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Iterative.ai VS Apple Machine Learning Journal

Compare Iterative.ai VS Apple Machine Learning Journal and see what are their differences

Iterative.ai logo Iterative.ai

Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.

Apple Machine Learning Journal logo Apple Machine Learning Journal

A blog written by Apple engineers
  • Iterative.ai Landing page
    Landing page //
    2023-08-18
  • Apple Machine Learning Journal Landing page
    Landing page //
    2022-12-13

Iterative.ai videos

Reimagining DevOps for ML by Elle O'Brien, Iterative.ai

Apple Machine Learning Journal videos

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Category Popularity

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Data Science And Machine Learning
AI
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Data Science Notebooks
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Developer Tools
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User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Iterative.ai and Apple Machine Learning Journal

Iterative.ai Reviews

  1. Rare

    The people at iterative.ai are special.

    Its hard to describe quickly, but if you're a potential client or employee--you could easily go your entire career unaware that groups like this exist.

    Their tools (like DVC) are exceptional, but I write this review because one need only interact with the people there to understand why they're execptional.

    The culture there is one that can only exist when the founding talent is top-tier. The experience you'll have, though, is so much more than that.

    Recommend whole-heatedly.

    👍 Pros:    Constantly improving|Quality|Community

Apple Machine Learning Journal Reviews

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Social recommendations and mentions

Apple Machine Learning Journal might be a bit more popular than Iterative.ai. We know about 6 links to it since March 2021 and only 6 links to Iterative.ai. 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.

Iterative.ai mentions (6)

  • Work with Google Drive files locally
    PyDrive2 is am open-source python package maintained by the awesome people at Iterative. And it is very easy to install:. - Source: dev.to / over 1 year ago
  • Any MLOps platform you use?
    These three are made by Iterative.ai, and seem like very clean implementations of MLOps tooling - especially if you aren't dealing with massive data. https://iterative.ai/. Source: over 1 year ago
  • How does your data science team collaborate?
    For what it's worth. (Full disclosure: I'm the community manager at Iterative (DVC,et.al.) Just wanted to make you aware of our online course (free) that we created specifically for Data Scientists (https://learn.iterative.ai). We know that bridging the gap between prototype to production/ jupyter notebook to reproducible/software engineering compatible, is a challenge. That's why we created the course. To also... Source: almost 2 years ago
  • Advice about Infra and IaC
    What do you think of iterative.ai tools like dvc or cml? I have no direct experience, but I am looking at setting up something similar to what you need for a personal project. Source: almost 2 years ago
  • TPI - Terraform provider for ML/AI & self-recovering spot-instances
    Hey all, we (at iterative.ai) are launching TPI - Terraform Provider Iterative https://github.com/iterative/terraform-provider-iterative. Source: about 2 years ago
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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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What are some alternatives?

When comparing Iterative.ai and Apple Machine Learning Journal, you can also consider the following products

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

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

Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

MCenter - Machine Learning Operationalization

Lobe - Visual tool for building custom deep learning models