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What is MLOps?
MLOps is the practice of applying DevOps principles to machine learning so that models move from experimentation into production and stay reliable once they are there. Rather than treating a trained model as a one-off artifact, MLOps treats the whole lifecycle — data, code, model, deployment and monitoring — as something to be versioned, automated and continuously maintained. As the MLOps Guide puts it, the aim is to bring DevOps methodology to the machine learning model lifecycle.
What that means in practice
A project is usually considered MLOps-oriented when it covers several of these areas:
- Data and model versioning — tracking which data and which model version produced a given result.
- Feature management and storing — organising reusable features instead of recomputing them ad hoc.
- Automation of pipelines and processes — reproducible steps from raw data to deployed model.
- CI/CD for machine learning — testing and releasing models with the same discipline as software.
- Continuous monitoring — watching deployed models for drift, degradation or failures.
Why teams bother
The core problem is that a notebook that works locally is not a product. A concrete scenario: a data scientist hands over a model, and three months later nobody can reproduce the training run, the input schema has shifted, and the API is silently returning worse predictions. Versioning makes the run reproducible; automated pipelines make retraining routine; monitoring catches the decay before users do.
A useful next step
Pick the weakest of the five areas above for your current project and fix only that one first. If you cannot say which data and code produced your last deployed model, start with versioning; if you can, but retraining is manual and error-prone, start with pipeline automation. Broad rollouts tend to stall, while one fixed link in the chain usually exposes the next.
How can I build an end-to-end MLOps pipeline using DVC, CML, and IBM Watson Machine Learning?
Start by treating the pipeline as four connected stages: version the data and model, automate the training run, deploy the model as an API, and monitor it in production. The MLOps Guide walks through exactly this combination, using Cookiecutter for project scaffolding, Terraform to set up the IBM environment, DVC for versioning and pipelines, CML with GitHub Actions for CI/CD, and IBM Watson Machine Learning for deployment.
Stage-by-stage build
- Project structure — Generate a consistent layout with Cookiecutter so training, data and deployment code live in predictable places. A stable skeleton is what makes the later automation repeatable.
- Data and model versioning with DVC — Track datasets and model artifacts in DVC while keeping Git for code. The guide covers data versioning and working with DVC pipelines, which lets you define training as reproducible stages rather than a single script.
- CI/CD with CML and GitHub Actions — The guide splits this into implementing tests, continuous integration with CML and GitHub Actions, and continuous delivery with CML, GitHub Actions and Watson ML. CML is what turns a training run into something that reports results back to the repository.
- Deployment with Watson Machine Learning — Push the trained model to a Watson deployment space so it is served as an API. Terraform handles the IBM environment setup and deployment space before any of this runs.
- Monitoring with IBM OpenScale — Continuous monitoring is listed as a core MLOps principle, and the guide pairs it with OpenScale for the deployed model.
How the tools divide the work
| Concern | Tool in this guide |
|---|---|
| Reproducible project scaffold | Cookiecutter |
| Data and model versioning, pipelines | DVC |
| CI, test reporting, delivery | CML + GitHub Actions |
| IBM environment and deployment space | Terraform |
| Model serving as API | IBM Watson Machine Learning |
| Production monitoring | IBM OpenScale |
The guide describes itself as modular: a team can swap tools at any point depending on preferences and use case, so the table above is a working default rather than a fixed requirement.
A practical next step
Pick one model you already retrain by hand, scaffold it with Cookiecutter, and move only its data and training stages into DVC first. Once a single dvc repro reproduces your model, add the CML and GitHub Actions layer, then deploy through Watson Machine Learning. This ordering keeps failures attributable: versioning problems surface before CI problems, and CI problems surface before deployment problems.
For the full walkthrough, see MLOps Guide. If you want a second reference for the versioning and pipeline concepts, DVC documents the underlying commands, and GitHub Docs covers the Actions side of the CI/CD setup.
What are the main differences between MLOps and DevOps for machine learning projects?
MLOps extends DevOps practices to the machine learning lifecycle, but the differences go beyond tooling. DevOps manages code; MLOps must manage code, data, and models together, because a model's behavior changes when its training data or environment changes — not only when the code changes.
