When we talk about artificial intelligence, something that comes immediately to our mind is where we are going to store and compute the large amounts of data and the algorithms that we will need to build accurate models for our AI solution. Amazon AWS, Microsoft Azure, and Google Cloud provide us with a set of tools and services that help us in this process.
The main idea of this post is to give a general overview of the platforms and compare them. All of them provide tools that you can use in the whole process of implementing AI.
SageMaker Studio is a complete tool from Amazon where you can start implementing (prepare, build, train, manage, and deploy) machine learning models. SageMaker provides visual editors, debuggers, profiles, and CI/CD for machine learning. They offer solutions that manage complex machine learning infrastructure for you.
These are the tools from AWS that can improve your ML implementation speed:
Amazon Augmented AI (Amazon A2I) is an innovation that Azure and Google still don't have. This tool provides a way to improve the accuracy of models' predictions with the possibility to use teams of people to improve them and obtain more precise results.
Microsoft Azure also provides a no-code experience with the integration of different applications that Microsoft already has for businesses (Power BI, Power Apps, Power Automate, Power Virtual Agents). This can help different users from your company to take advantage of AI with tools that are friendly and ready to use.
Microsoft Automated ML allows users to create models with no-code experience by helping them train and find the best one for their data. It will provide you with good suggestions and improve interactively.
With the Microsoft Azure Machine Learning Designer, the user can create models from templates from different ones that are commonly used in IA with a drag and drop feature. It also allows you to add code in parts of the implementation to customize your models. The solution adapts to your needs.
Google Cloud Platform includes a suite called Google Cloud AutoML for users with no experience and minimal effort. Google helps to optimize your time because it is not necessary to implement a model or to create the infrastructure. The time for training data is also reduced to a fraction of what it usually takes using other managed solutions.
Google also provides similar tools to Amazon and Microsoft to build machine learning solutions. You can customize your experience by starting from scratch, or you can use Google's set of tools to assist you at every step of the way.
If you want to start with a simple integration using an SDK from various providers, you can use these available tools:
All of these tools provide valuable functionality to users who do not have knowledge of AI. They are compatible with a wide range of popular libraries and frameworks, including TensorFlow, MXNet, Keras, Pytorch, Chainer, SciKit Learn, and others. In addition, Microsoft and Amazon offer solutions to easily use Jupyter notebooks. In Google Cloud, you can work with them, but it will require additional configuration. They all allow you to start from scratch, build your own infrastructure and processes, or use their automated suites. The three companies have different areas of expertise. So, depending on the type of AI project, you should compare the benefits and prices. There is no general rule when choosing an AI cloud service provider as the choice depends on several factors and needs, but this blog post gives you a general idea of the tools that they offer.
We’d love to learn more about your project.
Engagements start at $75,000.