Alternatives to ML Kit logo

Alternatives to ML Kit

Tensorflow Lite, TensorFlow, Postman, Postman, and Stack Overflow are the most popular alternatives and competitors to ML Kit.
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209
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What is ML Kit and what are its top alternatives?

ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
ML Kit is a tool in the Machine Learning Tools category of a tech stack.

Top Alternatives to ML Kit

  • Tensorflow Lite
    Tensorflow Lite

    It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size. ...

  • TensorFlow
    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • Postman
    Postman

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...

  • Postman
    Postman

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...

  • Stack Overflow
    Stack Overflow

    Stack Overflow is a question and answer site for professional and enthusiast programmers. It's built and run by you as part of the Stack Exchange network of Q&A sites. With your help, we're working together to build a library of detailed answers to every question about programming. ...

  • Google Maps
    Google Maps

    Create rich applications and stunning visualisations of your data, leveraging the comprehensiveness, accuracy, and usability of Google Maps and a modern web platform that scales as you grow. ...

  • Elasticsearch
    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

  • GitHub Pages
    GitHub Pages

    Public webpages hosted directly from your GitHub repository. Just edit, push, and your changes are live. ...

ML Kit alternatives & related posts

Tensorflow Lite logo

Tensorflow Lite

75
1
Deploy machine learning models on mobile and IoT devices
75
1
PROS OF TENSORFLOW LITE
  • 1
    .tflite conversion
CONS OF TENSORFLOW LITE
    Be the first to leave a con

    related Tensorflow Lite posts

    TensorFlow logo

    TensorFlow

    3.8K
    106
    Open Source Software Library for Machine Intelligence
    3.8K
    106
    PROS OF TENSORFLOW
    • 32
      High Performance
    • 19
      Connect Research and Production
    • 16
      Deep Flexibility
    • 12
      Auto-Differentiation
    • 11
      True Portability
    • 6
      Easy to use
    • 5
      High level abstraction
    • 5
      Powerful
    CONS OF TENSORFLOW
    • 9
      Hard
    • 6
      Hard to debug
    • 2
      Documentation not very helpful

    related TensorFlow posts

    Tom Klein

    Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

    See more
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 2.8M views

    Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

    At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

    TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

    Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

    https://eng.uber.com/horovod/

    (Direct GitHub repo: https://github.com/uber/horovod)

    See more
    Postman logo

    Postman

    94.7K
    1.8K
    Only complete API development environment
    94.7K
    1.8K
    PROS OF POSTMAN
    • 490
      Easy to use
    • 369
      Great tool
    • 276
      Makes developing rest api's easy peasy
    • 156
      Easy setup, looks good
    • 144
      The best api workflow out there
    • 53
      It's the best
    • 53
      History feature
    • 44
      Adds real value to my workflow
    • 43
      Great interface that magically predicts your needs
    • 35
      The best in class app
    • 12
      Can save and share script
    • 10
      Fully featured without looking cluttered
    • 8
      Collections
    • 8
      Option to run scrips
    • 8
      Global/Environment Variables
    • 7
      Shareable Collections
    • 7
      Dead simple and useful. Excellent
    • 7
      Dark theme easy on the eyes
    • 6
      Awesome customer support
    • 6
      Great integration with newman
    • 5
      Documentation
    • 5
      Simple
    • 5
      The test script is useful
    • 4
      Saves responses
    • 4
      This has simplified my testing significantly
    • 4
      Makes testing API's as easy as 1,2,3
    • 4
      Easy as pie
    • 3
      API-network
    • 3
      I'd recommend it to everyone who works with apis
    • 3
      Mocking API calls with predefined response
    • 2
      Now supports GraphQL
    • 2
      Postman Runner CI Integration
    • 2
      Easy to setup, test and provides test storage
    • 2
      Continuous integration using newman
    • 2
      Pre-request Script and Test attributes are invaluable
    • 2
      Runner
    • 2
      Graph
    • 1
      <a href="http://fixbit.com/">useful tool</a>
    CONS OF POSTMAN
    • 10
      Stores credentials in HTTP
    • 9
      Bloated features and UI
    • 8
      Cumbersome to switch authentication tokens
    • 7
      Poor GraphQL support
    • 5
      Expensive
    • 3
      Not free after 5 users
    • 3
      Can't prompt for per-request variables
    • 1
      Import swagger
    • 1
      Support websocket
    • 1
      Import curl

    related Postman posts

    Noah Zoschke
    Engineering Manager at Segment · | 30 upvotes · 3M views

    We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.

    Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

    Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.

    This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

    Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct

    Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.

    Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.

    See more
    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 27 upvotes · 5.2M views

    Our whole Node.js backend stack consists of the following tools:

    • Lerna as a tool for multi package and multi repository management
    • npm as package manager
    • NestJS as Node.js framework
    • TypeScript as programming language
    • ExpressJS as web server
    • Swagger UI for visualizing and interacting with the API’s resources
    • Postman as a tool for API development
    • TypeORM as object relational mapping layer
    • JSON Web Token for access token management

    The main reason we have chosen Node.js over PHP is related to the following artifacts:

    • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
    • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
    • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
    • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
    See more
    Postman logo

    Postman

    94.7K
    1.8K
    Only complete API development environment
    94.7K
    1.8K
    PROS OF POSTMAN
    • 490
      Easy to use
    • 369
      Great tool
    • 276
      Makes developing rest api's easy peasy
    • 156
      Easy setup, looks good
    • 144
      The best api workflow out there
    • 53
      It's the best
    • 53
      History feature
    • 44
      Adds real value to my workflow
    • 43
      Great interface that magically predicts your needs
    • 35
      The best in class app
    • 12
      Can save and share script
    • 10
      Fully featured without looking cluttered
    • 8
      Collections
    • 8
      Option to run scrips
    • 8
      Global/Environment Variables
    • 7
      Shareable Collections
    • 7
      Dead simple and useful. Excellent
    • 7
      Dark theme easy on the eyes
    • 6
      Awesome customer support
    • 6
      Great integration with newman
    • 5
      Documentation
    • 5
      Simple
    • 5
      The test script is useful
    • 4
      Saves responses
    • 4
      This has simplified my testing significantly
    • 4
      Makes testing API's as easy as 1,2,3
    • 4
      Easy as pie
    • 3
      API-network
    • 3
      I'd recommend it to everyone who works with apis
    • 3
      Mocking API calls with predefined response
    • 2
      Now supports GraphQL
    • 2
      Postman Runner CI Integration
    • 2
      Easy to setup, test and provides test storage
    • 2
      Continuous integration using newman
    • 2
      Pre-request Script and Test attributes are invaluable
    • 2
      Runner
    • 2
      Graph
    • 1
      <a href="http://fixbit.com/">useful tool</a>
    CONS OF POSTMAN
    • 10
      Stores credentials in HTTP
    • 9
      Bloated features and UI
    • 8
      Cumbersome to switch authentication tokens
    • 7
      Poor GraphQL support
    • 5
      Expensive
    • 3
      Not free after 5 users
    • 3
      Can't prompt for per-request variables
    • 1
      Import swagger
    • 1
      Support websocket
    • 1
      Import curl

    related Postman posts

    Noah Zoschke
    Engineering Manager at Segment · | 30 upvotes · 3M views

    We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.

    Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

    Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.

    This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

    Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct

    Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.

    Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.

    See more
    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 27 upvotes · 5.2M views

    Our whole Node.js backend stack consists of the following tools:

    • Lerna as a tool for multi package and multi repository management
    • npm as package manager
    • NestJS as Node.js framework
    • TypeScript as programming language
    • ExpressJS as web server
    • Swagger UI for visualizing and interacting with the API’s resources
    • Postman as a tool for API development
    • TypeORM as object relational mapping layer
    • JSON Web Token for access token management

    The main reason we have chosen Node.js over PHP is related to the following artifacts:

    • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
    • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
    • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
    • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
    See more
    Stack Overflow logo

    Stack Overflow

    69.1K
    893
    Question and answer site for professional and enthusiast programmers
    69.1K
    893
    PROS OF STACK OVERFLOW
    • 257
      Scary smart community
    • 206
      Knows all
    • 142
      Voting system
    • 134
      Good questions
    • 83
      Good SEO
    • 22
      Addictive
    • 14
      Tight focus
    • 10
      Share and gain knowledge
    • 7
      Useful
    • 3
      Fast loading
    • 2
      Gamification
    • 1
      Knows everyone
    • 1
      Experts share experience and answer questions
    • 1
      Stack overflow to developers As google to net surfers
    • 1
      Questions answered quickly
    • 1
      No annoying ads
    • 1
      No spam
    • 1
      Fast community response
    • 1
      Good moderators
    • 1
      Quick answers from users
    • 1
      Good answers
    • 1
      User reputation ranking
    • 1
      Efficient answers
    • 1
      Leading developer community
    CONS OF STACK OVERFLOW
    • 3
      Not welcoming to newbies
    • 3
      Unfair downvoting
    • 3
      Unfriendly moderators
    • 3
      No opinion based questions
    • 3
      Mean users
    • 2
      Limited to types of questions it can accept