Where the two diverge
| Dimension | DevOps | MLOps |
|---|---|---|
| Primary artifacts | Application code, configs | Code, datasets, trained models, features |
| Versioning | Source control for code | Versioning of data and models alongside code |
| Testing | Unit, integration, end-to-end | Those plus data validation and model evaluation |
| Pipeline | Build and deploy software | Build, train, evaluate, deploy, and retrain |
| Production concern | Uptime and correctness | Uptime, correctness, and drift in data or model quality |
| Change trigger | Code commits | Code commits, new data, or degraded metrics |
The practical consequence: in DevOps a passing test suite usually means you can ship. In MLOps, a model can pass every test and still decay in production as the world it was trained on shifts. That is why continuous monitoring and retraining loops are treated as first-class concerns rather than afterthoughts.
What this guide actually covers
MLOps Guide frames MLOps as bringing DevOps methodology to the machine learning model lifecycle, and lists five recurring themes: data and model versioning, feature management and storing, pipeline automation, CI/CD for machine learning, and continuous model monitoring. It also notes that its environment is modular, so teams can swap tools based on project preferences and use cases — a useful signal that the tool choices are illustrative rather than mandatory.
The tutorial walks through a concrete stack — Cookiecutter for project structure, Terraform for environment setup, DVC for data versioning and pipelines, CML with GitHub Actions for CI/CD, and IBM Watson services for deployment and monitoring — which is a reasonable way to see how the abstract differences above turn into actual files, jobs, and dashboards.
A decision criterion
If your team already runs DevOps, the question is not whether to adopt MLOps wholesale but which of the three extra artifacts — data, features, or models — currently lacks versioning and monitoring. Start there. A team with stable data but no model monitoring has a different first step than one retraining weekly from unversioned datasets.
As a next step, read the guide's principles section before the implementation section; the tooling only makes sense once you accept that data and model state must be tracked with the same discipline as code.
How do I set up an MLOps environment with Terraform and IBM Watson tools?
Set up the environment in layers, using Terraform for infrastructure and the IBM Watson tools for the model lifecycle. The MLOps Guide walks through exactly this combination: Terraform to provision the IBM environment and deployment space, Watson Machine Learning to deploy models as APIs, and Watson OpenScale for monitoring.
A practical order of work
- Define the project structure first. The guide starts new projects with Cookiecutter, which gives you a consistent layout before any cloud resources exist.
- Provision IBM Cloud access with Terraform. The guide's "Setting Up the IBM Environment with Terraform" step covers IAM access and then creating the deployment space. Treat the Terraform configuration as the single source of truth so the space can be recreated or torn down cleanly.
- Version data and models with DVC, including pipelines, so training runs are reproducible rather than manual.
- Deploy with Watson Machine Learning, exposing the model as an API.
- Add CI/CD with CML and GitHub Actions — tests first, then continuous integration, then continuous delivery into Watson ML.
- Monitor with IBM OpenScale once the model is live, so drift and quality issues surface after deployment rather than at the next retraining cycle.
Where the trade-offs sit
| Choice | Good for | Cost or constraint |
|---|---|---|
| Terraform-managed IBM environment | Teams that need repeatable, reviewable infrastructure | You must learn Terraform state and IAM concepts up front |
| Watson ML deployment | Fast path from model to API on IBM Cloud | Ties deployment to the IBM ecosystem |
| OpenScale monitoring | Production oversight without building your own dashboards | Another service to configure and pay for |
| DVC + CML + GitHub Actions | Open, portable versioning and CI | More moving parts than a single integrated platform |
The guide is explicitly modular: you can swap tools at any point, so if your team already uses a different CI system or cloud, keep the principles (versioning, automation, CI/CD, monitoring) and replace the specific tools.
Next step: read the guide's Principles of MLOps section before the implementation chapters, then follow the implementation guide in order — Cookiecutter, Terraform, DVC, Watson ML, CML, OpenScale. Start with MLOps Guide and work through one layer at a time rather than provisioning everything before your first deployment.
When should I use DVC for data versioning versus other version control tools?
Use DVC when your version-controlled artifact is data or a model rather than source code, and when you need to reproduce a training run from an exact dataset snapshot. Git remains the right tool for code, configuration and small text files; DVC is designed to sit alongside Git and handle the large binary files Git handles poorly.
