How to Build Generative AI Applications in the Cloud: A Beginner’s Guide

In Today’s Cloudbites:

♾️ The Synergy Between Cloud and AI

💻 How to Build Generative AI Applications

☁️ Cloud Services for Building Your App

🤫 PLUS: AI Can Now Control Your Computer

Read time: 8 minutes

Hi friends, welcome back to Cloudbites

In this newsletter, we'll discuss the synergy between AI and the Cloud, and how to build your own Generative AI application. We’ll also cover Anthropic’s updated AI model that automatically interacts with your computer.

CLOUD COMPUTING ☁️

♾️ The Synergy Between Cloud and AI

Ever wondered how Cloud Computing and Generative AI link together?

The short answer is: They work seamlessly and benefit one another. 

#1 Cloud Supports AI Workloads

Cloud Computing provides the infrastructure necessary for Generative AI, including high-performance GPUs and TPUs that make model training and quick deployment possible. 

E.g. Microsoft Azure powers AI applications like ChatGPT by providing computing resources, storage, and machine learning services to handle the intensive data processing required.

#2 Cloud is Scalable & Cost-Efficient

Cloud Computing also allows Generative AI applications to adjust resources dynamically based on demand, which is vital for handling large datasets and complex models.

Additionally, the cost-effectiveness of Cloud services means you can access advanced AI capabilities without the hefty investment in hardware. With Cloud solutions, you only pay for what you use.

#3 AI Optimizes Resource Allocation

Generative AI enhances Cloud Computing by optimizing resource allocation and improving user experiences. 

In fact, AI-driven analytics help Cloud providers anticipate resource needs, which minimizes downtime and boosts performance.

#4 AI Streamlines Development

By integrating Generative AI into development tools, Cloud platforms enable developers to generate code and propose architectures more efficiently.

These will streamline workflows and improve the quality of your application.

💻 How to Build Generative AI Applications

Earlier this month, I had the opportunity to speak at SXSW Sydney about this topic.

Building Generative AI applications may seem challenging, but with a clear roadmap, it’s easier than you may think.

Here’s how you can get started:

#1 Define the Problem or Use Case

Identify the problem you want to solve or the use case for your Generative AI application to shape the objective of your project.

E.g. Generating engaging product descriptions for an e-commerce site, composing music, etc. 

#2 Choose a Suitable Foundation Model

The next step is to select a foundation model that aligns with your project requirements. 

Many Cloud platforms offer pre-trained models that you can leverage. But if you have special requirements, you could always train your own custom model using your own dataset. 

#3 Set Up Your Development Environment

Prepare your development environment by installing the necessary libraries and packages tailored for Generative AI to reduce potential errors. 

This may include frameworks like TensorFlow or PyTorch, as well as specific SDKs or APIs provided by the platform. 

#4 Experiment with Your Chosen Model

To see how your model responds to different inputs and configurations, it’s important to experiment and refine its capabilities when necessary.

This step will help you understand how the model behaves and identifying any adjustments needed to improve performance. 

#5 Integrate the Model into Your Application

Once you’re satisfied with the model's performance, you could now integrate it into your application using the platform’s API or other integration tools.

In this step, make sure that the integration supports the functionalities you outlined in your use case, providing users with a smooth experience.

#6 Continuously Monitor Performance

After deployment, it’s always good to continuously monitor the performance of your Generative AI application to assess its outputs for quality and relevance. 

For your app to be successful in the long run, regularly evaluate how well the model is meeting user needs and preferences for an optimal user experience.

☁️ Cloud Services for Building Your App

#1 AWS – Amazon Bedrock

Amazon Bedrock is an AWS service designed to simplify the process of building Generative AI applications by providing access to a variety of foundation models through a unified API.

#2 Azure – Azure OpenAI Service

Azure OpenAI Service brings OpenAI’s models, like GPT, to Azure’s platform, enabling you to integrate these advanced models into your applications.

#3 Google Cloud – Vertex AI

Vertex AI offers a comprehensive set of tools to build, train, and deploy custom Generative AI models.

Note: There are many more Cloud options available, and it's important to compare each option before deciding which one is best for you.

ARTIFICIAL INTELLIGENCE 🤖

🤫 PLUS: AI Can Now Control Your Computer

Anthropic recently updated its latest AI model, Claude 3.5 Sonnet.

Its newest feature is the ability to control a computer by moving the cursor, clicking buttons, and typing text.

💡Here’s what you need to know:

• Anthropic introduces a beta feature that allows Claude 3.5 Sonnet to autonomously interact with computers based on screen observations

• This new model is still experimental and may be prone to errors

• The updated Claude 3.5 Sonnet cannot perform many routine computer actions and relies on a snapshot-based view of the screen, which may cause it to miss notifications

You can now build with the computer-use beta on Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI.

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THAT’S A WRAP

Thanks for reading! 😊

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