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Apple Machine Learning Journal VS Float

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

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Apple Machine Learning Journal logo Apple Machine Learning Journal

A blog written by Apple engineers

Float logo Float

The leading resource management software for agencies, studios, and firms. With a simple, drag and drop interface and powerful editing tools, Float saves you time and keeps projects on track.
  • Apple Machine Learning Journal Landing page
    Landing page //
    2022-12-13
  • Float Landing page
    Landing page //
    2023-02-11

Float is the world's leading resource management software for agencies, studios, and firms. Since 2012, Float has been helping the world’s best teams including RGA, VICE, Deloitte, and Buzzfeed schedule and deliver over 5.5million tasks, in more than 150 countries.

With an easy to use, intuitive interface, drag and drop features, and powerful editing tools, Float makes planning your projects and scheduling your team's time visual and simple. Search your schedule for practically anything and track your team's utilization with powerful reporting tools. Forecast your budget spend and plan ahead based on your team's real capacity and resources.

Integrate your schedule with Slack, Google Calendar and 1,000+ of your apps via Zapier. Access and update your Float schedule from anywhere with apps for iOS and Android.

By providing a single view of your real resource capacity and a shared calendar of who's working on what, Float makes team scheduling across multiple projects faster, easier and more efficient.

Apple Machine Learning Journal

Website
machinelearning.apple.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-
Edit details

Float

Website
float.com
Pricing URL
Official Float Pricing
$ Details
$5.0 / Monthly ($5/person scheduled/month)
Platforms
Browser iOS Android
Release Date
2012 February
Edit details

Apple Machine Learning Journal features and specs

  • Expert Insight
    The journal provides in-depth insights from Apple's own machine learning experts, offering unique and valuable perspectives on the latest research and applications in the field.
  • Practical Applications
    The content often focuses on real-world applications and implementations of machine learning within Apple's ecosystem, making it highly relevant for practitioners.
  • High-Quality Content
    The articles in the journal are meticulously reviewed and curated, ensuring high-quality and reliable information.
  • Cutting-Edge Research
    Readers get early access to cutting-edge research and innovations directly from Apple's R&D teams.
  • Free Access
    The journal is freely accessible to the public, removing barriers for anyone interested in learning from industry leaders.

Possible disadvantages of Apple Machine Learning Journal

  • Apple-Centric
    The focus is predominantly on Apple's ecosystem, which may limit the applicability of some insights and solutions for those working with other platforms.
  • Infrequent Updates
    The journal does not publish new content as frequently as some other machine learning blogs or journals, potentially limiting its usefulness for staying up-to-date with the latest in the field.
  • Technical Depth
    While the technical rigor is generally high, this can make the content less accessible to beginners or those without a strong background in machine learning.
  • Limited Interactivity
    The journal primarily provides static articles and lacks interactive elements or community features such as forums or comment sections for reader engagement.
  • Bias Towards Proprietary Solutions
    The solutions and approaches advocated often align closely with Apple's proprietary technologies, which may not always be applicable or optimal for all contexts and use cases.

Float features and specs

  • User-Friendly Interface
    Float offers an intuitive and easy-to-navigate interface, making it accessible for users of all skill levels.
  • Collaboration Tools
    Float provides robust collaboration features, including real-time updates and team communication capabilities, which enhance team coordination.
  • Resource Management
    The platform excels at resource management, allowing for efficient allocation and tracking of team members and project resources.
  • Integrations
    Float integrates with popular tools like Slack, Trello, and Asana, streamlining workflows and improving productivity.
  • Mobile Accessibility
    With mobile accessibility, users can manage schedules and resources on-the-go, adding flexibility to their project management.

Possible disadvantages of Float

  • Cost
    Float may be considered expensive for small businesses or startups due to its subscription pricing model.
  • Limited Customization
    Users may find limitations in terms of customization options for specific needs or preferences.
  • Learning Curve
    Despite its user-friendly design, there may still be a learning curve for users who are new to project management tools.
  • Performance Issues
    Some users report occasional performance issues, such as slow loading times or lag, particularly with larger projects.
  • Reporting Features
    While adequate for basic needs, the reporting and analytics features may not be as advanced as some competitors.

Analysis of Apple Machine Learning Journal

Overall verdict

  • Yes, the Apple Machine Learning Journal is considered a valuable resource for those interested in applied machine learning, particularly in the context of consumer technology. The content is generally well-regarded for its quality and relevance to ongoing developments in the field.

Why this product is good

  • The Apple Machine Learning Journal offers insights into the cutting-edge machine learning advancements and applications at Apple. It features articles and research papers from Apple's machine learning teams, showcasing practical implementations in real-world products. This makes it an excellent resource for understanding how theoretical ML concepts are applied in industry settings.

Recommended for

  • Machine learning practitioners looking for industry applications of ML
  • Data scientists interested in Apple's ML innovations
  • Researchers seeking inspiration for practical ML implementations
  • Students learning about real-world applications of machine learning

Analysis of Float

Overall verdict

  • Float is generally well-regarded and is a strong choice for teams looking for a reliable resource management solution. While it may not suit all businesses, particularly those seeking a broader project management suite, it excels in its niche.

Why this product is good

  • Float is considered good due to its user-friendly interface, robust features, and seamless integrations with various project management tools. It allows teams to efficiently plan, schedule, and track resources, ensuring optimal utilization and project efficiency.

Recommended for

  • Project managers who need to allocate resources quickly and effectively.
  • Small to medium-sized businesses looking for a straightforward resource management tool.
  • Teams that integrate with other popular project management software and need complementary resource scheduling.

Apple Machine Learning Journal videos

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Float videos

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Category Popularity

0-100% (relative to Apple Machine Learning Journal and Float)

Apple Machine Learning Journal

Float

AI
100 100%
AI
0% 0
Resource Scheduling
0 0%
Resource Scheduling
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Employee Scheduling
0 0%
Employee Scheduling
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apple Machine Learning Journal should be more popular than Float. It has been mentiond 7 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 (7)

  • Apple Intelligence Foundation Language Models
    Https://machinelearning.apple.com Fun fact: Their first paper, Improving the Realism of Synthetic Images (2017; https://machinelearning.apple.com/research/gan), strongly hints at eye and hand tracking for the Apple Vision Pro released 5 years later. - Source: Hacker News / 10 months ago
  • 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 2 years 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 2 years ago
  • Apple’s secrecy created engineer burnout
    They have something for ML: https://machinelearning.apple.com. - Source: Hacker News / about 3 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 3 years ago
View more

Float mentions (2)

  • 2022 Accounting/time billing ideas for SW dev consulting? On my second, not really happy
    You wouldn't want something like NetSuite just for time entry. Try float.com, one of my clients uses this and it seems to be work and is simple. Source: about 3 years ago
  • Project/Team Management software/platform assistance needed
    Schedule more than one task to a team member per day i.e. Hours per task per day - float.com and avasa.com allows this. Source: over 3 years ago

What are some alternatives?

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

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

ResourceGuru - The fast, simple way to schedule people, equipment, and other resources online.

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

When I Work - When I Work is an employee scheduling and communication app using the web, mobile apps, text messaging, social media, and email.

Lobe - Visual tool for building custom deep learning models

Ganttic - Ganttic is a flexible resource management platform for scheduling teams, equipment, vehicles and multiple projects simultaneously. Save time, eliminate double bookings, and increase efficiency.

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