As artificial intelligence (AI) has developed as a technology, it has become involved in more and more aspects of business and life.
AI cars get massive headlines, but it goes a long way beyond that – AI is now involved in everything from data analysis to logistics.
One of the other spheres in which AI is proving very useful is in software development.
There are now a range of different AI tools that developers can use to make their jobs and lives easier, and we’re going to look at a selection of them in this article today.
We’ll first go through the list, explaining what the tools are and how they can help developers.
However, that’s not all. After that, make sure you check out our buyer’s guide and frequently asked question sections underneath.
These should provide you with all the advice you’ll need to make sure you make the right choice. But without further ado, let’s get into the AI tools themselves!
Microsoft Azure AI Platform
This one’s well-known enough to almost not need any kind of introduction. However, it’s well-known for a reason.
It offers a huge range of different services that are invaluable for developers.
These include a variety of ready-made services and straightforward modules for a number of different AI capabilities including translation, transcription, image recognition, and more.
Because it uses pre-trained algorithms, you’ll be able to get very fast machine learning capabilities very quickly, and it serves as a great open playground for learning about machine learning.
There’s then also the core data science cloud service, which will allow you to train and deploy your ML models after you’ve made them.
It’s even easy to import models from elsewhere (whether pre-built or open source). You can then make changes to them in a variety of programming languages.
You can also use it to build custom AI bots for practically any function. Virtual assistants, Q&A bots, etc.
Pros
- Wide range of services.
- The huge user base means there are lots of resources for help and advice.
- Relatively simple to get started with.
Cons
- Limited customizability for niche applications or areas.
TensorFlow
If you’re looking for an end-to-end open source platform for machine learning, then it’s definitely worth taking a look at TensorFlow.
It provides an excellent range of functions, resources and tools for machine learning.
Even if you only want one part of its functionality, such as natural language processing, you’re still likely to find something that will work for you.
It was originally developed by Google back in 2014, along with several other key projects like Keras.
Since then, though, the project has been massively expanded on, and it’s now used extensively across Google’s various teams.
It provides many features, including the ability to save neural networks and training process information, as well as providing a wide range of visualizations and diagnostics functions.
Overall, it’s extremely powerful. And while it lacks some specific functions available in Microsoft Azure, it’s still one of the most popular open source AI platforms around.
Pros
- Very extensive set of functions for machine learning.
- Great community support network built up over the years.
- Simple to start using, no need for complex setup.
Cons
- Error messages can be tricky to understand.
PyTorcha
PyTorch is another major player in the market. If you’re already familiar with Python and have experience working with deep learning frameworks, then it’s easy to get up and running quickly.
While it’s based on NVIDIA’s proprietary CUDA framework, it’s got plenty of other benefits too.
It allows for GPU acceleration when necessary and uses C++ instead of CUDA, making it cross-platform compatible.
The main benefit of PyTorch is that it makes things much simpler than you’d expect.
You don’t need to worry about installing CUDA libraries or writing code; PyTorch does all that for you.
Instead, you just write your model using regular python code. Then, PyTorch takes care of building the graph to run through the model for you.
This might seem daunting at first, but it really simplifies everything, allowing you to focus on getting great results rather than figuring out how to create a graph.
Also, it supports both batching and parallelism within individual nodes, and provides native integration with GPUs.
With this, you can simply load data into memory, feed it through your model, and it’ll handle the rest for you.
Pros
- Strong support for GPUs.
- Built for speed.
- Open source.
- Simple to use.
Cons
- There can be some problems when scaling.
MXNet
MXNet provides an environment for deep learning that’s both flexible and efficient.
This enables users to build models easily while avoiding unnecessary overhead and resource consumption.
As a result, it’s often used for large scale tasks within the industry. It includes something called Gluon.
Gluon is great because it allows you to get the best of both easy imperative code and fast symbolic code at once, whereas before Gluon was added you’d have had to choose one or the other.
It also allows for simple linear regression etc. for uses like speech recognition, object detection, and more.
MXNet was designed right from the beginning for cloud infrastructure, which means it can achieve nearly linear scale with multiple CPUs or GPUs.
In fact, its performance in this area is so good that Amazon Web Services chose it to be their deep learning framework over its various competitors.
With its large ecosystem of tools and resources and its ability to support a range of programming languages, it’s a very solid option.
Pros
- Efficient for larger workloads.
- Flexible for different types of applications.
- Easy to integrate.
Cons
- Not as widely used as others, so less community support.
H2O
H2O is a leading open-source software tool integrated with an AI platform for developers and companies.
It was developed by H2O.AI and is written in different programming languages like Python, Java, and R.
It’s designed in programming languages that developers are familiar with so they can easily develop applications for predictive analytics and machine learning.
Its features include Deep Learning, Data Science, Graph Analytics, Big Data, Python, Java, Scala, and R.
These can also help a company provide customers with solutions for real time and non-real time data analysis, image processing, recommender systems, and more.
Overall, H2O can work with any type of data set and scales well even for very large datasets.
Its machine learning functionality is one of its main strengths, though, and it supports a variety of machine learning algorithms.
This technology helps you perform feature extraction, dimension reduction, classification, prediction, clustering, regression, among a range of other things.
Pros
- Flexible and portable models.
- Highly scalable.
- Native in cloud model deployment.
- Great model monitoring capabilities.
Cons
- Can take up a lot of memory.
Caffe
Caffe is a great deep learning framework. When you use it, you’ll see that it was designed with a focus on modularity, expression, and speed.
It’s especially suited to beginners who would like to dip their toes into the waters of deep learning and use some pre-built models for image recognition.
One of the most impressive things about it is its speed.
