Business
AI in the Cloud: What Are The Go-To Options?
The technological landscape has evolved to include AI assistants, self-driving cars, and machine learning solutions that process data in a blink of an eye. AI is by far the technology that can drive innovation forward, and you should keep up. Any AI endeavor begins with building a platform, and creating, improving and scaling solutions. Cloud-based AI services make this possible. In this article, we’ll look at AI in the cloud and three major providers who are blazing a trail in the world of AI cloud technologies.
Major Players for AI in the Cloud
For the scope of this article, AI is defined as machine learning, since ML is the biggest constituent of the technology. Major cloud service providers have paved a way for AI in the cloud. If you want to experiment with AI or go live with your solution, there are three widely known vendors: Amazon, Google, and Azure. Let’s race through these vendors and see what they’re up to.
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Amazon For Cloud Artificial Intelligence
Amazon began by making storage and virtual machines. More was yet to come for AI in the cloud. Fast forward to today – Amazon’s AI product goes under the name of SageMaker. Sagemaker is flexible and lets you solve any ML problem.
Generally, Sagemaker is a platform for full-cycle ML development. It simplifies and standardizes ML model development and makes the process more reproducible and reliable. SageMaker provides extensive documentation to help you understand how the algorithms work in the machine learning space.
Key features of SageMaker
With the features in SageMaker you can:
Built-in Jupyter notebooks for data exploration and model development
Pre-built algorithms and frameworks for common machine learning tasks
Automatic model tuning to optimize model performance
Secure and scalable infrastructure for training and deployment
Integration with other AWS services for data storage, processing, and deployment
Support for creating custom algorithms and frameworks to suit specific use cases
Built-in model monitoring and debugging tools to identify and fix issues with deployed models
Support for creating custom algorithms and frameworks to suit specific use cases
Google Vertex AI
Vertex AI is an ML platform for training and deploying machine learning models and AI apps. Vertex AI leverages a combination of data engineering, data science, and ML engineering workflows with a rich set of tools for collaborative teams.
Key Features of Google Vertex AI
When deploying AI in the cloud, Vertex AI is a unified platform where users can:
leverage a unified user interface and API for all AI-related Google Cloud services
create models with image, text, video, or structured data
perform custom model trainings
automate and scale projects throughout the ML lifecycle using Vertex AI’s end-to-end MLOps tools
integrate Vertex AI with all open-source frameworks
integrate video, translation, and natural language processing (NLP) with existing applications
Microsoft Azure Machine Learning
Azure Machine Learning Services is a stand-alone customizable cloud product. Azure Machine Learning lets you accelerate and manage ML-based projects. You can use the service to train algorithms, deploy models, and manage MLOps.
Key Features of Azure Machine Learning
Azure Machine Learning helps you nail cloud-based AI solutions by:
- building ML models on the platform
- supporting open-source technologies
- integrating ML models into apps or services
- using a robust set of tools, backed by Azure Resource Manager APIs for building advanced ML tooling
- providing security and role-based access control (RBAC) for your infrastructure
AI in the Cloud: The Verdict
Currently, the three vendors have been successfully head-to-head on the leading edge of AI in the cloud, offering similar feature sets. The core value of such platforms, however, is that they provide a standardized process for creating ML models, ensuring predictability and explainability. The platforms also speed up development and enable fast learning. The results achieved can be used to determine the feasibility of the project, so that businesses can decide if it makes sense for them to continue with AI.
To understand where your technology investment should go — Amazon, Microsoft, or Google — you will probably want to choose an AI cloud platform complementary to your existing technology. Other than that, beware of some challenges such as network transmission and infrastructure learning curve hurdles.
Exadel provides a 3-day AI Proof of Concept that will show within a few days whether AI is going to significantly benefit your organization. The AI PoC will also confirm whether the data you already have is enough to create a high-quality model.
Cloud-Based Artificial Intelligence Taking the Lead
AI is becoming the latest trend and essential ingredient for many businesses and developers to surge ahead in their game. As the adoption increases, more features and capabilities will be generated around AI, going far beyond chatbots and conversational UI.
If you nail AI in the cloud, you can apply your highly transferable learnings across many data-intensive industries. You’ll be able to find new ways to gather, organize, interpret and optimize how you manage data in your organization – ultimately competing in the market and boosting efficiency and profits.