Software Alternatives & Reviews

Apple Machine Learning Journal VS AETROS

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

Apple Machine Learning Journal logo Apple Machine Learning Journal

A blog written by Apple engineers

AETROS logo AETROS

Create, train and monitor deep neural networks
  • Apple Machine Learning Journal Landing page
    Landing page //
    2022-12-13
  • AETROS Landing page
    Landing page //
    2023-07-18

Category Popularity

0-100% (relative to Apple Machine Learning Journal and AETROS)
AI
76 76%
24% 24
Developer Tools
83 83%
17% 17
Games
0 0%
100% 100
Data Science And Machine Learning

User comments

Share your experience with using Apple Machine Learning Journal and AETROS. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Apple Machine Learning Journal should be more popular than AETROS. It has been mentiond 6 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: 12 months 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 / almost 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
View more

AETROS mentions (1)

  • Introducing Deepkit ORM, a high performance ORM for TypeScript
    Deepkit ORM is one of a whole collection of high performance libraries written in the last years for my need in developing complex isomorphic TypeScript applications (like for example https://deepkit.ai). Since we approach the beta version I'd like to introduce you to one of its flagship libraries, the ORM, and collect feedback. So, if you are interested, please keep reading and drop me a comment about your thoughts! Source: almost 3 years ago

What are some alternatives?

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

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

Colornet - Neural Network to colorize grayscale images

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

Quick Draw Game - Can a neural network learn to recognize doodles?

Lobe - Visual tool for building custom deep learning models

TensorFlow Lite - Low-latency inference of on-device ML models