Software Alternatives, Accelerators & Startups

Amazon Machine Learning VS bundlejs

Compare Amazon Machine Learning VS bundlejs and see what are their differences

Amazon Machine Learning logo Amazon Machine Learning

Machine learning made easy for developers of any skill level

bundlejs logo bundlejs

A quick and easy way to bundle, minify, and compress (gzip and brotli) your ts, js, jsx and npm projects all online, with the bundle file size.
  • Amazon Machine Learning Landing page
    Landing page //
    2023-03-13
  • bundlejs Landing page
    Landing page //
    2022-04-23

bundle is a quick and easy way to bundle your projects, minify and see it's gzip size. It's an online tool similar to bundlephobia, but bundle does all the bundling locally on you browser and can treeshake and bundle multiple packages (both commonjs and esm) together, all without having to install any npm packages and with typescript support.

If there is something I missed, a mistake, or a feature you would like added please create an issue or a pull request and I'll try to get to it. You can contribute to this project at okikio/bundle.

You can join the discussion on Github discussions or Twitter.

You can now use search queries in bundle, all you need to do is add this to the url
?q={packages}&treeshake={methods to treeshake}

e.g.
You want react, react-dom, vue, and @okikio/animate, but only want the Animate and toStr methods exported from @okikio/animate.

You would add this to the url bundlejs.com/?q=react,react-dom,vue,@okikio/animate&treeshake=[*],[*],[*],[{Animate,toStr}]

bundlejs

$ Details
free
Platforms
Web Google Chrome Firefox Safari JavaScript Edge
Release Date
2021 May

Amazon Machine Learning features and specs

  • Scalability
    Amazon Machine Learning can handle increased workloads easily without significant changes in the infrastructure, making it ideal for growing businesses.
  • Integration with AWS
    Seamlessly integrates with other AWS services like S3, EC2, and Lambda, simplifying data storage, processing, and deployment.
  • Ease of Use
    User-friendly AWS Management Console and APIs make it easier for developers to build, train, and deploy machine learning models without needing deep ML expertise.
  • Performance
    Offers high-performance computing capabilities that can accelerate the training and inference processes for machine learning models.
  • Cost-Effective
    Pay-as-you-go pricing model ensures that you only pay for what you use, making it a cost-effective solution for various ML needs.
  • Prebuilt AI Services
    Provides prebuilt, ready-to-use AI services like Amazon Rekognition, Amazon Comprehend, and Amazon Polly, which simplify the implementation of complex ML solutions.

Possible disadvantages of Amazon Machine Learning

  • Complexity
    While the service is designed to be user-friendly, the underlying complexity of Machine Learning algorithms and models can be a barrier for novice users.
  • Vendor Lock-In
    Using Amazon Machine Learning extensively may lead to dependency on AWS services, making it difficult to switch providers or integrate with non-AWS services in the future.
  • Cost Management
    Although pay-as-you-go is cost-effective, if not managed properly, costs can quickly escalate especially with extensive use and large-scale data processing.
  • Limited Customization
    Prebuilt models and services may lack the level of customization needed for highly specialized use-cases requiring unique algorithms or configurations.
  • Data Privacy
    Storing and processing sensitive data on an external service may raise concerns regarding data privacy and compliance with data protection regulations.
  • Learning Curve
    Despite its ease of use, there is still a learning curve associated with mastering the AWS ecosystem and effectively utilizing its machine learning capabilities.

bundlejs features and specs

  • brotli
  • gzip
  • lz4
  • npm
  • deno
  • Configurable
  • jsx
  • TypeScript
  • Offline
  • Error and warning alerting
  • Open-source

Analysis of Amazon Machine Learning

Overall verdict

  • Amazon Machine Learning is a good fit for businesses that need a reliable cloud-based machine learning platform, especially those already utilizing AWS services. Its scalability and integration capabilities make it suitable for a wide range of machine learning tasks.

