models, sluggish development cycles, excessive costs, and eventually lost commercial prospects.

Lower operating expenses and quicker time-to-market are two main benefits of automating model creation and deployment with MLOps. The goal of the newly-emerging area of MLOps is to give the ML lifecycle speed and agility. With MLOps, ML development cycles become shorter, and deployment velocity rises. Effective resource management, in turn, leads to significant cost reductions and faster time-to-value.

Implementing MLOps in an organization involves several steps to enable a seamless transition to a more automated and efficient machine learning workflow. Here is a high-level plan from the ITRex experts:

Partnering with an MLOps company can offer numerous benefits and advantages for organizations seeking to successfully implement MLOps practices. Let us outline the most common ones:

Here, at ITRex, we help organizations harness the full potential of ML models effortlessly. ITRex’s MLOps team matches technological skills with business knowledge to produce an iterative, more structured ML workflow. Our extensive expertise in all AI domains, from classic ML to deep learning and generative AI, a strong data team, and an internal R&D department allow us to build, deploy, and scale AI solutions that generate value and translate into ROI.

For instance, our MLOps experts helped a social media giant with dozens of millions of users improve live stream content moderation by developing an ML tool and applying MLOps best practices. The client wanted to develop AI algorithms that would automate live stream content policing and implement MLOps approach to accelerate the deployment of the model. Our ML/AI engineers built a computer vision model for sampling and analyzing live streams, and MLOps engineers transferred the model to a graphical processing unit (GPU) to improve the ML model’s throughput performance. Go to the case study page to learn about the results of the project.