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Prefect Enables Data Science Teams in Production
The fastest way to deploy data science code and models to production while retaining full visibility and security.
Find us at Booth #17!
Deploy your models to prod faster
Don't change your code for your orchestrator: Prefect is pythonic, and doesn't require boilerplate.
- Scheduling & orchestration: from model training to pipelines
- Flexible infra options, including up to 250 free hours of Prefect-managed compute
- Turn your models into hosted APIs, making them accessible to anyone programmatically
1import httpx
2from prefect import flow
3
4
5@flow(log_prints=True)
6def get_repo_info(repo_name: str = "PrefectHQ/prefect"):
7 url = f"https://api.github.com/repos/{repo_name}"
8 response = httpx.get(url)
9 response.raise_for_status()
10 repo = response.json()
11 print(f"{repo_name} repository statistics 🤓:")
12 print(f"Stars 🌠 : {repo['stargazers_count']}")
13 print(f"Forks 🍴 : {repo['forks_count']}")
14
15
16if __name__ == "__main__":
17 get_repo_info.serve(name="my-first-deployment")Focus on your code, not the infra
Prefect’s hybrid execution model means your code just works, locally or in production.
- Spin up a Prefect server for local development
- Simplified infrastructure concepts means you can get into production fast, and scale up without contorting your code
- Move from local to remote or between cloud services with ease
Talks
2PM Tue 4/23: Join Taylor (Prefect) & Mike (Education First) for a demo of how Mike's data engineer team unblocks data science deployments with Prefect.
11AM Wed 4/24: Hear from Jeff & Bill (Prefect) on a practical guide to implementing a platform approach when managing data workflows
Unblocking Data Science Teams with Prefect
Stop waiting for data engineering teams to run custom deployments for each data science model and each script. Instead, enable data science teams to run Python code automatically.
At Education First, Mike Grabbe’s data platform team has completely unlocked the data science team. Hear from Taylor and Mike on resolving a number of challenges with Prefect including Python work being deployed on unstable infrastructure and data engineering being a bottleneck.
Embracing a Platform Approach to Workflow Management
Even if you agree you need a data platform - no one agrees on what the requirements of one are. This talk will dive into what an interfaces approach practically means and examine the benefits and risks of implementing a platform approach to workflow management with real world examples.
Embracing a platform approach to workflow management increases velocity by making workflows composable, extensible, and observable. You want to give data people the tools they need to succeed while abstracting away infrastructure headaches.
More Reading
Read about how you can deploy any Python code securely and consistently with Prefect.
