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.
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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)What ML Engineers Say About Us
Prefect helps us automate the workflow pipelines and data processing jobs which help feed into larger ML model systems. With Prefect, we can host on multiple platforms and run jobs that suit our data needs and infra requirements. - G2 Crowd
Prefect helps me to automatically schedule and run data & machine learning workflows in the cloud. With this serverless setup, I am saving costs and dev/maintenance work. - G2 Crowd
Prefect elegantly solves the problem of Python script automation and data/workflow orchestration. It adds logging/observation to Python scripts. Prefect is the backbone of my data landscape - orchestrating my data integrations, data models, and machine learning training. - G2 Crowd
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. - Case Study
Prefect allows us to monitor our machine learning models efficiently. The logging is very useful. - G2 Crowd
Prefect provided the flexibility to choose code storage, runners, and executors. The cherry on top was the ability to handle multi-tenancy, which simplified the workflows and reduced the development time. - G2 Crowd
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. - G2 Crowd
Helps Us Focus On Our Areas of Expertise
Prefect has enabled our team to orchestrate the execution of a variety of services, with complex interdependencies, into a single flow. Automating complex workflows helps reduce user error and helps engineers focus on their areas of expertise. - G2 Crowd
It's helping us bridge the gap between on-prem legacy systems and modern cloud-based systems. It's allowed us to automate the routine tasks between those systems and saves us time and helps reduce errors. - G2 Crowd
Ready to Accelerate Your Machine Learning Pipelines?
- ✓ Python-native development
- ✓ Flexible infrastructure
- ✓ Full ML tool integration
- ✓ Production-grade reliability
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