Until recently, getting involved in AI development required expensive hardware and deep technical expertise—this kept many people from exploring this field. However, cloud AI developer services are breaking down these walls, making AI accessible to hobbyists and developers alike.
Here’s how these cloud AI developer services allow anyone to build, train, and deploy artificial intelligence models without the need for high-end equipment or command-line expertise.
What Do Cloud AI Developer Services Offer?
Cloud AI developer services are platforms that provide tools, pre-built machine learning models, and computational resources to help you work on AI projects without requiring expensive hardware.
Using cloud-based services is cost-effective because you can rent resources on demand, paying only for what you use.
Some of the biggest Cloud AI developer platforms include AWS (Amazon Web Services), Google Cloud and Microsoft Azure.
For hobbyists and small teams, Massed Compute is a great option thanks to its user-friendly virtual desktop interface.
Here’s what you can get with cloud AI developer services:
Pre-Built Models
You don’t have to start from scratch! Services like AWS SageMaker, Google Cloud’s AutoML, and Azure Cognitive Services provide pre-trained models for common AI tasks like image recognition, text analysis, and more. You can fine-tune these models to suit your project’s needs.
Integrated Development Environments (IDEs)
Many cloud platforms offer user-friendly tools and virtual desktop interfaces. With virtual desktop infrastructure (VDI), you can access a powerful desktop environment from any device. This eliminates the need to have command-line knowledge, which makes AI development accessible even if you’re a hobbyist.
Scalability
What if you need more power? You can increase your allocated resources—like adding extra cloud GPUs—whenever your project demands it. This is particularly useful for machine learning tasks that start small but grow in complexity.
What Are the Open Source AI Tools to Make AI Development Easier?
Cloud AI developer platforms can provide seamless integration with popular open-source AI tools. Here are some examples of these open source AI tools:
TensorFlow, developed by Google, is an open-source framework used for deep learning, computer vision, and NLP, and is fully supported by Google Cloud’s AI Platform, along with AWS and Azure.
PyTorch, created by Facebook, is used for research-focused projects and dynamic neural networks, and is available on AWS SageMaker, Google Cloud, and Azure Machine Learning.
Hugging Face Transformers is a library for NLP tasks. Cloud AI platforms provide easy deployment with pre-built containers.
Scikit-Learn is a Python library for traditional machine learning algorithms that is commonly included in platforms like Google Cloud and Azure.
Apache MXNet, optimized for speed and scalability, is natively supported by AWS, and Keras, a high-level API built on TensorFlow, is pre-installed in many cloud AI platforms for quick prototyping.
Jupyter Notebooks, an open-source tool for coding, data visualization, and collaboration, is supported across all major cloud providers. These tools, combined with cloud infrastructure, enable developers to access powerful resources and streamline the AI development process.
Should I Use AI or Cloud Computing for 2025?
If you want to work on an AI project in 2025, with cloud tools, you can easily bring your idea to life.
You can collect your dataset, upload and store your data in a cloud platform to store, train your model using an intuitive virtual desktop interface, and test and deploy your model.
This could take days to do instead of the weeks or months required if you were doing this all locally. And the costs would be minimal. Many cloud services offer free trials or pay-as-you-go options.
Why wait? Start your AI development with cloud platforms in 2026. Explore Massed Compute’s marketplace today to rent a GPU virtual machine and use the coupon code MassedComputeResearch for 15% off any GPU rental.
