What is MLOps?
MLOps, short for Machine Learning Operations, is a set of practices and tools that combines machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain AI models in production, ensuring they continue to perform accurately and deliver business value over time.
What Is MLOps?
MLOps, or Machine Learning Operations, is the discipline of managing the full lifecycle of machine learning models from development through production deployment and ongoing maintenance. Just as DevOps transformed how software is built and deployed, MLOps provides the frameworks, tools, and practices needed to operationalise AI at scale.
The challenge MLOps solves is significant: research shows that while many companies can build AI prototypes, fewer than 50% successfully deploy them to production, and even fewer maintain them effectively over time. MLOps bridges this gap by bringing engineering rigour to the AI development process.
Why MLOps Exists
Building an AI model in a research notebook is fundamentally different from running one reliably in a business environment. Without MLOps, organisations typically encounter these problems:
- Models degrade silently. An AI model trained on last year's data may produce increasingly poor results as patterns in real-world data shift, a phenomenon called model drift.
- Deployments are manual and error-prone. Data scientists may hand off models to engineering teams with inadequate documentation, leading to bugs and delays.
- Reproducibility is impossible. Without tracking which data, code, and parameters produced a given model, teams cannot reproduce results or debug problems.
- Scaling is painful. A model that works on a laptop may fail when exposed to real production traffic volumes.
Core Components of MLOps
An effective MLOps practice typically includes:
- Data pipeline management: Automated systems for collecting, cleaning, validating, and versioning the data used to train models
- Experiment tracking: Tools that record every training run, including the data, hyperparameters, code version, and resulting metrics
- Model registry: A centralised repository that stores trained models with metadata about their performance, lineage, and approval status
- Automated training pipelines: Systems that can retrain models automatically when new data is available or performance degrades
- Deployment automation: CI/CD (Continuous Integration/Continuous Deployment) pipelines specifically designed for ML models
- Model monitoring: Real-time tracking of model performance, data quality, and business impact in production
MLOps Tools and Platforms
The MLOps ecosystem has matured significantly, with options for every budget:
- End-to-end platforms: AWS SageMaker, Google Vertex AI, and Azure Machine Learning provide comprehensive MLOps capabilities within their cloud ecosystems
- Open-source tools: MLflow (experiment tracking and model registry), Kubeflow (ML pipelines on Kubernetes), and DVC (data version control) offer powerful capabilities at no licensing cost
- Specialised tools: Weights & Biases (experiment tracking), Seldon Core (model serving), and Great Expectations (data validation) excel in specific areas
- Lightweight options: For SMBs starting out, a combination of MLflow, Docker, and basic CI/CD tools can provide a solid foundation
MLOps for SMBs in Southeast Asia
For businesses in the region, MLOps maturity does not need to start at the most sophisticated level. A practical approach follows a maturity model:
Level 1 - Manual: Data scientists train models manually, deployment is a handoff to engineering, monitoring is basic. Suitable for your first AI project.
Level 2 - Automated training: Training pipelines are automated, experiment tracking is in place, and model versioning is established. Appropriate once you have 2-3 models in production.
Level 3 - Full CI/CD: Automated testing, deployment, and monitoring with automated retraining triggers. Necessary when AI is a core part of your business operations.
Level 4 - Advanced: A/B testing of models, automated feature engineering, and self-healing pipelines. Appropriate for AI-native companies at scale.
Most SMBs should aim for Level 2 within their first year of serious AI adoption and progress to Level 3 as their AI portfolio grows.
Getting Started with MLOps
- Start with experiment tracking. Install MLflow or a similar tool and begin logging every training run from day one. This single step prevents enormous headaches later.
- Version your data. Use DVC or cloud storage versioning to ensure you can always reproduce a model's training conditions.
- Containerise your models. Package models in Docker containers to ensure consistent behaviour across environments.
- Set up basic monitoring. Track model predictions and flag anomalies that could indicate drift or data quality issues.
- Automate gradually. Start with manual processes and automate them one at a time as you understand the requirements.
MLOps is what separates companies that get real, sustained value from AI from those that accumulate a graveyard of abandoned prototypes. For CEOs investing in AI, MLOps capabilities determine whether your AI investments deliver ongoing returns or become expensive one-time experiments. Without MLOps, AI models decay in production, require constant manual intervention, and create business risk rather than reducing it.
For CTOs and technical leaders, MLOps is an essential capability that must be planned and staffed from the beginning of your AI journey. The common mistake of treating deployment and maintenance as an afterthought leads to technical debt that can cost more to resolve than the original model development. Building MLOps capabilities early, even at a basic level, saves significant time and money as your AI portfolio grows.
In Southeast Asia's competitive market, the ability to deploy and maintain AI models reliably is a differentiator. Many companies in the region have started AI projects, but those with mature MLOps practices can iterate faster, catch problems earlier, and scale successful models to new markets more efficiently. This operational excellence becomes a compounding advantage as AI becomes more central to business operations.
- Start simple and build incrementally. You do not need a sophisticated MLOps platform on day one. Begin with experiment tracking and model versioning, then add automation as your needs grow.
- Choose tools that match your team size and skill level. A small team benefits more from managed platforms like Google Vertex AI or AWS SageMaker than from complex open-source toolchains that require significant DevOps expertise.
- Invest in monitoring from the start. Model drift is the most common reason AI projects fail in production, and it happens silently. Basic monitoring can catch problems before they impact business results.
- Establish clear ownership of models in production. Someone, whether a data scientist or ML engineer, must be responsible for each model health and performance.
- Document your model lineage, including what data was used, what preprocessing was applied, and what hyperparameters were chosen. This is essential for debugging, compliance, and reproducibility.
- Plan for model retraining. Decide in advance how often models should be retrained, what triggers retraining, and how new model versions are validated before deployment.
- Budget for MLOps as part of every AI project. A common rule of thumb is that ongoing operations cost 2-3 times the initial model development over the model lifetime.
Frequently Asked Questions
What is the difference between MLOps and DevOps?
DevOps manages the lifecycle of software applications, while MLOps manages the lifecycle of machine learning models. MLOps inherits many DevOps principles like CI/CD, monitoring, and automation but adds unique concerns specific to AI: data versioning, experiment tracking, model drift detection, and automated retraining. MLOps is more complex because models depend on both code and data, and their behaviour can change even when the code remains the same.
How much does MLOps cost for a small AI team?
The cost depends heavily on your approach. Using open-source tools like MLflow and Kubeflow, the primary cost is engineering time for setup and maintenance, typically requiring at least one dedicated ML engineer. Managed platforms like AWS SageMaker or Google Vertex AI charge based on usage and can range from a few hundred to several thousand dollars per month. For most SMBs, starting with a managed platform and a part-time ML engineer is the most cost-effective approach.
More Questions
You should start thinking about MLOps as soon as you deploy your first AI model to production. At minimum, implement experiment tracking and basic monitoring from day one. As you scale to multiple models or AI becomes business-critical, invest in more comprehensive MLOps tooling and processes. The worst time to invest in MLOps is after a production model has failed silently for months. The cost of prevention is always lower than the cost of recovery.
Need help implementing MLOps?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how mlops fits into your AI roadmap.