Best 10 Artificial Intelligence Platforms for Business of 2024

This article explores the best AI platforms for building, deploying, and optimizing AI applications. We will discuss what AI applications are and also explain how AI platforms differ from AI tools.

In short, AI platforms include software, frameworks, and services required to build AI apps.

According to a McKinsey report, AI will add a staggering $25.6 trillion to the global economy by 2027. At the same time, with automation gaining traction, another study suggests that around two-thirds of jobs could be influenced by AI-driven automation in the near future.

The growing demand for AI tools reflects a strategic shift among small businesses. Businesses are increasingly integrating AI across various services to strengthen their operations. Meanwhile, enterprises are focusing on innovation and tackling complex challenges through AI technology.

AI platforms enable training data, deep learning, and machine learning (ML) development. They significantly reduce both the time and costs associated with development efforts. These platforms offer a range of options, from open-source solutions to custom-built ones.

Let’s explore the best AI platforms in 2024.

What is an AI Platform?

AI refers to a broad field of computer science that aims to create intelligent programs. These programs are capable of mimicking human cognitive functions like learning, problem-solving, and decision-making.

These programs include various AI technologies like machine learning, computer vision, and natural language processing. Examples of AI in action include chatbots that can hold conversations and self-driving cars that navigate roads.

An AI platform is a software suite that provides tools and functionalities specifically designed to develop, test, deploy, and manage AI applications.

They leverage AI technology, which includes several methods and techniques, to perform tasks that traditionally require human intelligence. This includes machine learning, natural language processing, computer vision, robotics, and more.

Popular AI platform examples are Google Cloud AI Platform, Amazon SageMaker, and Microsoft Azure Machine Learning.

Best AI Platforms for Businesses

AI platforms empower businesses to develop innovative data analysis, prediction, and automation solutions. This can lead to new products, services, and improved efficiency, giving them a competitive edge.

From Microsoft Azure to ChatGPT, the top 10 AI platforms for businesses for AI development are below.

1. Microsoft Azure

Microsoft Azure AI is a cloud computing platform for AI development. It provides tools and infrastructure for building custom AI models, integrating pre-built AI services, and developing intelligent solutions.

Its suite of services includes AI software, features, and frameworks designed to develop, deploy, and maintain AI applications. Moreover, it has different AI services covering the entire development lifecycle. From data preparation to model training, deployment, and monitoring, Azure streamlines AI workflows.

Azure simplifies the process of using AI in your applications. With its pre-built AI models, like Azure Cognitive Services, you can easily add features like understanding natural language and analyzing images to your apps. This is even possible if your team doesn’t have a deep background in AI development.

It works with popular tools and frameworks like Python and TensorFlow, making it familiar and easy for developers to use.

In addition, it provides powerful analytics tools to help you understand what your AI is doing. This allows businesses to optimize processes, improve customer experiences, and uncover new opportunities — all driven by data-driven insights.

2. Amazon Web Services (AWS) SageMaker

Amazon Web Services (AWS) SageMaker is a fully managed service that provides developers and data scientists with the tools to build, train, and deploy machine learning models at scale.

It offers a range of capabilities, such as data labeling, model training, and model hosting. SageMaker provides an integrated development environment (IDE) for building and iterating on models, along with managed infrastructure for training and deployment, making it easier for businesses to leverage AI technologies in their applications.

3. IBM Watson

IBM Watson is a multifaceted platform by IBM. It offers a range of tools and services for artificial intelligence (AI) development. Developers can leverage pre-trained AI models for tasks like natural language processing and image recognition, while also having the capability to build and train custom models using their own data.

Furthermore, the platform provides infrastructure for deploying and managing AI models in production environments and tools for monitoring performance.

4. Google Cloud AI

Google Cloud AI covers the entire AI development lifecycle, from data preparation and model training to deployment and management. This cloud-based solution is used by more than 60% of Gen AI startups, nearly 90% of Gen AI unicorns, and around 60% of the world’s 1,000 largest companies.

It offers different services for training, testing, and developing generative AI tools. Its key features include Vertex AI, a unified platform for building machine learning models, and pre-built APIs designed for natural language processing and image recognition tasks.

Additionally, it provides services such as cloud storage for training data and access to high-powered computing resources like Tensor Processing Units (TPUs).

Google Cloud AI is an excellent choice for data scientists, AI developers, and business leaders.

5. TensorFlow

Google’s TensorFlow is an open-source AI platform for creating ML models that can run on desktop, mobile, web, and cloud. It provides scores of data tools to preprocess, clean, and consolidate data.

Created by the Google Brain team, TensorFlow is widely used in training models. This library is well-suited for large-scale machine learning and numerical computation. It provides a library with pre-trained models that you can use in your projects.

With this platform, developers can develop, customize, and optimize their existing Ml models.

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