Skills required for building powerful GenAI apps on AWS, Azure, and GCP

As a cloud architect or engineer, creating Generative AI (GenAI) applications on cloud platforms such as AWS, Azure, and GCP demands a blend of cloud expertise and GenAI knowledge. Let’s begin by examining the GenAI services provided by each cloud provider, followed by an exploration of the essential technical skills needed to build robust GenAI applications in the cloud.

Amazon Web Services (AWS):

• Amazon Bedrock: Amazon Bedrock allows you to choose the right Foundation Model (FM) for your specific use case. You can consume and fine-tune popular open-source models using SageMaker JumpStart.
• Amazon Titan: Familiarize yourself with Amazon Titan, a collection of home-grown foundation models developed by AWS researchers. These models power services like Alexa, CodeWhisperer, Polly, and Rekognition.
• Amazon CodeWhisperer: This AI coding companion generates real-time code suggestions based on natural language comments and existing code in your Integrated Development Environment (IDE).

Microsoft Azure:

• Azure OpenAI: Azure offers OpenAI, providing access to powerful language models like GPT-3 and GPT-4. You can fine-tune these models for specific tasks.
• Azure Machine Learning: Use Azure ML for model development, deployment, and management.
• Azure Cognitive Services: Explore pre-built APIs for vision, speech, language, and decision-making tasks.

Google Cloud Platform (GCP):

• Google AI Platform: GCP’s AI Platform offers tools for training, deploying, and managing machine learning models.
• Google Cloud AutoML: Use AutoML to build custom models without extensive ML expertise.
• Google Cloud AI: Leverage pre-trained models and APIs for various tasks, including natural language understanding and image recognition.

Read: Evaluating Cloud Platforms: What Cloud Architects Need to Know(Opens in a new browser tab)

Core skills required to build GenAI apps on Cloud:

Apart from understanding the GenAI services provided by AWS, Azure, and GCP, you must also possess expertise in designing and developing cloud-native solutions. Building GenAI applications, regardless of the cloud platform (AWS, Azure, GCP), necessitates a robust foundation in the following areas:

Cloud Platform Expertise:

• Cloud Architecture: Understand cloud design principles, scalability, high availability, and cost optimization.
• In-depth knowledge of at least one major cloud platform (AWS, Azure, or GCP) is essential. This includes understanding core services like storage, compute, networking, and security. Familiarity with all three platforms is a plus, allowing for flexibility and multi-cloud deployments.

Generative AI Techniques:

• Solid grasp of generative AI concepts and techniques: This includes understanding Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other relevant algorithms.
• Knowledge of Large Language Models (LLMs): As LLMs are often the foundation for text-based generative AI applications, understanding their capabilities and limitations is crucial.
• You can read the Generative AI basics and Fundamentals to know more about the above points.

Data Engineering and Machine Learning:

• Experience with data pipelines: The ability to design and manage data pipelines that ingest, clean, and transform data for training generative AI models is essential.
• Machine learning fundamentals: Understanding core machine learning concepts like supervised vs. unsupervised learning, model training, and evaluation metrics is crucial.

Software Development and Programming:

• Proficiency in at least one programming language commonly used in machine learning and data science (Python is a popular choice).
• Familiarity with containerization technologies like Docker and container orchestration platforms like Kubernetes. These are essential for deploying and scaling generative AI models in the cloud.

Additional Skills:

• Cloud Cost Optimization: Understanding how to optimize cloud resource allocation for cost-effective GenAI deployments.
• Security best practices: Securing sensitive data and ensuring the responsible use of generative AI models are critical considerations.
• APIs and SDKs: Familiarity with the cloud provider’s APIs and SDKs specific to GenAI services can streamline development.
• Scalability and Performance: Optimizing GenAI applications for scalability and performance in the cloud environment is important.
• DevOps Principles: Experience with infrastructure as code (IaC) tools and CI/CD pipelines for automating GenAI development and deployment.
• Read: How to modernize cloud app architectures using GenAI.
• Monitoring and Logging: Ability to design monitoring and logging solutions for GenAI applications to track performance and identify issues.
• Communication and Collaboration: Effective communication skills to collaborate with data scientists, developers, and other stakeholders involved in building GenAI applications.
• Problem-solving and critical thinking: The ability to troubleshoot issues, optimize architectures, and adapt to new technologies is essential in this fast-paced field.

Continuous Learning:

The field of generative AI is constantly evolving, so a commitment to continuous learning is vital. Staying up-to-date on the latest advancements, research papers, and best practices will ensure the architect remains at the forefront of this exciting domain.

By combining your GenAI expertise with these cloud architect skills, you can design, deploy, and manage robust and scalable GenAI applications on any of the major cloud platforms (AWS, Azure, GCP).