dwhitena

7 ms·

1.
Run Hugging Face Models in Go(github.com)
2 points by dwhitena 5y ago|0 comments
2.
Translating “Wash your hands” into 500 languages(datadan.io)
3 points by dwhitena 7y ago|0 comments
3.
Behavioral economics and AI-driven decision making(changelog.com)
2 points by dwhitena 8y ago|0 comments
4.
Scalable ML/AI on k8s with new Pachyderm release(medium.com)
3 points by dwhitena 9y ago|0 comments
5.
GPU-accelerated TensorFlow on Kubernetes(oreilly.com)
2 points by dwhitena 9y ago|0 comments
6.
Shell scripts for big genomic data(medium.com)
1 points by dwhitena 9y ago|0 comments
7.
Scaling bash commands over large data sets in genomics(medium.com)
1 points by dwhitena 9y ago|0 comments
8.
Using Go for Bioinformatics workflows(gopherdata.io)
1 points by dwhitena 9y ago|0 comments
9.
Shell scripts at scale, Bioinformatics and Pachyderm(medium.com)
3 points by dwhitena 9y ago|0 comments
10.
Bringing cron workflows to distributed systems(medium.com)
1 points by dwhitena 9y ago|1 comments
11.
by dwhitena 9y ago
Thanks! It was a ton of fun to create the example.
12.
Access controls and versioning for data – Pachyderm(medium.com)
2 points by dwhitena 9y ago|0 comments
13.
New release of k8s-based ML/AI platform Pachyderm(medium.com)
1 points by dwhitena 9y ago|0 comments
14.
“Machine Learning with Go” Released(amazon.com)
1 points by dwhitena 9y ago|0 comments
15.
ML/AI on Kubernetes – New Pachyderm Release(medium.com)
4 points by dwhitena 9y ago|0 comments
16.
Machine Learning with Go – Just Released(packtpub.com)
1 points by dwhitena 9y ago|0 comments
17.
Gonum – Numerical Computing for Go(gonum.org)
140 points by dwhitena 9y ago|45 comments
18.
Reproducible Data Science Built on Containers(pachyderm.io)
2 points by dwhitena 9y ago|0 comments
19.
Reproducible Data Science – Announcing Pachyderm Enterprise(pachyderm.io)
1 points by dwhitena 9y ago|0 comments
20.
by dwhitena 9y ago
Great questions and discussions. I'm definitely passionate about versioning in the context of models and data science for both data and code. I work full time on the open source Pachyderm project (pachyderm.io), and we have users ver
21.
GPU Accelerated Data Pipelines(medium.com)
4 points by dwhitena 9y ago|0 comments
22.
Unified ML pipelines and data management(medium.com)
2 points by dwhitena 9y ago|0 comments
23.
Autoscaling, containerized ML pipelines(medium.com)
1 points by dwhitena 9y ago|0 comments
24.
GPU Powered Pipelines Built on Containers(medium.com)
7 points by dwhitena 9y ago|0 comments
25.
by dwhitena 9y ago
Thanks for sharing this story. My experience has been that data scientists and analysts aren't able to efficiently use Hadoop/Spark even in cases where it is warranted. These individuals don't generally like working with Ja
26.
by dwhitena 9y ago
I will also throw in my (biased, as I work on the project) suggestion to take a look at Pachyderm ( http://pachyderm.io/ ). It is open source, language agnostic, and distributed. Plus it automatically tracks the provenance
27.
by dwhitena 9y ago
Reproducibility and provenance are essential for the data science workflows. And it's important to maintain these at scale. Take a look at Pachyderm some time as well if you get a chance (I work on the project for full disclosure).
28.
From cats to maps, a reusable image translation pipeline(blog.ycombinator.com)
2 points by dwhitena 9y ago|0 comments
29.
Reusable ML pipeline to manage image generation(blog.ycombinator.com)
3 points by dwhitena 9y ago|0 comments
30.
From edge2cat to edge2anything with TensorFlow(blog.ycombinator.com)
6 points by dwhitena 9y ago|0 comments

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