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Apple Machine Learning Journal VS Dokku

Compare Apple Machine Learning Journal VS Dokku and see what are their differences

Apple Machine Learning Journal logo Apple Machine Learning Journal

A blog written by Apple engineers

Dokku logo Dokku

Docker powered mini-Heroku in around 100 lines of Bash
  • Apple Machine Learning Journal Landing page
    Landing page //
    2022-12-13
  • Dokku Landing page
    Landing page //
    2023-07-24

Apple Machine Learning Journal videos

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Dokku videos

00028 Creating Your Own PaaS with Dokku

Category Popularity

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AI
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Cloud Computing
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Developer Tools
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Reviews

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Dokku Reviews

35+ Of The Best CI/CD Tools: Organized By Category
Dokku is a great alternative if you’re working with a stringent budget. It’s a miniaturized self-hosted platform as a service. You can deploy applications to it using Git. Because it’s a Heroku derivative, it’s compatible with Heroku apps.
Heroku vs self-hosted PaaS
CapRover is in many ways similar to Dokku. It uses Docker for deployment just like Dokku but CapRover does not support buildpack deployments as it uses Dockerfiles only. This is not necessarily a bad thing since Dockerfile deployments are great in Dokku as well. You don’t have to write your own dockerfiles however for simple deployments as there are multiple defaults for...
Source: www.mskog.com

Social recommendations and mentions

Based on our record, Dokku should be more popular than Apple Machine Learning Journal. It has been mentiond 12 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apple Machine Learning Journal mentions (6)

  • Does anyone else suspect that the official iOS ChatGPT app might be conducting some local inference / edge-computing? [Discussion]
    For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: about 1 year ago
  • Which papers should I implement or which Projects should I do to get an entry level job as a Computer vision engineer at MAANG ?
    We even host annual poster sessions of those PhD intern’s work while at our company, and it’ll give you an idea of the caliber of work. It may not be as great as Nvidia, Stryker, Waymo, or Tesla (which are not part of MAANG but I believe are far more ahead in CV), but it’s worth of considering. Source: about 1 year ago
  • Apple’s secrecy created engineer burnout
    They have something for ML: https://machinelearning.apple.com. - Source: Hacker News / about 2 years ago
  • [D] Is anyone working on open-sourcing Dall-E 2?
    They're more subtle about it, I think. https://machinelearning.apple.com/ Some of the papers are pretty good. I don't disagree with your sentiment in aggregate, though. Source: about 2 years ago
  • How does Apple achieve both secrecy and quality for a release?
    Siri is not where it needs to be because Apple refuses to mine user data to enrich it. They also are very hesitant to allow researchers to publish their breakthroughs which makes recruitment very hard. Although this is changing https://machinelearning.apple.com/. - Source: Hacker News / about 2 years ago
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Dokku mentions (12)

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What are some alternatives?

When comparing Apple Machine Learning Journal and Dokku, you can also consider the following products

Amazon Machine Learning - Machine learning made easy for developers of any skill level

Google App Engine - A powerful platform to build web and mobile apps that scale automatically.

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

Salesforce Platform - Salesforce Platform is a comprehensive PaaS solution that paves the way for the developers to test, build, and mitigate the issues in the cloud application before the final deployment.

Lobe - Visual tool for building custom deep learning models

Google Cloud Functions - A serverless platform for building event-based microservices.