Organizations need to think about creating a roadmap for AI transformation. Whether businesses need to build AI platforms by themselves or they need to utilize fully developed platforms.
Today CTOs are excited to utilize the AI platforms on the cloud by moving the Server Environment to Cloud, CIOs need to think about it with serious consideration as every data science project needs huge investment. However, cloud has solutions available.
This guide will help the CIOs to understand which Cloud AI Platforms are available today.
Before diving into the details of Cloud AI Platforms, let us first look at the Gartner Magic Quadrant for Cloud AI Platforms, where the leaders are Microsoft, Google, IBM, and AWS, however, I personally recommend H20.ai
Which Cloud platform to chose?
If you are thinking of developing your own AI Platform for the organization then it will not be a good option, because you will need huge investment, and why reinvent the wheel, where AI vendors have already made the platform which means fully developed technologies are available. However, the strategy for the transformation must be defined before adopting the cloud AI platform.
Moreover, there is also a need to see if the current infrastructure can be supported by Cloud platforms. On-premises data centers will have challenges when cloud-based AI platforms are required to be adopted.
There is no doubt that the focus must be to use ready-made AI services than creating your own platform. But the question is which platform to choose?
Commercial or Open Source. Just have a look at this image.
1. SageMaker Studio on AWS
Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017. SageMaker enables developers to create, train, and deploy machine-learning models in the cloud. This platform is well known because there is complete documentation available.
2. Vertex AI by Google
Vertex AI Workbench is the single environment for data scientists to complete all of their ML work, from experimentation to deployment, to managing and monitoring models. It is one of the most advanced AI algorithms. User-friendly interface but very technical, the platform is still being evolved.
3. Azure ML Studio by Microsoft
Microsoft Azure Machine Learning Studio is rich, there is an old platform by Microsoft that will be retired on 31st August 2024. Classic resources will be discontinued by Microsoft. This is the most mature platform today. Clean and user-friendly.
4. Watson Studio by IBM
IBM Watson Studio consists of a workspace that includes multiple collaboration and open-source tools for use in data science. Though IBM is not a leader in the public cloud, Watson studio has made a comeback for them.
5. Open source AI Platform – H20.ai
In my top 5 list, I always try to consider 5th as Open Source, where you can use your technical skills to develop the environment yourself.
My choice from all open source projects which are available is H20.ai
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform. H2O works with R, Python, Scala on Hadoop/Yarn, Spark, or your laptop.
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