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Luis Guerra is a Principal Cloud Architect at Caylent, where he oversees client engagements by providing best practices, technical guidance, and hands-on development alongside engineering teams. An active member of the AWS community in Mexico, Luis regularly speaks at User Group Meetups and Community Days. Passionate about AWS and cloud architecture, his philosophy centers on leveraging technology to make people's lives easier. Outside of work, he serves as a Karate coach and enjoys golf.
View Luis's articlesIf you have mastered DevOps, you have probably faced the next big challenge: bringing machine learning models into production. Data scientists train incredible models in notebooks, but then comes the million-dollar question: How do you put them into production in a reliable and scalable way?
Models live in notebooks, isolated from the CI/CD workflows that power modern software. As a result, deployments are slow, updates are rare, and business impact is delayed. The reason is not model quality. It is the lack of integration between two worlds that have historically operated in isolation: Machine Learning and DevOps.
Bridging that gap is exactly where MLOps comes in. While only 32% of models traditionally created reach production, companies with mature MLOps practices can deploy models in less than six months, an achievement only 23% of organizations without MLOps manage to accomplish.
Beyond speed, MLOps fundamentally improves quality and reliability by bringing the same engineering standards that power modern software development to the machine learning lifecycle. It also introduces new complexity, including model management, data versioning, and algorithm performance monitoring. When integrated effectively, these practices not only accelerate deployments but also strengthen collaboration between teams.
In this blog, you will learn how to build that integration step by step, including architecture best practices, automation workflows, and a real-world example of how one startup went from manual monthly updates to automated weekly retraining.
The goals of DevOps and MLOps are similar: speed, reliability, automation, and scalability.
DevOps focuses on continuous integration, continuous delivery, infrastructure as code, automated testing, and continuous deployment. It's a world where code is central, and everything revolves around software versions.
MLOps builds on those principles but adds more complexity, such as data and model versioning, data quality validation, model monitoring, and automated retraining pipelines. The key difference is that ML models don't just depend on code but also on the data that feeds them, which changes constantly.
Both approaches traditionally work with different tools and separate teams, but the ultimate goal is to have a unified cycle in which models are treated like any other software artifact.
The first obstacle is usually organizational. Data, machine learning, and DevOps teams operate independently and quite separately in functions, each with their own tools, processes, and metrics. Data scientists prefer Jupyter notebooks and iterative experimentation, while DevOps engineers seek deterministic and repeatable processes. This creates organizational friction as data scientists might run dozens of experiments before finding a promising model, making it difficult to fit into traditional CI/CD pipelines that expect clear, versioned releases. The challenge intensifies because in the ML world, not every code change represents a new model version - a model's behavior depends equally on the data it was trained on, which may change independently of the code.
The second challenge is technical. The pipelines and tools traditionally used for each discipline hinder the complete traceability and auditability of models. A model may have been trained with a specific data version, using a particular code version, but if these elements are not synchronized, reproducing results becomes almost impossible.
Finally, deployments are slow because each model requires manual processes. A change in the model's code does not necessarily trigger a complete validation and deployment cycle, as it does with traditional applications. Beyond the process complexity, there's a cost barrier: running tests and deployment on traditional code is relatively cheap, but validating ML models requires expensive compute resources for retraining and evaluation, making teams hesitant to automate these cycles without careful planning.
Integration allows models to be treated as traditional software – meaning they can be versioned, tested, and deployed from the same CI/CD pipeline. Each commit to the repository can start a pipeline that trains, validates, and deploys the model if it meets the established quality criteria.
Collaboration dramatically improves when all teams share practices in repositories, logs, metrics, and monitoring tools. Data scientists can observe how their models behave in production, while DevOps engineers gain a better understanding of the specific needs of ML algorithms.
From a control and security perspective, applying the same DevSecOps standards to models and data ensures consistency in audits, regulatory compliance, and risk management.
Faster feature deployment in traditional applications drives business value by improving ROI, enhancing user experience, attracting new users, and fostering innovation. The same principle applies when deploying a new model version. More accurate predictions or quicker results not only improve the user experience but also advance key business objectives.
Once you understand the “why” behind MLOps and DevOps integration, the next step is building a secure, scalable architecture that can support it in production. On AWS, that typically means designing for isolation, automation, traceability, and continuous delivery from the ground up.
The following architecture demonstrates a series of practices for a highly secure scenario with a specific need to work across multiple AWS accounts. However, this can be greatly simplified if there is no need for such high security and multiple accounts.
