Why ML Capabilities Of GCP Is Way Ahead Of AWS & Azure

Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure are the leading cloud providers by a long shot. Though late to the party, GCP has seen robust growth over the years. Google Cloud’s revenue jumped nearly 46 percent year-on-year to $4.04 billion in the first quarter of 2021.

“All vendors offer strong ML services and functionalities, but this is where GCP stands out as their years of search engine expertise, and research come into play,” said Diwakar Chittora, Founder & CEO, IntelliPaat.

Here is how GCP offers more benefits than AWS, Azure.

Tensor Processing Unit (TPU)

TPUs are Google’s custom-developed application-specific integrated circuits (ASICs) to accelerate ML workloads. A big advantage for GCP is Google’s strong commitment to AI and ML. “The models that used to take weeks to train on GPU or any other hardware can put out in hours with TPU. AWS and Azure do have AI services, but to date, AWS and Azure have nothing to match the performance of the Google TPU,” said Jeevan Pandey, CTO, TelioLabs. 

Benefits of TPU:

• Can be leveraged by Google Cloud Machine Learning Engine to run complex ML models
• Increased performance of linear algebra computation
• Reduced time-to-accuracy when training complex neural network models
• Scalable across different machines 

Vertex AI

Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. It has pre-trained APIs for vision, video, natural language, etc. Vertex AI integrates with widely used open-source frameworks such as TensorFlow, PyTorch, and scikit-learn, along with supporting all ML frameworks via custom containers for training and prediction.

Benefits of Vertex AI: 

• Easily train and compare models using AutoML 
• Faster movement of models from experimentation to production
• Access to AI toolkit used internally to power Google, including computer vision, language and conversation data.
• Smoother end-to-end ML workflow that removes the complexity of self-service model maintenance and repeatability with MLOps tools

Open-source

Google cloud’s open-source contributions, especially in tools like Kubernetes --a portable, extensible, open-source platform for managing containerized workloads and services, facilitating declarative configuration and automation-- have worked to their advantage. 

“Kubernetes helps in the AI and ML workflow as it supports and leverages the speed of today’s cloud GPUs,” said Chittora.

Benefits of Kubernetes:

• Works on any container runtime or any infrastructure, including public cloud, private cloud and on-premises server
• Can host workloads on a single cloud or across many clouds.
• 100% open-source project offering more flexibility 

Speech and translate APIs

Google cloud’s speech and translate APIs are much more widely used than their counterparts. According to Gartner’s 2021 Magic Quadrant, Google cloud has been named the leader for Cloud AI services. Pre-trained ML models can be instantly used to classify objects in an image into millions of predefined categories. Additionally, one of the top ML services from Google cloud is Vision AI, powered by AutoML.

Benefits of speech and translate APIs: 

• Can translate many languages
• Can detect source text language
• Speech API recognises more than 80 languages
• Affordable pricing
• Products are highly scalable, easy-to-use and accurate

AutoML

AutoML enables developers with limited machine learning expertise to build custom ML models in minutes. It is a cloud-based ML platform and uses a No-Code approach with a set of prebuilt models via a set of APIs. It is tightly integrated with all Google’s services and stores data in the cloud. Trained models can be deployed via the REST API interface. 

“Data Analysts can combine a custom model and pretrained models in a single product, and this feature keeps GCP above its competitors,” said Saket Saurabh, Head, Cloud and Analytics at STEMROBO.

Benefits of AutoML:

• A no-code way to build models
• Customers can train their own neural networks
• Can apply data and integrate predictions whenever you need
• Not limiting like single type of ML model offered by others

Other benefits

Abishek Chiffon, Data Scientist at Ideas2IT, outlined ML and AI services that put GCP ahead of AWS and Azure:

• Offers AI Hub, a repository of models in a format that can be deployed in Kubeflow, Deep Learning VMs in GPU or TPU.
• AI Platform Classic tool helps create ML training jobs with TensorFlow, Keras, PyTorch, Scikit-learn, and XGBoost and allows training at scale with user containers and custom frameworks. 
• Google has TensorFlow to build and launch self-contained Deep Learning models.
• Its AI Platform Notebooks are VM instances that come pre-integrated with TensorFlow and PyTorch instances, Deep Learning packages, and Jupyter notebook.
• Deep Learning VM images come pre-installed with all software, deep learning and ML frameworks on a Google Compute Engine instance. 

“AWS and Azure have different tools for different services, and also the training algorithm of GCP has matured over time through its Search and AI platforms used worldwide. GCP is turning out to be a popular choice for experienced and new data scientists and is ready to trump AWS and Azure soon,” said Saurabh of STEMROBO.