Turn Machine Learning Experiments into Production Systems
Deploy models faster without sacrificing flexibility. Prefect bridges the gap between ML experimentation and production, letting you focus on models while we handle the infrastructure.
Why Modern Machine Learning Teams Choose Prefect
- Use distributed computing to train and tune models faster
- Save over 70% on infrastructure costs by scaling resources dynamically
- Right-size CPU and GPU compute
- Integrate with your existing ML tools and environments
Trusted by Enterprise ML Teams
Model Training & Deployment
Automate machine workflow deployment from model training through production inference jobs.
Support Production ML Systems
Manage high-availability models at scale like fraud detection to recommendation engines while tracking model lineage and versioning.
Focus on Models, Not Infrastructure
Build machine learning pipelines natively in Python and deploy them from local to production without infrastructure complexity.
Scale Without Limits
Enable the whole ML team securely with self-service deployment and granular object-level access controls (RBAC & SCIM).
Complete Visibility
Monitor model training progress and production performance with custom drift detection and seamless ML tool integrations (like MLflow).
Machine Learning Infrastructure That Just Works
- Experiment Locally: Develop and test on your machine
- Deploy Anywhere: Push to production with confidence
- Scale Automatically: Resources adapt to your needs
- Monitor Everything: Full visibility into your ML systems
1@flow
2def main_flow(
3 train_path: str = "./data/green_tripdata_2021-01.parquet",
4 val_path: str = "./data/green_tripdata_2021-02.parquet",
5) -> None:
6 """The main training pipeline"""
7 # MLflow integration
8 mlflow.set_tracking_uri("sqlite:///mlflow.db")
9 mlflow.set_experiment("nyc-taxi-experiment")
10
11 # Your existing ML code, enhanced with Prefect
12 df_train = read_data(train_path)
13 df_val = read_data(val_path)
14 train_best_model(df_train, df_val)Hear From Our Users
Our job is to provide data analysts and data scientists the data they need to create data products that drive business value. And beyond that, we focus on enabling our data scientists by removing roadblocks and giving them powerful tools that make their jobs easier. Prefect is allowing us to achieve these objectives.
Prefect's flexibility with compute resources let us run different parts of our pipeline on the right infrastructure - CPU for preprocessing, GPU for training, and distributed systems for inference.
With Prefect, we're doing things like pulling data, transforming features, splitting data sets, and training models. We wanted to do more than Airflow could offer - like making sure very large and small tasks don't run on the same machine, and adding custom Python packages.
Ready to Accelerate Your Machine Learning Pipelines?
- ✓ Python-native development
- ✓ Flexible infrastructure
- ✓ Full ML tool integration
- ✓ Production-grade reliability
Learn More About Prefect
