Boost Your AI Skills Today
Looking to advance your expertise in AI? At the end of this article, make sure to review our resource collection.
Overview
Navigating the world of AI development in the cloud isn't as magical as it might sound, but it is full of real challenges and opportunities. The cloud offers flexibility, scalability, and compute power that can transform AI projects—but only if you know how to use it well. This guide will give you practical tips with a touch of that "field guide" vibe to help you feel more prepared for the terrain ahead.
Find Your Bearings: Choose the Right Framework
Your framework is your guiding tool. Whether you prefer PyTorch*, TensorFlow*, or another option, make sure it fits the requirements of your project and the cloud environment you’re working in. Most frameworks offer solid cloud integration, but some perform better in certain cloud platforms or with specific hardware accelerators. Make your choice wisely, but don’t overthink it—stick with what you know works unless your project indeed calls for a change.
Pro Tip: Choose a framework with strong cloud support and a large community. This will save you time when issues inevitably arise.
Pack Smart: Optimize for Cloud Efficiency
The cloud offers near-infinite resources—at a price. Inefficient models can quickly run up costs, so make sure you're packing your AI models with optimizations. This includes using techniques like quantization, pruning, or mixed-precision training. Every cloud provider offers ways to monitor resource use, so use them to keep an eye on performance and cost.
Pro Tip: Monitor your resource use early. Unexpected costs are common pitfalls, especially if you let models run without proper checks.
Plan Your Route: Design Effective Data Pipelines
In AI development, data is often the heaviest part of your workload. The cloud can handle huge amounts of data, but you need a well-thought-out pipeline to avoid bottlenecks. Ensure your pipeline can scale as your data grows and be mindful of latency issues between where data is stored and where it’s processed. Consider using data caching and distributed storage to improve performance.
Pro Tip: Always test your data pipeline at a smaller scale before deploying it to production. This will save you headaches (and costs) later.
Expect Detours: Build for Resilience
Things will go wrong—that’s just part of cloud development. Servers may crash, network connections can fail, and bugs will appear. Building resiliency into your AI workflows is essential. Whether that means setting up automatic retries, using checkpointing, or using distributed systems that can handle failures, ensure your systems are prepared for the unexpected.
Pro Tip: Use cloud-native features like autoscaling and fault-tolerant services. These can handle much of the heavy work when problems arise.
Stay Safe: Secure Your Environment
Security isn't just for infrastructure teams; it's a key part of AI development too. Handle data securely, encrypt sensitive information, and manage access controls diligently. AI models often deal with private data, so ensuring compliance with regulations like General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) may also be necessary. Cloud providers offer robust security tools, so make use of them.
Pro Tip: Don't hard-code sensitive information like API keys into your scripts. Use environment variables and cloud-based secrets management.
Measure Progress: Track the Right Metrics
It's easy to get lost in the development details without stepping back to evaluate your model's performance. Set clear goals and track key metrics such as model accuracy, training speed, and cost efficiency. In cloud development, you'll also want to monitor performance metrics like latency, throughput, and infrastructure use.
Pro Tip: Use cloud-native monitoring and logging services to track your model’s real-time performance and resource consumption.
Collaborate: Share Your Journey
Cloud environments are designed to make collaboration easier. Whether your team is working on shared datasets, tuning models, or troubleshooting, take advantage of the cloud's collaborative tools. Document your workflows, version your models, and share what you learn with your team to ensure everyone stays aligned.
Pro Tip: Use tools like managed notebooks or version-controlled environments to streamline collaboration and reproducibility.
Resource Library
In the vast landscape of cloud AI development, the right resources can transform a challenging expedition into a streamlined process. The following section offers code samples, articles, and more to help you implement the previous strategies and tips. Gain the expertise to build on platforms like Microsoft Azure*, Google Cloud Platform* service, Amazon Web Services (AWS)*, and Intel® Tiber™ AI Cloud.
What you’ll learn:
- Deploy Kubernetes* on AWS using services like load balancers, Elastic Kubernetes Service (EKS), and Amazon EC2* VMs powered by Intel® Xeon® Scalable processors.
- Launch instances on the Intel Tiber AI Cloud to take advantage of Intel's latest accelerators and computing platforms.
- Use tools like Helm and Infrastructure as Code frameworks to deploy workloads optimized for Intel software and hardware in the cloud.
Get Started
Step 1: Get Started on the Intel Tiber AI Cloud
The article discusses Intel Tiber AI Cloud, a platform that provides developers with access to cutting-edge Intel hardware and software solutions for building, testing, and optimizing AI and HPC applications at low cost and overhead. It highlights the ease of getting started, the range of hardware options, and how to set up instances for development, including a simple chatbot demo using AI Tools from Intel.
Step 2: Intel® Cloud Optimization Module for AWS CloudFoumation*: XGBoost on Kubernetes
This module can be used to build and deploy AI applications on the AWS cloud. Specifically, it focuses on one of the first Intel Cloud Optimization Modules, which serves as a template with codified Intel accelerations covering various AI workloads. It introduces the AWS services used in the process, including Amazon Elastic Kubernetes Service (EKS), Amazon Elastic Container Registry (ECR), Amazon Elastic Compute Cloud (EC2), and Elastic Load Balancer (ELB).
Also, check out the following three-part video series to build and deploy performant AI applications on the AWS cloud using Kubernetes, Docker*, and AI Tools from Intel.
Video 1: Introduction and Environment Setup
Video 2: Resource Creation and Application Deployment
Video 3: Application Testing and Summary
Step 3: Intel Optimized Cloud Modules
The complete instruction set and all source code is published on GitHub*
This set of cloud-native open source reference architectures helps developers build and deploy Intel-optimized AI solutions on leading cloud providers, including AWS, Microsoft Azure*, and Google Cloud Platform service.