MLOps Pipeline
AdvancedMl ProjectEnd-to-end ML pipelines from training to production monitoring.
Published 27 September 2026
About MLOps Pipeline
The MLOps Pipeline stack brings together the tools needed to operationalise machine learning beyond the notebook: Apache Airflow for workflow orchestration, dbt for data transformation, PyTorch and Pandas for training and feature engineering, Snowflake as the central data warehouse, and FastAPI to serve the trained model as a REST API. Snowflake runs on GCP, AWS, or Azure, whichever already hosts the rest of the pipeline's compute.
The pipeline follows a standard MLOps pattern: raw data lands in Snowflake, dbt transforms it into clean, versioned feature tables, Airflow DAGs trigger training runs on a schedule or on data arrival, and the trained model is registered and served through a FastAPI endpoint for real-time predictions. Pandas (or Polars, for larger feature tables) handles tabular preprocessing between Snowflake reads and PyTorch tensors. Airflow's sensor and operator ecosystem makes it straightforward to chain heterogeneous steps, such as SQL transformations, Python scripts, and cloud storage operations, into a single observable DAG. Add the containerization addition (Docker, or Kubernetes once training and serving need to scale and coordinate as more than a single instance) once everything needs to run in identical, reproducible environments.
This stack suits teams that have moved past ad hoc notebooks and need a repeatable, auditable pipeline from raw data through to a live prediction endpoint. Snowflake's separation of storage and compute keeps costs predictable for large datasets, and dbt's version-controlled transformations give data lineage that ML teams can trace from model output back to raw source. Add MLflow or Weights & Biases once training runs need to be tracked and compared systematically.
Key Features
- ✓Apache Airflow DAGs orchestrate every step from raw data ingestion to model registration
- ✓dbt transforms raw Snowflake tables into versioned, tested feature sets
- ✓PyTorch handles model training on Pandas or Polars-prepared feature tables
- ✓FastAPI (or Flask) serves the trained model as a REST API for real-time predictions
- ✓Snowflake separates storage and compute; scale training data without paying for always-on compute
- ✓End-to-end pipeline is auditable: Airflow logs every run, dbt versions every transformation
When to Use MLOps Pipeline
- →Scheduled retraining pipelines for production recommendation or ranking models
- →Feature engineering workflows that transform raw event data into ML-ready tables
- →Real-time prediction APIs serving a trained model to downstream applications
- →Data science teams migrating from notebooks to production-grade pipelines
Pros
- Airflow DAGs make complex multi-step pipelines visible, retryable, and monitorable
- dbt brings software engineering practices (testing, versioning, documentation) to data transformation
- Snowflake handles petabyte-scale data without requiring infrastructure management
- FastAPI closes the loop from trained model to a live prediction endpoint, without learning a separate serving stack
Cons
- High operational complexity: Airflow, dbt, and Snowflake each have their own learning curve
- Snowflake costs can escalate quickly for frequent, compute-heavy training jobs
- FastAPI/Flask serving is a plain REST wrapper; high-throughput or GPU-batched inference needs a dedicated serving layer like TorchServe or Triton on top
Hosting Options for MLOps Pipeline
Deploy MLOps Pipeline on Google Cloud Platform
The default: Snowflake runs natively on GCP, and GCP's own compute (GKE, Compute Engine) is a common pairing for the Airflow workers, PyTorch training jobs, and FastAPI serving instance that sit alongside it. Cloud Composer runs Airflow as a managed service, and Cloud Run serves the model endpoint without a cluster to manage.
Deploy MLOps Pipeline on Amazon Web Services
Swap to AWS when the rest of the organization's infrastructure is already there: Snowflake runs natively on AWS too, and EC2 or EKS covers the same training and serving compute this pipeline needs. Amazon MWAA runs Airflow as a managed service, and SageMaker or GPU EC2 instances take the PyTorch training jobs.
Deploy MLOps Pipeline on Microsoft Azure
Swap to Azure for organizations standardized on Microsoft's cloud: Snowflake runs natively on Azure as well, paired with AKS or Azure's own compute for training and serving. Azure Machine Learning handles GPU training runs, Azure Container Apps serves the FastAPI endpoint, and Airflow runs on AKS or as a managed Apache Airflow job in Microsoft Fabric.
These are highlighted picks. To see all the tools, check the Hosting & Cloud category.
Model Serving Options for MLOps Pipeline
MLOps Pipeline with FastAPI
The default: async request handling for concurrent predictions, automatic OpenAPI docs for the prediction endpoint, and the same framework already common in the wider Python ML-serving ecosystem.
MLOps Pipeline with Flask
Swap in Flask when the team already has other Flask services and wants one consistent framework, or when the serving endpoint is simple enough that FastAPI's async handling and generated docs aren't worth the extra dependency.
These are highlighted picks. To see all the tools, check the Python Backend Frameworks category.
Data Libraries Options for MLOps Pipeline
MLOps Pipeline with Pandas
The default: the deepest ecosystem of any Python DataFrame library, and the library most PyTorch/Snowflake tutorials and examples already assume.
MLOps Pipeline with Polars
Swap in Polars once feature tables get large enough that Pandas' single-threaded processing becomes the pipeline's bottleneck; its Rust-based, multi-threaded engine is significantly faster on the same hardware, at the cost of a smaller ecosystem of examples.
These are highlighted picks. To see all the tools, check the Data & ML Libraries category.
