We help you streamline the entire ML lifecycle, from data preparation to model deployment and monitoring for optimized performance and rapid innovation.
What Is MLOps?
MLOps, short for Machine Learning Operations, is a set of practices and tools designed to streamline the development, deployment, and maintenance of machine learning models in production environments. It bridges the gap between data science and IT operations, combining principles from DevSecOps—such as automation, continuous integration, and collaboration—with the unique challenges of machine learning workflows. The goal of MLOps is to create a repeatable, scalable, and efficient process that allows organizations to train and deploy ML models quickly while ensuring they remain reliable, accurate, and adaptable to changing conditions.
At its core, MLOps encompasses the entire ML lifecycle, starting with data management and preparation. This includes collecting, cleaning, and versioning datasets to ensure models are built on high-quality inputs. From there, it moves into model development, where data scientists experiment, train, and validate models. MLOps introduces automation tools like CI/CD pipelines (continuous integration/continuous deployment) to test and integrate these models seamlessly. Once deployed, models are monitored for performance, drift, and degradation, enabling teams to retrain or update them as needed. This systematic approach reduces the time from experimentation to real-world impact.
MLOps at TekFive
At TekFive, MLOps represents a natural and exciting evolution of our established expertise in DevSecOps, leveraging our deep-rooted experience in automating and securing software development lifecycles for federal clients like NASA and the U.S. Department of Veterans Affairs. For over a decade, we’ve honed a proven approach to DevSecOps, implementing CI/CD pipelines, containerization with tools like Docker and Kubernetes, and robust security practices to deliver high-quality software faster and more reliably. Transitioning into MLOps allows us to extend this foundation into the realm of machine learning, where we can apply our skills in automation, orchestration, and monitoring to streamline the entire ML lifecycle—from data preparation and model training to deployment and continuous performance tracking. Our history of bridging development, security, and operations uniquely positions us to tackle the complexities of operationalizing ML models at scale.
Our MLOps journey at TekFive builds on this DevSecOps legacy by integrating machine learning workflows into our existing frameworks, ensuring that ML models are not only developed efficiently but also deployed securely and maintained effectively in production. We recognize that ML introduces new challenges, such as managing data drift and ensuring model reliability, which align closely with the principles of automation and continuous improvement we’ve mastered in DevSecOps. By adapting our visual pipeline management and hybrid cloud expertise—demonstrated in projects like hosting over 900 containerized applications for NASA—we’re empowering data scientists and engineers to collaborate seamlessly. This transition enhances our ability to deliver innovative, AI-driven solutions to our federal partners, maintaining the same agility, security, and operational excellence that have defined our DevSecOps success.