It's been a while since we used to ask ourselves "When will machine learning play a key role in a full stack developer's skill set?" It is happening right now! Devs are constantly looking to leverage the best machine learning platforms for developers.
Those (platforms) that would help them harness the power of ML to create algorithms for their day-to-day tasks.
But which are the best machine learning tools and frameworks for developers, more precisely?
And here, I'm afraid that you're asking the wrong question: there's no such thing as “the best”, but “the best machine learning software for you”:
- for the specific problem that you're trying to solve through the algorithm that you design
- for the specific data that you're dealing with
The algorithm that you'll put together will need to suit both your problem and your data type. Therefore, gaining a thorough understanding of your data is crucial.
- is it a toolkit specialized in deep-learning that you need?
- or maybe one that ships with both machine learning and great data analytics tools?
- or are you maybe planning to leverage your future machine learning tool to do some streaming analytics?
There is one best ML toolkit for each type of data-based task that you're trying to automate by tapping into this technology of the... present.
And another great news for you is that all the big vendors — IBM, Google, Microsoft — are striving to make their software as simple-to-adopt as possible for developers. So that they/you can easily build services by just abstracting some of their algorithms' complexity.
But let's not beat around the bush any longer and let us put the 10 best machine learning platforms into the spotlight instead:
No doubt about it: open-source reigns supreme.
So, there's no wonder that Apache PredictionIO, an open-source stack with an open-source machine learning server on top, is head of the list.
Now, let's briefly go through its most powerful features:
- it's built to respond, in real-time, to dynamic queries when used as a web service
- … and also to unify data coming from multiple platforms in batch (in real-time again, needless to add) in order to collect comprehensive predictive analytics
- moreover, it ships with a template gallery for you to use as you create your machine learning engines
In short: as an ML tool to build predictive engines, Apache PredictionIO's perfectly equipped to streamline any type of artificial intelligence task.
2. Accord.NET, One of the Best Machine Learning Platforms for Developers
Accord/NET is a framework designed for scientific computing in .NET.
Why is it listed in this top here, of the best machine learning platforms for developers? Let me give you just a few strong reasons:
- it comes with image and audio processing libraries “turbocharged” with a wide range of scientific computing applications: pattern recognition, machine learning, statistical data processing
- it enables you to build a whole variety of scientific computing apps for commercial use that tap into real-time detection, signal processing, natural learning algorithms, computer audition, computer vision and so on
“Packed” with all these features, Accord.NET's main advantage over the other 9 most popular machine learning tools and frameworks in this list is its unmatched versatility.
What is Azure Machine Learning Model Management?
Here are 3 equally valid “definitions”:
- one of Microsoft's 3 machine learning tools (the other 2 being Learning Bench and Learning Experimentation)
- a cross-platform client for managing data and experimentation
- a workbench empowering you, the developer, to build your own AI model
Moreover, besides its machine learning tools, Microsoft also launched 3 AI tools:
- Custom Speech Service
- Bing Speech APIs
- Content Moderators
… adding up to the already existing 25 developer tools from its library, all geared at making AI more accessible.
Google open sourced this library of machine learning code and thus made it a lot easier for developers worldwide (including you) to:
… complex deep-learning-based neural nets.
Any developer can now leverage this software to build his/her deep-learning framework.
A “deep-learning framework” that runs seamlessly on an entire plethora of devices.
Expect to have all the
- needed resources
- data flow graphs (processed on Python or C++) presented as numerical computations that can get processed on CPUs
… right at hand when using TensorFlow
All too many strong features for this software library to be included in the list of 10 best machine learning platforms for developers.
The main rationale behind this platform? Helping both organizations and developers with little or zero experience of predictive analytics to leverage machine learning technology and turn their data into gold.
How? By providing its users with all the AI tools they would need for building their apps:
- virtual agents
- cognitive search engines
From sample code to starter kits, to other tools put at your disposal, you're practically enabled to:
- get quickly familiarized with predictive analytics
- harness its power and your own data to good use.
An in-memory data processing framework “spoiling” developers with an ever-growing library of much-needed algorithms and utilities:
- collaborative filtering
And this is not the only strong reason why Spark has been listed among the very best machine learning platforms for developers:
Expect to find Singa, too, the open-source framework shipping with a programming tool, that you get to use on various machines, across all their deep-learning networks!
When time is not on your side and you need to use an API immediately, Veles's the platform to go for.
Samsung's distributed deep-learning platform (C++-based, using Python for node coordination) is designed precisely for this type of “emergency” situations:
When you desperately need to use trained models for data analysis.
A deep-learning C++ framework designed to provide you, the developer, with an automatic imaging-based inspection tool.
“What are its main use cases?” you might ask.
So far it's been leveraged to build a variety of apps:
- large-scale industrial apps in vision
- … speech
- … multimedia apps
- as well as in academic research projects
And from some of its most “notorious” users, I should mention Facebook, Pinterest, and Google.
Is there a machine vision app that you need to develop? Consider Caffee then, one of the best machine learning platforms for developers.
Nervana's and Intel's “baby”, Neon is an open source machine learning library that's open source to boot.
“OK, but why would I want to use it?”, you might ask yourself.
- it's a cloud service, which makes it a lot faster than other “rivaling” machine learning frameworks: you get to build, train and deploy deep-learning technologies much faster
- its toolkit includes intelligent agents and technologically advanced apps that you get to tap into
There's a whole variety of AI toolkits that Amazon Web Services (AWS) put at your disposal. To name just a few:
- Amazon Rekognition Image
- Amazon Polly: automates the process of turning voice into written content
- Amazon Lex: makes the perfect base for your chatbots
Each one has its own key feature, which enables you to build your machine learning tools in a certain, distinctive way.
The END! This is the shortlist of 10 best machine learning platforms for developers to leverage.
The one that you should scan through first when looking for the ML software to suit your own scenario.