MLOps
Operationalising machine learning at scale
We help you take your machine learning models from experimentation to production—safely, efficiently, and at scale. DVT’s MLOps services ensure that your models are continuously integrated, deployed, monitored, and maintained with the right governance, automation, and performance in mind.
Bring engineering discipline
to your data science innovation
Staying competitive with AI requires more than good ideas—it demands the ability to develop, deploy, and manage machine learning models efficiently. At DVT, we bring structure and scale to your data science initiatives through end-to-end MLOps. From design to deployment, we ensure your machine learning operations are robust, scalable, secure, and ready to deliver measurable value.
What our clients say
"Quality technical staff"
"Breath of fresh air"
"Invaluable part of the team"
"Can be trusted to take on highly dynamic & difficult to define work"
"People are highly skilled professional & awesome"
"Extremely professional, adaptable & flexible"
"An integral part of our team and their support has been superb"
"The agile toolkit & mindset is an invaluable enabler"
"Went above & beyond the expectation"
"Instrumental in creating ambassadors & embedding agile discipline"
"Formed a highly effective & efficient team that aims to flexibly achieve a shared goal"
"World-class mobile platform in record time"
"Magic started to happen"
"Professionalism, focus & dedication"
"Impressed with their familiarity of Agile methodology"
"One of our valuable partners"
"Excellent partner with quick professional solutions."
"Quality technical staff and consulting, backed by friendly & professional management."
"Professional consulting and collaboration. Realised our requirements."
"Committed to success, delivers on their promise to create cutting edge technology."
"Comforting to have such a professional partner on call to help you."
"Creativity delivered what others couldn't; cost-effective and timely."
"To say that we were impressed would be an understatement."
What our clients say
Roadmap to Adopting MLOps
Successful MLOps adoption requires a structured, step-by-step approach that ensures seamless integration and long-term value:
Start with a full review of your current ML and DevOps maturity. Identify gaps, opportunities, and align goals with business priorities. Create a clear roadmap covering scope, timelines, and resource needs.
Choose technologies that match your organisation’s scale and use cases—such as Azure, AWS, Databricks, or Microsoft Fabric. Ensure integration across platforms for smooth data flow and collaboration.
Bring together the right mix of talent—data scientists, ML engineers, DevOps, and IT teams. Strong collaboration across disciplines is essential for embedding MLOps successfully.
Develop pipelines that automate data prep, model training, deployment, and monitoring. Prioritise scalability, traceability, and CI/CD to accelerate delivery and minimise errors.
Set up monitoring to detect issues like model drift early. Apply governance to manage risk, maintain compliance, and ensure the reliability of all deployed models.
Continuously improve by incorporating feedback, tracking performance, and adapting to new tools and methods. MLOps should evolve as your organisation grows in AI maturity.
Benefits of Adopting MLOps
Integrating MLOps into your AI practice unlocks measurable gains in performance, collaboration, and scale:
Benefits of Adopting MLOps
Integrating MLOps into your AI practice unlocks measurable gains in performance, collaboration, and scale:
Automating key processes—like data prep, training, testing, and deployment—helps your team move faster, reduce manual effort, and shorten time-to-value for machine learning models.
Platforms & Toolsets
Microsoft Azure
Azure Machine Learning offers a robust toolkit for MLOps, including Azure ML registries for model versioning and sharing, Azure DevOps for CI/CD pipeline automation, and MLflow for experiment tracking and model lifecycle management.
Amazon Web Services
AWS provides an extensive suite of MLOps tools, including SageMaker for model building, training and deployment; CodePipeline for automating CI/CD workflows; and SageMaker Model Monitor for continuous performance monitoring of deployed models.
Data Intelligence Platforms
Databricks and Microsoft Fabric strengthen MLOps capabilities through advanced data processing, collaborative development environments, and seamless tool integration. Databricks supports Delta Lake for reliable data storage and MLflow for model tracking, while Microsoft Fabric integrates with Power BI and Azure Data Lake to provide a unified data intelligence ecosystem.
Necessary Expertise
Data Science and
ML Expertise
Strong foundation in machine learning, including data preprocessing, algorithm selection, model training, and evaluation.
DevOps
Skills
Practical knowledge of CI/CD pipelines, containerisation tools like Docker, and orchestration platforms such as Kubernetes.
Cloud
Computing
Experience deploying and managing solutions on cloud platforms such as Azure and AWS, using their machine learning and DevOps toolsets.
Collaboration and
Communication
Ability to collaborate across technical and business teams, and to communicate clearly with diverse stakeholders.
Benefits of DVT’s MLOps Service
DVT’s MLOps service is designed to meet the critical success factors for implementing MLOps effectively within enterprise environments. Our engineering-led approach ensures we deliver measurable value aligned to your business and technical needs.
Benefits of DVT’s MLOps Service
DVT’s MLOps service is designed to meet the critical success factors for implementing MLOps effectively within enterprise environments. Our engineering-led approach ensures we deliver measurable value aligned to your business and technical needs.
Our team of experienced MLOps professionals has a strong track record in designing, deploying, and managing ML environments. We apply industry best practices tailored to your organisation’s level of MLOps maturity, ensuring a solution that fits – not a one-size-fits-all approach.
News & Insights
FAQ's
MLOps (Machine Learning Operations) ensures that ML models are developed, deployed, and maintained efficiently at scale. It brings stability, speed, and governance to your AI initiatives.