The architecture should use separate AWS accounts for experimentation, development, staging, and production, in addition to a secure assets account. This separation allows for granular access control, complete resource isolation, and progressive deployment with controlled rollback. Each environment can have specific configurations according to its needs, from smaller instances for experimentation to scalable infrastructure for production.
| Purpose | AWS Service |
|---|---|
| Training and Registration | Amazon SageMaker (Pipelines + Model Registry) |
| Version Control | Git Repos (Github, GitLab, BitBucket) |
| CI/CD | AWS CodePipeline + AWS CodeBuild + Amazon CodeDeploy |
| Containers | Amazon ECR |
| ETL / Data | AWS Glue, Amazon Athena, Amazon SageMaker Feature Store |
| Model and Data Monitoring | Amazon SageMaker Model Monitor, Amazon CloudWatch |
The flow begins when data scientists experiment in Amazon SageMaker Studio with data stored in the data lake (Data warehouse, database, etc.). Once they have a promising prototype, they transform it into a reproducible Amazon SageMaker Pipeline that includes all steps from data preparation to model evaluation.
When the pipeline is ready, a AWS CodePipeline execution is automatically triggered by either a commit to the repository, a predefined schedule, or a detected drift in production. The pipeline trains the model, executes automated validation tests, and, if successful, registers the model in the Model Registry with all its metrics and metadata. This Registry, as well as the Amazon S3 Bucket, are stored in the secure account to have a single source of truth and protect assets created by the pipelines against (intentional or unintentional) edits.
After manual or automatic approval based on predefined criteria, the model is progressively deployed from development, through staging, to production. Once in production, Amazon SageMaker Model Monitor continuously monitors performance and detects data or model drift, triggering retraining processes when necessary.
Configure Amazon SageMaker Studio as the unified environment where all data scientists work. This ensures consistency in libraries, Python versions, and data access. Integrate this environment with corporate Git repositories so that all experimental code is versioned from day one. The configuration of this environment can be done via Terraform + SSM to ensure repeatability.
Develop Amazon SageMaker Pipeline templates that cover the most common use cases in your organization. These templates should include standard steps such as data validation, training, evaluation, bias tests, and model registration. Reusability accelerates development and ensures consistency.
Define minimum metrics that every model must exceed before advancing in the pipeline. This includes performance metrics like accuracy or F1-score, as well as data drift tests, schema validation, and bias verification. These gates should be configurable by model type or use case.
Connect Amazon SageMaker Pipelines with AWS CodePipeline so that code or data changes automatically trigger the entire flow. This requires configuring webhooks, appropriate IAM policies, and orchestration between services.
Configure Amazon CloudWatch, Amazon SageMaker Model Monitor, and observability tools to have complete visibility from training to production. Alerts should integrate with existing incident systems so that operations teams can respond quickly to model problems.
Full integration primarily requires native AWS services, which simplifies management but can increase costs. Amazon SageMaker represents the most significant expense, especially during intensive training. A ml.g4dn.xlarge instance for training costs approximately $1.50 per hour, while ml.m5.large inference endpoints are around $0.10 per hour.
For organizations with limited budgets, open-source alternatives can be run on EC2, EKS, or ECS. MLflow for experiment tracking, Kubeflow for pipelines, and Seldon Core for serving can significantly reduce costs. However, they require a greater investment in setup and maintenance time, which are additional costs that must ultimately be added as engineering time.
An effective hybrid strategy is to use Amazon SageMaker for experimentation and training, but deploy models on more economical endpoints using custom containers in Amazon ECS or Amazon EKS. This offers the best balance between ease of use and cost control.
Integrating MLOps with DevOps on AWS fundamentally transforms how organizations develop, deploy, and maintain machine learning models. Models cease to be isolated projects and become software components with complete traceability, guaranteed quality, and continuous updates.
If you have already mastered DevOps, this is the natural next step in your technical evolution if you are attracted to the world of ML, AI, or Deep Learning. ML models are not so different from traditional software, and applying the same engineering disciplines to them transforms them into reliable and scalable business assets.
To explore implementation in more detail, start with the official Amazon SageMaker Pipelines documentation and the MLOps workshops available on AWS. The initial investment in setup time quickly pays off when models begin to update and improve automatically.
Implementing MLOps and DevOps together can be complex, but you don’t have to do it alone. Our Artificial Intelligence & MLOps experts help organizations turn data into smarter decisions and a real competitive edge by applying advanced machine learning techniques on AWS. We build scalable, efficient solutions that accelerate time-to-market and set both data and business teams up for success. And with our Generative AI offerings, we make it easier for organizations of any size to amplify the power of their data, speed up experimentation, and confidently adopt the next generation of AI technologies. Contact us today to get started.
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Luis Guerra