vtuulos

9 ms·

31.
Show HN: I Wrote a Book, Effective Data Science Infrastructure(outerbounds.com)
5 points by vtuulos 4y ago|1 comments
32.
Testable Technical Documentation with Notebooks, Nbdev, and Docusaurus(outerbounds.com)
3 points by vtuulos 4y ago|0 comments
33.
by vtuulos 5y ago
Thanks for sharing! I am still proud of this hack :)
34.
by vtuulos 5y ago
Outerbounds | Systems dev | SF / Remote | Full-time Outerbounds was founded recently to commercialize Metaflow, an open-source Python framework that makes infrastructure easily accessible for machine learning/data science projects
35.
Integrating Pythonic visual reports into ML pipelines(outerbounds.com)
6 points by vtuulos 5y ago|0 comments
36.
by vtuulos 5y ago
Yep, Kedro and Metaflow are more similar to each other than to other generic DAG orchestrators like Airflow. Kedro and Metaflow make it easier to develop robust ML projects where orchestration plays an important role but it is not everythin
37.
by vtuulos 5y ago
Outerbounds | Systems dev | SF / Remote | Full-time Outerbounds was founded recently to commercialize Metaflow, an open-source Python framework that makes infrastructure easily accessible for machine learning/data science projects
38.
by vtuulos 5y ago
Outerbounds | Systems dev | SF / Remote | Full-time Outerbounds was founded recently to commercialize Metaflow, an open-source Python framework that makes infrastructure easily accessible for machine learning/data science projects
39.
MLOps and DevOps: Why Data Makes It Different(oreilly.com)
4 points by vtuulos 5y ago|1 comments
40.
Open-Sourcing a Monitoring GUI for Metaflow, Netflix’s ML Platform(netflixtechblog.medium.com)
4 points by vtuulos 5y ago|0 comments
41.
by vtuulos 5y ago
There are a few different ways to deal with spot interruptions. First, it is a good idea to specify multiple instance types in your compute environment so even if some instances types become unavailable in spot, Batch can use another type a
42.
by vtuulos 5y ago
Outerbounds | Frontend dev | SF / Remote | Full-time Outerbounds was founded recently to commercialize Metaflow, an open-source Python framework that makes infrastructure easily accessible for machine learning/data science project
43.
Simple Production ML with Metaflow and Seldon(seldon.io)
4 points by vtuulos 5y ago|0 comments
44.
by vtuulos 5y ago
As the article mentions, Metaflow will start supporting Kubernetes natively soon, although data scientists don't need to care about it :) Nothing changes in your Metaflow code when you move e.g. from AWS to Azure, so Metaflow isn'
45.
Data scientists shouldn’t need to know Kubernetes(huyenchip.com)
181 points by vtuulos 5y ago|112 comments
46.
Data scientists shouldn’t need to know Kubernetes(huyenchip.com)
5 points by vtuulos 5y ago|0 comments
47.
Improving Data Science Processes to Speed Innovation(medium.com)
5 points by vtuulos 5y ago|0 comments
48.
by vtuulos 5y ago
Outerbounds | Frontend dev | SF / Remote | Full-time Outerbounds was founded recently to commercialize Metaflow, an open-source Python framework that makes infrastructure easily accessible for machine learning/data science project
49.
by vtuulos 5y ago
Outerbounds | Frontend dev, Systems dev, ML/AI Specialist | SF / Remote | Full-time Outerbounds was founded recently to commercialize Metaflow, an open-source Python framework that makes infrastructure easily accessible for machin
50.
by vtuulos 5y ago
This is very cool! If you are interested in using a similar setup for your ML workloads (Argo + K8S), I'd love to chat with you (ville@outerbounds.co). We are working on integrating Argo & K8S for Metaflow, an open-source ML framew
51.
by vtuulos 5y ago
I love the idea! The Journey team has a great founder-market fit too :) I am looking forward to using the product by myself. Congrats for the launch!
52.
by vtuulos 5y ago
Just my experience from Netflix tells that a large company can have tens of use cases for optimization of different kinds, ML included. Often it is the least hype-worthy cases that are the most valuable for the business (e.g churn predictio
53.
by vtuulos 5y ago
Interesting, thanks for sharing CNN! (Disclaimer: I hack Metaflow for living) Do other folks find Terraform more appealing than Cloudformation in general?
54.
by vtuulos 6y ago
The topic is close to my heart so maybe one day :)
55.
by vtuulos 6y ago
re: separate process - fault-tolerance is a key requirement. There are a myriad of ways how a highly parallelized, network- and data-intensive code can fail, so isolating it in a separate process is a safer approach than trying to try-excep
56.
by vtuulos 6y ago
It is a bit of a hidden gem but Metaflow includes a Boto-based highly parallelized, error-tolerant S3 client that Netflix uses routinely to get 10-20Gbps throughput between EC2 and S3. Technically it is independent from Metaflow, so you cou
57.
by vtuulos 6y ago
Coming soon!
58.
by vtuulos 6y ago
As of today, Metaflow supports AWS Batch for scaling out and up. While supporting K8S is convenient in the operations point of view (assuming you have a K8S cluster already), it doesn't make the scalability story any better.
59.
by vtuulos 6y ago
Reproducibility is a spectrum. A good starting point is to snapshot the exact version of the code that produced a model. Even better, you should snapshot the full dependency graph, including transitive dependencies, of all (compiled) librar
60.
by vtuulos 6y ago
No plans but it should be possible technically. We'd need help from the Julia community :)

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