How MLOps can Transform Enterprise App Development
Overview
Organizations are leveraging machine learning to enhance their decision intelligence capabilities. They are trying to accelerate and refine their predictive capabilities to strengthen their market positions and boost returns. But Machine Learning development teams often face challenges like data discrepancies, lack of data/model versioning, data accuracy, and budget constraints. The failure to develop an accurate and efficient repeatable model often leads to a derailed business initiative. Recent global research by Thomas Davenport & Katie Malone discovered that about 80% of projects across industries are junked before the deployment of an ML model. Enterprises are banking heavily on AI/ML to reinforce their decision intelligence capabilities. But this trend indicates that their investments might be in jeopardy.
However, MLOps has emerged as a lifesaver for such businesses. It helps developers to improve the quality of their machine learning models through a more reliable and automated way to develop, test, deploy, and manage the models. While the approach started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management.
What is MLOps?
MLOps are a set of practices that aim to streamline the ML process and define a seamless handoff between data scientists and the ML engineers to models in production reliably and efficiently. The approach is designed to improve the quality of production models, thereby enhancing their business viability while complying with regulatory requirements.
The MLOps workflow traverses the complete machine learning model lifecycle from build to deployment and continuous governance thereafter. It ensures a consistent evolution of the model by countering any emerging shifts and drifts. This allows businesses to generate actionable outputs with accuracy that they can utilize to make better decisions.
How MLOps can transform enterprise app development for the better
In this article, we are discussing how MLOps adoption can help businesses optimize enterprise app development. Just like DevSecOps in application development helps eliminate silos, MLOps streamlines the automated optimization of a machine learning pipeline. Here are a few effective ways in which the practice is helping enterprises enhance their machine learning app development outcomes.
1. Facilitates Improved Collaboration Among Various Stakeholders
With data science taking a more prominent role in business, there is an increasing need for collaboration between machine learning teams and operations teams. There is an ongoing shift towards a more data-driven approach to problem-solving, which means that production environments need to be agile enough to handle frequent data updates while maintaining stability.
The machine learning pipeline is broken up into multiple, specific stages. However, several of these steps have remained stagnant since the inception of machine learning pipelines. In this context, MLOps systems have been created to improve collaboration among disparate stakeholders and reduce the time it takes to move data through the pipeline.
This enhances reusability and collaboration within the enterprise, enabling secured access control, version control, and performance measurement of the machine learning models.
2. Valuable in Reducing Bias
By combining machine learning (ML) and operations (Ops), MLOps creates a more robust system that augments ML development, so it can adapt to its evolution and the drifts in data. A static model that is locked into a specific type of input and designed always to produce the same outputs will not be able to react if the nature of its underlying data changes, or if its calculations are skewered by intentional or unintentional bias — including but not limited to the ongoing issues around race and gender equity. This obstructs enterprises’ path to building an application that helps them in making data-driven decisions.
MLOps creates a harmonious digital experience for everyone including programmers, data teams, business analysts, customers, and other users. It helps development groups keep pace with ML’s relentless march forward in building and optimizing an algorithm that is suitable for the business. It boosts the credibility, reliability, and productivity of machine learning app development.
3. Enables Accurate Model Development
MLOps enables an enterprise with precise model development, from data collection and preparation to model design and implementation. MLOps supports and optimizes models so business decision-makers can focus on the overall results rather than being distracted by the mechanics of the process.
The ability to collect, store, and share data is a near-universal business asset. Machine learning (ML) is a promising way to extract real value from data. However, ML is not a one-size-fits-all solution for every business situation.
Starting with an experienced MLOps team that has deployed hundreds of models using best practices in data preparation, model design, and implementation, as well as operationalizing the ML models and integrating them with deep technical expertise to help you build reliable and trusted ML models that get optimal performance out of your data assets.
4. Enhanced Model Training and Retraining
MLOps support and optimize models to allow automatic, streamlined changes. They consistently measure and order the behaviors of the model while it’s running. The operation and iteration occur in a synchronized manner. It improves the speed and makes model updates less labor-intensive. MLOps improve prediction accuracy with enhanced model training & retraining with repeatable workflows. This will help enterprise apps in evolving in the right direction and consistently generate greater business value.
Looking Ahead
Leading experts have marked MLOps as the next significant shift in software engineering. With industries looking to boost intelligent technology applications across processes and departments, MLOps will soon be high on their priority list.
At Compunnel, our Center of Excellence identified MLOps as a gamechanger very early. Led by SMEs who have pioneered the development of high-impact AI/ML accelerators, we are helping enterprises solve critical business challenges through AI/ML and MLops Services. Our multi-industry and platform-agnostic solutions have been pivotal in improving our customers’ overall efficiency and productivity. Are you struggling to deploy an accurate ML model? Reach us now to learn about effective MLOps-driven solutions from our Machine Learning SMEs.
