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Machine Learning

Turn ML experiments into production systems

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Testimonial
I used the parallelized hyperparameter tuning with Prefect and Dask to run about 350 experiments in 30 minutes, which normally would have taken 2 days
Andrew Waterman
Andrew Waterman
Machine Learning, Actium Health

Why ML teams choose Prefect

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.

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).

Hear from ML teams

Adithya B.

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

Dr. Wolfgang S.

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

Andreas N.

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

Wendy T.

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

Kamilly R.

Prefect allows us to monitor our machine learning models efficiently. The logging is very useful. - G2 Crowd

Sunil U.

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

ML Engineering Lead

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

Jonathan W.

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

Kaleb K.

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

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