    related Stack Overflow posts

    Tom Klein

    Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

    See more
    Google Maps logo

    Google Maps

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    567
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    • 253
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    • 136
      Address input through maps api
    • 82
      Sharable Directions
    • 47
      Google Earth
    • 46
      Unique
    • 3
      Custom maps designing
    CONS OF GOOGLE MAPS
    • 4
      Google Attributions and logo
    • 1
      Only map allowed alongside google place autocomplete

    related Google Maps posts

    Tom Klein

    Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

    See more

    A huge component of our product relies on gathering public data about locations of interest. Google Places API gives us that ability in the most efficient way. Since we are primarily going to be using as google data as a source of information for our MVP, we might as well start integrating the Google Places API in our system. We have worked with Google Maps in the past and we might take some inspiration from our previous projects onto this one.

    See more
    Elasticsearch logo

    Elasticsearch

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    Open Source, Distributed, RESTful Search Engine
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      Powerful api
    • 315
      Great search engine
    • 231
      Open source
    • 214
      Restful
    • 200
      Near real-time search
    • 98
      Free
    • 85
      Search everything
    • 54
      Easy to get started
    • 45
      Analytics
    • 26
      Distributed
    • 6
      Fast search
    • 5
      More than a search engine
    • 4
      Great docs
    • 4
      Awesome, great tool
    • 3
      Highly Available
    • 3
      Easy to scale
    • 2
      Potato
    • 2
      Document Store
    • 2
      Great customer support
    • 2
      Intuitive API
    • 2
      Nosql DB
    • 2
      Great piece of software
    • 2
      Reliable
    • 2
      Fast
    • 2
      Easy setup
    • 1
      Open
    • 1
      Easy to get hot data
    • 1
      Github
    • 1
      Elaticsearch
    • 1
      Actively developing
    • 1
      Responsive maintainers on GitHub
    • 1
      Ecosystem
    • 1
      Not stable
    • 1
      Scalability
    • 0
      Community
    CONS OF ELASTICSEARCH
    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
      Expensive
    • 4
      Hard to keep stable at large scale

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    Tim Abbott

    We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

    We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

    And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

    I can't recommend it highly enough.

    See more
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 10M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    GitHub Pages logo

    GitHub Pages

    17.6K
    1.1K
    Public webpages freely hosted and easily published.
    17.6K
    1.1K
    PROS OF GITHUB PAGES
    • 290
      Free
    • 217
      Right out of github
    • 185
      Quick to set up
    • 108
      Instant
    • 107
      Easy to learn
    • 58
      Great way of setting up your project's website
    • 47
      Widely used
    • 41
      Quick and easy
    • 37
      Great documentation
    • 4
      Super easy
    • 3
      Easy setup
    • 2
      Instant and fast Jekyll builds
    • 2
      Great customer support
    • 2
      Great integration
    CONS OF GITHUB PAGES
    • 4
      Not possible to perform HTTP redirects
    • 3
      Supports only Jekyll
    • 3
      Limited Jekyll plugins
    • 1
      Jekyll is bloated

    related GitHub Pages posts

    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.6M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
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    Dale Ross
    Independent Contractor at Self Employed · | 22 upvotes · 1.6M views

    I've heard that I have the ability to write well, at times. When it flows, it flows. I decided to start blogging in 2013 on Blogger. I started a company and joined BizPark with the Microsoft Azure allotment. I created a WordPress blog and did a migration at some point. A lot happened in the time after that migration but I stopped coding and changed cities during tumultuous times that taught me many lessons concerning mental health and productivity. I eventually graduated from BizSpark and outgrew the credit allotment. That killed the WordPress blog.

    I blogged about writing again on the existing Blogger blog but it didn't feel right. I looked at a few options where I wouldn't have to worry about hosting cost indefinitely and Jekyll stood out with GitHub Pages. The Importer was fairly straightforward for the existing blog posts.

    Todo * Set up redirects for all posts on blogger. The URI format is different so a complete redirect wouldn't work. Although, there may be something in Jekyll that could manage the redirects. I did notice the old URLs were stored in the front matter. I'm working on a command-line Ruby gem for the current plan. * I did find some of the lost WordPress posts on archive.org that I downloaded with the waybackmachinedownloader. I think I might write an importer for that. * I still have a few Disqus comment threads to map

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