Can AWS machine learning help your business grow?
Machine learning, and artificial intelligence in general, are among a number of technologies that are completely changing the way companies are working today. They’re increasing efficiency, and freeing up developers for new tasks to help businesses achieve faster growth. Machine learning and AI are becoming a lot more accessible thanks to platforms like Amazon Web Services (AWS), who are working to help every business and developer access and use them.
But what is machine learning?
And can it be used to benefit your business?
Today, we’re going to look at what machine learning can actually do to help companies grow, and how AWS is helping to increase access for current and potential developers.
So firstly let’s take a look at machine learning and AI in general.
It might surprise you to know that AI has actually been around since 1956. The main goal of machine learning and AI has always been efficiency. Teaching computers to learn takes a lot less time than programming them with the exact skills for a single task. Especially when you consider how many things we expect our computers to do these days.
One of the most common and interesting developments is deep learning, which is a subset of machine learning. Deep learning uses algorithms based on our own brains, called neural networks, to complete tasks. This is something most people will interact with daily. If you own a virtual personal assistant like Alexa, you’ll be interacting with it using deep learning.
Some of the many uses of machine learning that you might hear about or even use today are: voice recognition, self-driving cars, product recommendations, and fraud detection. The applications of this technology are designed to make our lives easier, safer, and to personalise our user experiences. Machine learning models can be designed to analyse videos and images, turn text into lifelike speech, build conversational agents for customer service, as well as translate and transcribe scripts. So, with the amazing things machine learning can be used to do, it’s easy to see why it’s so popular.
However, even though machine learning has a massive potential and is a great development tool, it’s still not used universally. This is because, despite the many great benefits, it’s complex to set up, expensive and computationally intensive. So before a lot of businesses can commit to it, they need to ensure the benefits to them and their profits outweigh these drawbacks. But can platforms like AWS give people greater access to machine learning?
Companies like AWS have the resources and abilities to not only use machine learning for their own development, but also to help others implement it. Amazon Sagemaker was designed by AWS specifically to reduce the complexities surrounding machine learning and AI applications.
They wanted to make it easy for anyone to build, train, and deploy machine learning algorithms, whether they were experienced, or completely new to AI. Part of the way they achieved this is with a selection of pre-installed algorithms. These are tried and tested machine learning algorithms that users can choose from and use in their own services.
A major benefit of SageMaker is that it uses the newest techniques, including reinforcement learning. Plus, it lets users choose the frameworks they work best with if they choose to make their own custom models. They can choose from TensorFlow, Pytorch, and Apache MXNet, depending on what they’re most comfortable with. And if this still seems confusing, Amazon also have a Machine Learning Solutions Lab with hands-on workshops and advisory services to help you match business challenges to flexible, intelligent solutions. Plus, they’re constantly working to improve research into machine learning by funding university departments, faculty, PhD students and more with their Machine Learning Research Grants.
So AWS definitely has a commitment to reducing the complexity of implementing machine learning. But how does it meet the computational demands we looked at earlier? It’s a big ask when you consider just how many businesses use AWS. In short, the power of NVIDIA Tesla v100 Tensor Core GPUs is used by Amazon EC2 P3 instances. P3 instances provides faster network throughput to help other businesses use machine learning in the cloud with high performance compute.
This can help companies train machine learning models faster than ever before through AWS. AWS offers a range of compute options, including GPUs, FPGAs, and high memory instances to run interference.
So surely access to pre-trained machine learning models, and the ability to create custom ones faster than ever is going to cost a lot?
AWS pricing is actually based on the individual services you choose to use, which combine to result in a monthly bill. Amazon ML has an hourly rate for the compute time needed when creating your predictive models. You then also pay for the number of predictions generated for your services and applications. If you’re utilising real-time predictions, you also pay for hourly reserved capacity, which depends on the amount of memory your model needs.
AWS also has a free tier, which offers a variety of SageMaker services for the first two months after you sign up. If you’re still not sure exactly how your machine learning models will add up to a monthly cost, they have a variety of pricing examples available for potential customers to browse.
Machine learning is one of the most common and fastest developing technologies available to us at the moment. Some huge company names, like Netflix, Duolingo, Siemens, and more, are using AWS machine learning to develop their services to suit their modern customer base.
Thanks to cloud providers like AWS, machine learning is becoming available to more developers than ever for a reasonable price. It’s becoming more and more obvious that this technology is going to be instrumental in moulding our future businesses.
If you’re eager to find out more about the ways your company could be using machine learning and AI, or want to find out more about AWS and their services, feel free to get in touch with us.