Why this product is good

  • Amazon Machine Learning offers scalable solutions integrated with AWS services, making it a strong choice for users already within the AWS ecosystem. Its tools are built to handle large datasets and provide robust infrastructure, contributing to ease of deployment and management. Additionally, the service enables developers and data scientists to build sophisticated models without requiring deep machine learning expertise.

Recommended for

  • Developers and data scientists seeking seamless integration with AWS cloud services.
  • Organizations handling large-scale data analyses and machine learning projects.
  • Enterprises that prioritize scalability and flexibility in their machine learning operations.
  • Teams looking for a platform that supports both novice and expert users with varying levels of machine learning expertise.

Amazon Machine Learning videos

Introduction to Amazon Machine Learning - Predictive Analytics on AWS

More videos:

  • Tutorial - AWS Machine Learning Tutorial | Amazon Machine Learning | AWS Training | Edureka

bundlejs videos

No bundlejs videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Amazon Machine Learning and bundlejs)
AI
100 100%
0% 0
Developer Tools
82 82%
18% 18
Web Application Bundler
0 0%
100% 100
Data Science And Machine Learning

User comments

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Social recommendations and mentions

Based on our record, bundlejs should be more popular than Amazon Machine Learning. It has been mentiond 8 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.

Amazon Machine Learning mentions (2)

  • Rant + Planning to learn full stack development
    There’s also the ML as a service (MLaaS) movement that lowers the barrier for common ML capabilities (eg image object detection and audio transcription). Basically, you use APIs. See: https://aws.amazon.com/machine-learning/. Source: almost 3 years ago
  • Ask the Experts: AWS Data Science and ML Experts - Mar 9th @ 8AM ET / 1PM GMT!
    Do you have questions about Data Science and ML on AWS - https://aws.amazon.com/machine-learning/. Source: over 4 years ago

bundlejs mentions (8)

  • Zod 4
    These numbers don't reflect anything useful. This is the total size of the code in the package, most of which will be tree-shaken. In Zod's case, the package now contains three independent sub-libraries. I recommend plugging a script into bundlejs.com[0] to see bundle size numbers for a particular script [0] https://bundlejs.com. - Source: Hacker News / 23 days ago
  • PackagePhobia – Find the cost of adding a new dev dependency to your project
    [bundlejs](https://bundlejs.com/) is the better alternative to check your dependency sizes with. - Source: Hacker News / 3 months ago
  • ESM & CJS: The subtle shift in bundlejs' behaviour
    I was closing out some long lived issues over on bundlejs, when issue #50 reminded me of the ongoing debate about how bundlejs should handle the ESM and CJS packages. - Source: dev.to / almost 2 years ago
  • TANStack Query
    Still, I'm not really sure about its dependencies: it lists react and @tanstack/react-query (as opposed to @tanstack/query-core) and bundlejs reports 124KB gzipped. Also, while using it, you still need to refer to their react docs (that documentation is really good and has a lot of examples) but not everyone will be thrilled about checking a react documentation when they're using an angular package. Source: almost 2 years ago
  • Jest not recommended to be used in Node.js due to instanceOf operator issues
    It's somewhere in between. React as a lib and architecture _is_ platform-agnostic. The core logic is defined in the `react-reconciler` package. It contains all the implementation of rendering components, diffing trees, managing state, and running effects, as well as all the "Suspense" implementation. However, the way `react-reconciler` works is that it's built _into_ each platform-specific renderer... - Source: Hacker News / almost 2 years ago
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What are some alternatives?

When comparing Amazon Machine Learning and bundlejs, you can also consider the following products

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

BundlePhobia - Find the performance impact of adding a npm package to your bundle.

Apple Machine Learning Journal - A blog written by Apple engineers

Webpack - Webpack is a module bundler. Its main purpose is to bundle JavaScript files for usage in a browser, yet it is also capable of transforming, bundling, or packaging just about any resource or asset.

Lobe - Visual tool for building custom deep learning models

esbuild - An extremely fast JavaScript bundler and minifier