5 of the Best Machine Learning Tools for Your Project

machine learning tools

Will the application you’re planning to develop rely on machine learning? If so, you’ll need to think about which tools you want to use to build and train your neural networks, program deep learning capabilities, and turn your already-very-capable development team into a hothouse of AI talent.

There’s no compensating for skill when it comes to developing machine learning apps. Equally, the right tools will ease the burden on your team and make life so much easier for them – usually by offering a set of useful tools, modules, and libraries to speed up the development process and cut back on the amount of raw coding required.

Below, we’ve outlined six of our favorite tools for machine learning. It’s certainly not an exhaustive list, but it should give you a solid idea about where to start.

Think we’ve unfairly missed something? Tweet us @Tivix with your top picks!

1. TensorFlow

TensorFlow is probably the most widely-used machine learning framework around right now. It’s an open-source platform developed by Google that has gained a significant dev following since its first release in 2015.

What does TensorFlow offer? 

TensorFlow is comprised of two main tools:

  • TensorBoard: a visualization tool for network modelling and performance
  • TensorFlow Serving: a high-performance serving system for machine learning models, designed for production environments.

TensorFlow is particularly flexible, making it perfect for remote teams or those on the go. Run it on a CPU, GPU, desktop or on mobile – whatever suits your needs best.

Feature-wise, there’s support for regressions, classifications, neural network creation and algorithms. TensorFlow will help you develop an AI with solid image, handwriting and speech recognition, as well as natural language processing, if you have the coding skills to make the most of it.

What to bear in mind

This brings us to our first caveat: TensorFlow is a code-heavy option for a machine learning framework.

For best results, you’ll probably be coding in Python (though you can use JavaScript, C ++, Java and Go, C#, or Julia if you’re ok with using an experimental interface). TensorFlow certainly won’t build your neural network for you, and even those with a solid understanding of Python might experience a steep learning curve whilst using it.

It’s also worth noting that every computation in TensorFlow has to be created as a static graph, which is clunky and slows things down considerably. Newer frameworks have avoided this issue – it’ll be interesting to see whether TensorFlow retains its popularity as more options become available.

2. PyTorch

Another open-source platform, PyTorch was originally developed by Facebook – but has since been in use across a number of A-list tech companies, including Twitter and Salesforce. It’s regarded as TensorFlow’s main competitor in terms of how many people use it.

What does PyTorch offer?

PyTorch makes it as straightforward as it can be to train a neural network and even offers a few pre-trained options – so if the learning curve of TensorFlow isn’t for you, PyTorch could be a great option. It’s more ‘pythonic’ (if that isn’t a word then it should be), more intuitive, and can save you a significant amount of time during the development phase.

Additionally, the PyTorch library uses a dynamically updated graph. This gives it an edge over TensorFlow because you avoid the clunky static graph issue we mentioned above.

What to bear in mind

PyTorch lacks the distributed application management framework of TensorFlow, and there’s no native equivalent to TensorBoard – you can use a separate visualization tool, and some TensorBoard/PyTorch integrations do exist, but it does present an added level of complexity.

Reputationally, PyTorch works best as a tool for small-scale apps, passion projects, and rapid prototyping. It’s easy to work with and quick to pick up, but lacks the scalability and production readiness that TensorFlow and other machine learning tools offer.

3. Keras

Moving onto higher-level frameworks brings us to Keras. The Keras API sits on top of widely-used lower-level libraries like Theano, TensorFlow, and CNTK to create a modular, user-friendly approach to AI development.

What Keras offers

It’s hard not to fall in love with how elegant Keras is as a framework.

The API is beautifully written.

The fact that it’s unapologetically Python (“No separate models configuration files in a declarative format” declares the website, almost combatively) keeps it simple and results in code that’s easy to debug.

The range of modules on offer save so much time, with complex models created using just a single command line.

This is speedy prototyping to the max – and if you’re prepared to sacrifice some customizability, Keras’s combination of user friendliness, speed, and simplicity could win you over.

What to bear in mind

If you’re a complete novice when it comes to AI development, Keras will get you up and running faster than any other tool. But (there’s always a ‘but’) this comes at the expense of flexibility. Like a lot of high-level frameworks, it’s the Keras way or the highway, and the modular structure means you lose a significant amount of configurability.

Elegant and beautifully designed as it is, whether you’ll love Keras will depend on your priorities. If these include fast prototyping without the need for much experimentation, Keras will be useful.

4. Apache MXNet

MXNet is an open source framework for creating, training and deploying neural networks. It hasn’t been as widely-used as the first two frameworks on this list, but it’ll be interesting to see whether this changes as a result of it coupling up with Apache.

What does Apache MXNet offer?

Scalability, flexibility, and plenty of it.

We’ll start with the coding options. You’ll probably get the most out of it if you use Python (but then, Python is the language for AI and machine learning so this isn’t surprising), but there’s also support for Scala, Julia, Clojure, Java, C++, R and Perl.

On top of that, it supports both imperative and symbolic programming, so if you’re new to AI development and you haven’t got a huge amount of experience with symbolic programming, MXNet is a solid option. It also supports multiple GPUs concurrently, with optimized computations and fast context switching.

What to bear in mind

You’ll be able to do most of the stuff you do on TensorFlow on MXNet without any significant issues. However, one thing you do need to note is that the MXNet community is significantly smaller; for an open source project like MXNet this means a slower pace of development as well as scarcer support when you run into difficulties. That said, this could change now it’s an Apache project – watch this space.

If you’ve got a bit of experience in programming AIs and want to experiment more, you’ll probably be better looking elsewhere.

5. ONNX

Not the primary tool in your machine learning development toolbox, but handy nonetheless. ONNX was originally developed as a collaboration between Microsoft and Facebook to help with portability across frameworks, and has since expanded out from there.

What does ONNX offer?

ONNX lets you train models in one framework and then transfer them to another for inference. This is particularly useful if you love working in one framework, say PyTorch, but struggle when it comes to scaling your prototypes or moving them into production on that framework. So, you could build in PyTorch, and transfer to MXNet for its scalability strong point.

What to bear in mind

Not much – ONNX is an open format that facilitates framework interoperability. Whilst if you don’t use it correctly, it could lead to delays and frustration, there’s nothing here that might tank your project.

The only thing that you really should do before relying on being able to use it is to check which frameworks it’s compatible with. PyTorch, Caffe 2, MXNet, and Microsoft Connective Toolkit are currently the biggest frameworks that support ONNX models, and you can connect other common frameworks and libraries too – but its not universal.

TensorFlow and Keras have been slow to engage officially, though there are tools on GitHub you could use to get round this.