The MLOps Guide covers DVC specifically under its versioning section, alongside data versioning and pipeline work, and treats versioning as one of the core MLOps principles next to automation, feature storing, CI/CD and continuous monitoring. That framing is the clearest signal of intended use: DVC belongs in the data-and-model half of your repository, not as a replacement for Git.
H3 When DVC is the better fit
- Your datasets or model weights are too large or change too often to commit directly to Git.
- You need to point a specific model or metric back to the exact data version that produced it.
- You want pipeline stages and their outputs tracked together, so a rerun is reproducible rather than manual.
- Several people or CI jobs need to pull the same data version without passing files around by hand.
H3 When a general version control tool is enough
- The artifact is code, notebooks, schemas or small configuration.
- Your data is small, stable and rarely regenerated.
- You only need line-by-line history and review, not dataset lineage.
H3 Practical decision criterion
Ask whether you could rebuild yesterday's model from today's repository. If the answer is no because the data moved underneath you, you need data versioning; DVC is the option this guide builds its tutorial around. If the answer is yes because the data is fixed and small, plain Git is simpler and adds no extra storage or tooling.
A concrete scenario: a team retrains weekly on a growing customer dataset and must explain to an auditor which rows produced a deployed model. Git alone cannot answer that. With DVC-style versioning, the commit records the data revision and the pipeline output, so the audit question becomes a checkout.
One trade-off to weigh: DVC adds a remote storage dependency and a learning curve for anyone used to Git only. If your team is small and your data is modest, that overhead may not pay off. The guide notes its environment is modular, so tools can be swapped at any point to suit project preferences and use cases, which applies here too.
Next step: read the versioning section of MLOps Guide and check whether your current repository can reproduce a past training run; if it cannot, that gap tells you whether DVC is worth adopting now or later.
How do I implement continuous monitoring for machine learning models with IBM Watson OpenScale?
Continuous monitoring with IBM Watson OpenScale means checking a deployed model’s behaviour after release — not just its training accuracy. The MLOps Guide places OpenScale at the monitoring stage of its end-to-end workflow, after deployment with Watson Machine Learning and CI/CD with CML and GitHub Actions. Its stated purpose is to keep production models reliable by watching for drift and quality problems over time.
What monitoring covers in this guide
The guide lists continuous monitoring as one of the core MLOps features, alongside data and model versioning, feature storing, pipeline automation, and CI/CD for machine learning. OpenScale is the tool it uses for that monitoring step. In the guide’s architecture, the flow runs from versioning through deployment and continuous delivery, then into monitoring — so OpenScale is the last link in a chain, not a standalone add-on.
How to approach it practically
- Get a model deployed and reachable as an API first. Monitoring has nothing to observe until a model is serving predictions.
- Establish what “normal” looks like. Record the training data distributions, expected input ranges, and baseline performance so drift has something to be measured against.
- Decide what to watch: input drift, output drift, accuracy or fairness metrics where labels arrive later, and request volume or latency.
- Wire monitoring into the same pipeline that deploys the model, so every new version is enrolled automatically rather than by hand.
- Define who acts when a threshold trips — retrain, roll back, or investigate — before you need it.
A concrete scenario
A team ships a credit-scoring model through Watson Machine Learning. Three months later, application volumes shift and the input population no longer resembles the training set. Without monitoring, the first signal is a business complaint. With OpenScale in the loop, the drift appears as an alert while the model is still serving, and the team can retrain on fresher data before decisions degrade.
Trade-offs to weigh
| Choice | Benefit | Cost |
|---|---|---|
| Full continuous monitoring | Early drift detection, audit trail | Setup effort, ongoing review time |
| Monitoring only accuracy | Simple, familiar metric | Blind to drift until labels arrive |
| Manual spot checks | No tooling needed | Misses gradual shifts, inconsistent |
Monitoring is only as useful as the response behind it. If no one owns the alerts, dashboards become decoration.
Next step
Read the monitoring section of MLOps Guide and work backwards: note what OpenScale expects to receive, then check whether your deployment and versioning setup already produces it. If not, fix those earlier stages first — the guide is modular, so tools can be swapped, but the sequence matters.
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