Orchestrator Options for MLOps Pipeline
MLOps Pipeline with Apache Airflow
The default: scheduled DAGs chaining dbt feature builds and PyTorch training runs, with retries, sensors for data arrival, and the largest operational knowledge base of the three options.
MLOps Pipeline with Dagster
Swap in Dagster for asset-centric orchestration: feature tables and trained models become versioned assets with lineage from Snowflake source to model artifact, and software-defined schedules fit evolving training pipelines.
MLOps Pipeline with Prefect
Swap in Prefect when training logic is dynamic: conditional branches and mapped runs over experiment configs express more naturally as Prefect flows than as static DAG definitions.
MLOps Pipeline Add-ons
Each addition below extends this stack with a capability the base stack works fine without. None are required: include the ones your product actually needs when building this stack, and skip the rest.
Experiment Tracking Add-ons
Add experiment tracking when you want to log hyperparameters, metrics, and model versions across training runs instead of comparing them by hand.
MLOps Pipeline with MLflow
MLflow logs hyperparameters, metrics, and model artifacts for every scheduled training run, so retraining results can be compared and traced back to the pipeline run that produced them. Its model registry tracks which version is currently deployed behind the FastAPI endpoint.
MLOps Pipeline with Weights & Biases
Weights & Biases covers the same tracking job with richer, real-time dashboards and easier team collaboration out of the box, at the cost of running on W&B's cloud rather than self-hosted infrastructure.
CI/CD Add-ons
Add CI/CD when you want a dedicated pipeline for running tests, linting, or multi-stage builds before a deploy goes out. Many hosting platforms already redeploy automatically on every push on their own — a CI/CD tool adds the most value on top of that by gating the deploy on a passing test suite, and matters even more when the hosting choice does not auto-deploy at all, such as a self-hosted server.
MLOps Pipeline with GitHub Actions
Lints the PyTorch training scripts and runs dbt test against the Snowflake feature tables on every push, before either reaches the scheduled Airflow run.
MLOps Pipeline with GitLab CI/CD
The same lint-and-test step via .gitlab-ci.yml, for teams running this pipeline's code from a GitLab repo.
These are highlighted picks. To see all the tools, check the CI/CD Pipelines category.
Containerization Add-ons
Add containerization when you want the app packaged the same way across local development, staging, and production, or need to deploy somewhere that isn't a managed serverless platform.
MLOps Pipeline with Docker
Package the Airflow scheduler, dbt runs, and PyTorch training jobs identically across a laptop, CI runner, and cloud VM, so a training run that works locally behaves the same in production.
MLOps Pipeline with Kubernetes
Orchestrates the training jobs and the FastAPI serving instance together once either needs to run as more than one replica, autoscale with traffic, or coordinate with other services. Adds real operational complexity, so it earns its place once Docker alone stops being enough.
These are highlighted picks. To see all the tools, check the Containerization category.
Frequently Asked Questions about MLOps Pipeline
Do I need this over the simpler PyTorch ML Training stack?
Only once training needs to be scheduled, repeatable, and traceable back to the raw data it ran against. PyTorch ML Training is the right starting point for exploration and one-off training runs; this stack adds Airflow, dbt, and Snowflake specifically for teams running the same pipeline on a recurring schedule against production data.
Do I need the containerization addition from day one?
Not for a single person iterating locally. It earns its place once training needs to run identically across a laptop, a CI runner, and wherever the scheduled Airflow job actually executes.
FastAPI or Flask for serving the model?
FastAPI is the default: async request handling for concurrent predictions and automatic OpenAPI docs for the endpoint. Flask is simpler and worth it mainly when the team already runs other Flask services and wants one consistent framework.
Docker or Kubernetes for this pipeline?
Docker alone is enough for a single training job and a single serving instance. Add Kubernetes once the API needs to run as more than one replica, autoscale with traffic, or coordinate with other services outside this pipeline.
Why only GCP, AWS, or Azure for hosting?
Snowflake itself only runs on those three clouds, so the hosting choice here is really about which cloud runs the Airflow workers, PyTorch training jobs, and FastAPI serving instance alongside it, not a generic app-hosting decision. Pick whichever already hosts the rest of the team's infrastructure.
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Scores
Tools in the MLOps Pipeline Stack
Programming Languages
Databases
Data Engineering & ETL
Data & ML Libraries
Add-ons (optional — add any, or none)
Experiment Tracking
CI/CD
Containerization
MLOps Pipeline Pricing
The pipeline's core (PyTorch, Pandas or Polars, Apache Airflow, dbt Core, FastAPI or Flask, and Python) is open source and free to run on your own infrastructure. The main cost is Snowflake, which is usage-based with custom pricing that scales with compute and storage, plus the underlying cloud (GCP, AWS, or Azure) billing for the Airflow, training, and serving compute it all runs on. Experiment tracking is optional: MLflow is free to self-host, Weights & Biases has a free tier with paid team plans from around $50/mo.
PyTorch, Pandas/Polars, Apache Airflow, dbt Core, FastAPI/Flask, and Python are all free; dbt Cloud is a paid option.
Custom pricing that scales with compute and storage; no fixed monthly fee.
Model training, running Airflow, and the FastAPI serving instance all incur cloud compute costs on whichever cloud is chosen, on top of Snowflake's own billing.
MLflow is free to self-host; Weights & Biases has a free tier with paid team plans from around $50/mo.