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Neptune.ai

Neptune.ai

Neptune.ai

Leading company in the artificial intelligence industry

Machine Learning

Data Science

Model Management

Experiment Tracking

MLOps

San Francisco, California

51-200

2018年成立(第8年)

远程

Private

融资总额

$52M

最近融资

Series B

开放职位

3个职位

Technical Product Manager

Product

Remote in Europe

On-site

Lead

Full-time

Remote Work

弹性工作

股权

查看详情

1个月前

Principal Solutions Architect

Solutions Architect

Canada

On-site

Staff

Contract

Remote Work

弹性工作

股权

无限假期

查看详情

1个月前

Principal Solutions Architect

Solutions Architect

USA

On-site

Staff

Contract

Remote Work

弹性工作

股权

无限假期

查看详情

1个月前

产品与服务

Neptune Experiment Tracking

Neptune Experiment Tracking

MLOps

NE

Neptune Experiment Management

Experiment Tracking

Neptune Model Registry

Neptune Model Registry

MLOps

NE

Neptune Model Registry

Model Management

Neptune Metadata Store

Neptune Metadata Store

Data Management

NE

Neptune Metadata Store

Metadata Management

Neptune Monitoring

Neptune Monitoring

ML Monitoring

NE

Neptune Notebooks

Notebook Management

Neptune Notebooks

Neptune Notebooks

Development Tools

NE

Neptune Dashboard

Visualization

Neptune API & SDK

Neptune API & SDK

Integration

NE

Neptune API & Integrations

Integration

NE

Neptune Teams & Collaboration

Collaboration

Neptune Dashboard

Neptune Dashboard

Visualization

NE

Neptune Monitoring

Monitoring

Neptune Integrations

Neptune Integrations

Integration

新闻动态

All

Reddit

X

News

HN

LinkedIn

YouTube

Are Book LLMs actually worth the ethical headache? Looking at alternatives

Been thinking about this a lot lately after reading about all the copyright disputes going on between publishers and AI companies. The whole "Book LLMs" situation feels like it's getting messier by the month, and I'm genuinely not sure the tradeoff is worth it anymore. Like, the bias risk from skewed source material combined with the legal exposure and the compensation debates. it just seems like there are cleaner ways to build these things. Synthetic data and open licensed datasets aren't perfe

Reddit

·

1个月前

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1

·

1

[P] We made GoodSeed, a pleasant ML experiment tracker

# GoodSeed v0.3.0 🎉 I and my friend are pleased to announce **GoodSeed** \- a ML experiment tracker which we are now using as a replacement for Neptune. # Key Features * **Simple and fast**: Beautiful, clean UI * **Metric plots:** Zoom-based downsampling, smoothing, relative time x axis, fullscreen mode, ... * **Monitoring plots**: GPU/CPU usage (both NVIDIA and AMD), memory consumption, GPU power usage * **Stdout/Stderr monitoring**: View your program's output online. * **Structured Configs

Reddit

·

1个月前

·

83

·

19

What Neptune.ai Got Right (and How to Keep It)

HN

·

2个月前

·

2

Show HN: Pluto – open-source Experiment Tracker for Neptune users

Hey HN! We&#x27;re Roanak and Andrew from Trainy (YC S23). We build GPU infrastructure for ML teams (scheduling multi-node training jobs on Kubernetes). When Neptune announced they&#x27;re shutting down, our customers didn&#x27;t have a clear path forward. The alternatives exist but none of them matched the UI experience Neptune had, especially at scale. So we decided to build Pluto.<p>Pluto is an open-source experiment tracker based on our fork of MLOp. The main idea is that you can add one imp

HN

·

2个月前

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2

Setea de bani la buget reînvie proiecte vechi. Statul vrea, din nou, să intre pe piața apei îmbuteliate. Noul brand național - Știrile ProTV

Statul român a reluat un proiect mai vechi de a a-și face propriul brand de apă minerală, care se va numi Dacia. Societatea care administrează 30% din izvoarele țării are în plan deschiderea unei fabrici în Deva, cel puțin pentru început.

Reddit

·

2个月前

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11

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30

更多 (剩余15条)

总浏览量

2

申请点击数

0

模拟申请者数

0

收藏

0

评价

3.6

10条评价

工作生活平衡

3.8

薪酬

2.5

企业文化

3.2

职业发展

2.8

管理层

2.3

65%

推荐给朋友

优点

Flexible working hours

Good team culture and collaboration

Comprehensive benefits

缺点

Below average compensation

Limited career advancement

Poor management communication

薪资范围

0个数据点

Intern

Intern

0份报告

$107,063

年薪总额

基本工资

-

股票

-

$91,035

$123,090

面试经验

36次面试

难度

4.0

/ 5

时长

3-6w

录用率

23%

体验

正面 65%

中性 20%

负面 15%

面试流程

1

Recruiter Screen

2

ML Coding

3

ML System Design

4

Research Discussion

5

Team Interviews

联系方式与地址