It’s able to process one image per millisecond for inference, and for learning it can manage one image every 4 milliseconds, and this speed is ever-increasing as updates come to technology and to Caffe itself.
There is a fairly large community using Caffe for a variety of applications from PhD research projects to full scale applications that businesses are using for vision, speech, and other media.
Pros
- Well-suited to image based CNN.
- Significantly faster than most of its competitors.
- Adapted for mobile apps.
Cons
- Somewhat limited functionality compared to other options.
Buyer’s Guide
There’s a lot to consider when it comes to choosing which AI tool is the best for you and your circumstances, so here’s a bit of advice to help make the decision a bit simpler.
Remember to read on afterwards for the FAQ section, too.
Think About What You Need It For
There are all kinds of different uses for these sorts of AI tools, and some are better than others at certain things.
For example, if you specifically want to work on software for a mobile app, you might find that some tools are specifically designed with that in mind.
If you need good speech transcription learning, then another might suit you better.
Just remember that each tool has its own unique features, limitations, and functions, so choose the one that works best for you.
Also Consider Your Size And Hardware Needs
Some tools will be much bigger than others, both in terms of disk space and RAM requirements.
This is going to depend a lot on how many cores each tool offers and what kind of hardware you have available.
Another thing to think about is whether you plan on using them locally, in the cloud, or on a server somewhere else.
If you’re planning on using the tool on your local system or one in the cloud but you still want to do offline data processing, make sure you pick a tool that can handle this.
How Much Money Are You Willing To Spend?
While several AI tools we’ve covered here are free, such as TensorFlow and PyTorch, not all of them are.
Microsoft Azure, for example, comes with a free period, but then you’ll have to pay if you want to keep using it.
This might make going for a free one seem like the obvious choice, but in Azure’s case at least, you’re buying quite a lot of functionality for that money.
Of course, it all comes back to being clear about what your needs and intentions are, as well as figuring out how much cash you’re willing to part with.
How Confident Are You With The Technology Already?
Although individual opinions and tastes vary, it’s generally agreed that some AI tools are easier to use than others.
It’s not quite that straightforward, though, because a lot of what’s easier or harder depends on you.
For instance, if you’re great at using C++, then getting a tool to work well with C++ is going to be relatively easy.
However, if all you know is Python, then you’re going to have a harder time. To an extent, you can pick your tool to fit your strengths.
However, there are still some that have higher learning curves.
There are often benefits for powering through, but you’ll have to decide whether these benefits are worth the extra time or training they’ll take.
Frequently Asked Questions
What Are Machine Learning And Deep Learning?
The basic idea of ML is simply getting computers to learn things without being explicitly told them by finding patterns within data.
However, it doesn’t end there.
With the likes of Deep Learning (DL), neural networks, and Bayesian inferences, it also takes into account how to model those patterns in order to take actions, automate tasks, etc.
So while DL is more focused on pattern recognition, it can also tackle classification, regression, and the like. The list goes on and on.
That said, the main point here is that ML deals with the ability to detect patterns within data.
So the focus is on discovering hidden aspects within data without needing to specify exactly what exactly those aspects are.
Now, an important distinction between ML and DL is that DL refers specifically to deep nets.
These are models that use multiple complex algorithms that are based on and inspired by the human brain.
This allows the network to learn complex relationships among input data.
All machine learning uses algorithms but with deep learning, these are particularly advanced and try to imitate human thought.
That means that deep learning is a subtype of machine learning (or, if you like, a concept within machine learning).
Deep learning is at the forefront of a range of exciting technologies. Driverless cars, for instance, use deep learning as a means of spotting obstacles.
Digital assistants like Amazon
What Is A Neural Network?
Neural Networks are a specific type of Artificial Intelligence algorithm that use layers in order to detect patterns in data.
They’re an effective way to get machines to perform various types of analysis on large amounts of data.
Deep Learning is built upon artificial neural networks, which are essentially computer programs created through mathematical and physical equations.
These networks consist of nodes, and connections, allowing them to process information from one layer to the next until they reach some sort of conclusion.
They’re an essential part of deep learning, as described above.
Deep learning uses neural networks modelled on the human brain as a way of processing data, yielding results that weren’t possible before.
Why Do We Want To Use AI On Data?
AI is becoming increasingly useful because computers are getting super-smart; faster processors, storage, memory, and connectivity are making them ever smarter and more powerful.
It’s now possible to apply AI to almost any domain where humans need help: medicine, biology, transportation, law enforcement, financial services and even consumer products.
Application of AI to data allows us to process it many times more quickly than we’d be able to otherwise, and also to find actionable insights that can improve performance.
In fact, AI has already been applied to fields such as image recognition, speech processing, translation, natural language processing, sentiment analysis and planning, to name just a few.
And experts predict AI will transform every industry over the coming decade.
Is Machine Learning The Same Thing As Robotics?
The short answer is that they’re not the same thing, although they do overlap in some areas.
In particular, there is an increasing interest in integrating advanced machine learning into robots so that the robots can “think” for themselves.
One potential future goal for this is to create a true AI – i.e. an artificial organism that is self-aware and can genuinely think, rather than simply being able to follow patterns.
Researchers are divided on whether this is ever going to be possible, but it remains a tantalizing possibility.
More mundane uses for robots include performing repetitive tasks in factories, an area where machine learning has less connection.
Final Thoughts
There you have it, a summary of some of the best AI tools for software developers that are around today.
There’s quite a lot of variety available when it comes to great AI tools and there should be one that suits your purposes, no matter what niche your project falls into.
Whether you’re comfortable using a free one – and there are some great free options around, no doubt about it – or would prefer to go for a paid service, there’s something on this list that’s just right for you.
Also, now that you’ve read the buyer’s guide and FAQ sections, you should be in prime condition to make the choice for yourself.
