Leverage best practices to build and deploy AI-powered applications on a global scale
As large language models (LLMs) like ChatGPT revolutionize the way people work, organizations worldwide are exploring ways to leverage AI infrastructure to enhance their own products.
While LLMs have commanded most of the press attention in recent years, engineers have relied on machine learning (ML) workloads throughout software development processes for years, using them to automate and scale actions like:
However, processing ML workloads is costly and resource-intensive. The hardware requirements of a robust AI infrastructure demand significant compute and graphics processing resources. As more people rely on these processes, the infrastructure to support them must scale accordingly, which can become cost-prohibitive during peak usage without proper planning.
As a result, development teams must leverage the tools and resources at their disposal to optimize infrastructural processes, reducing overhead while continuing to deliver quality products for their customers. This guide will explore how developers can create more efficient AI infrastructure to improve performance without impacting the bottom line.
Challenges AI Infrastructures Must Overcome
The path toward creating and implementing robust AI infrastructure is fraught with potential setbacks. Developers must work diligently to overcome countless obstacles on the road to deploying quality products for their customers. Possible roadblocks include:
Best Practices to Achieve More Efficient ML Workloads
The cloud-native foundations of ML workloads rely on cloud-based, orchestrated clusters of containers to deploy higher-quality code faster and more reliably. The following best practices apply foundational knowledge to those ML workloads — combine these tips along with a suite of modern cloud optimization tools to speed up deployment times for your AI-powered models and infrastructure.
1. Pick the Right Cloud Platform
Before building out your ML workload, the first thing to decide is where to do it. Your choice of public cloud platform will have far-reaching implications beyond your initial setup, so it’s worth weighing the benefits and drawbacks of Azure, GCP, and AWS. Here are three specific factors to consider for building AI infrastructure on the public cloud:
- Your cloud provider should be able to meet the high resource demands of AI infrastructure. Running large-scale ML workloads is particularly demanding on GPUs, and GPUs remain in short supply across industries as manufacturers struggle to keep up with the ever-growing demand. Prioritize a cloud partner with a demonstrated ability to keep expanding its technical foundations even in difficult market conditions.
- Your cloud provider should also offer features and services that support your desired AI infrastructure. Though their overall offering and ultimate objectives are similar, the tools and processes each cloud platform provides can vary in key ways that you’ll be much better off accounting for ahead of time.
- Your team should be familiar with the cloud provider and how it works. Ideally, your team should include some members who have already worked on successful projects on that platform. If that isn’t feasible, use the educational resources available through each platform to familiarize your team with their fundamentals.
For more insight on choosing the right cloud platform for your project, watch our quick primer with DuploCloud VP of Engineering Zafar Abbas.
2. Stick to the Well-Architected Machine Learning Lifecycle
The Well-Architected machine learning lifecycle offers a holistic view of the entire AI infrastructure and ML workload process. It’s an excellent foundation for building efficient and secure machine learning projects, whether you’re using AWS, Google Cloud, or Azure.
This framework posits that developing and integrating ML workloads isn’t a straight line with a beginning and an end. Rather, it’s a cycle that constantly reviews previous steps and anticipates upcoming tasks to reinforce its core goals and solidify its outcomes.
It starts with developing a business goal, which determines what value you hope your ML workloads will achieve. You then convert this goal into your machine learning problem, determining what data your ML workload will observe and what it will predict. Next, you’ll collect relevant data that will feed into the workload to develop the AI model that powers it. Once it’s ready, you’ll deploy the workload into production and then monitor it for performance and accuracy.
However, the machine learning lifecycle doesn’t end there. It’s now time to revisit your business goal and determine whether your model meets those expectations. After actual ML workload development and optimization, you may need to reevaluate your goal and adjust it to better align it to your model. Or, you may determine that your original goal is still attainable and adapt your data set and model to achieve that goal.
The best way to approach the Well-Architected Machine Learning Lifecycle is by building a modern data architecture that can efficiently move data where it needs to go. AWS recommends utilizing a central data lake for storage and implementing data services around it, which will provide unified governance capabilities and greater ease of movement.
3. Run Multiple Processes Simultaneously With Distributed Computing
Distributed computing systems have long been used to process complex tasks by combining multiple devices on a network into a single unit capable of handling processes that a standalone computer cannot.
It’s also a technique that can speed up model training and deployment times for your ML workloads. Consider implementing open-source tools like Apache Spark or TensorFlow, which are specifically designed around parallel processing techniques like batch processing and real-time data streaming. And when combined with DuploCloud, you can apply these benefits across your entire AI infrastructure.
4. Choose Sustainable Models to Reduce Energy Consumption
Machine learning workloads require vast amounts of energy to run. One study found that NVIDIA’s AI data servers require over 85 terawatt-hours of electricity each year, which is enough energy to power a small country. This energy usage is only expected to grow as AI demand leads to the construction of more data centers. Tapping into this energy to power your AI infrastructure won’t just be expensive; it can significantly impact the environment if it isn’t offset elsewhere.
Luckily, there’s a wealth of sustainable algorithms that can achieve similar results while reducing your energy needs and being less taxing to your bottom line. For example, many developers rely on the Google-developed Bidirectional Encoder Representations from Transformers (BERT) algorithm to provide contextual understanding of surrounding text to make further predictions, which is helpful for predictive text applications like chatbots and writing applications. Meanwhile, a distilled version of BERT called DistilBERT maintains 95% of BERT’s performance benchmarks while running 60% faster with 40% fewer parameters.
According to AWS, the best way to choose an algorithm that balances power and sustainability is to start with a simple one that offers a baseline level of acceptable performance. Then, test out other, more complex algorithms to determine whether the additional gains are worth the extra resource cost.
5. Automate Containerization and Scaling With Kubernetes
Like developing cloud-native applications, developing, deploying, and scaling AI infrastructure with manual processes is a virtually impossible task. There is simply too much data and too many processes that need to occur simultaneously, especially when your ML workloads need to scale up to meet user demand.
To meet these demands in cloud apps, developers rely on Kubernetes, which automates container provisioning and deployment through a process known as horizontal scaling. This process speeds up infrastructure reaction times to necessary tasks like load balancing and resource scaling and provides self-healing capabilities for any errors that may arise.
Since this approach to AI infrastructure is versatile and well-established, there’s no need to reinvent the wheel. Look for Kubernetes solutions that help your team pre-configure containers to facilitate the training, testing, and deployment of ML workloads within a robust container orchestration environment.
6. Lean on Cloud Optimization Tools
Kubernetes is a powerful orchestration tool, but its complexity makes it challenging to implement and monitor without additional tools. That’s where cloud automation platforms like DuploCloud come in to provide deeper visibility into your AI infrastructure, reduce complexity, and speed up deployment.
DuploCloud is purpose-built to streamline AI and ML orchestration, integrating tools like Amazon SageMaker, AWS EMR, and Kubernetes and providing essential context into security and performance with a single pane of glass. It also includes 24/7 real-time monitoring and alert capabilities, notifying you when your ML workloads are out of alignment with a variety of required security and compliance frameworks. And with full integration to your CI/CD pipelines, you’ll experience faster, more reliable updates to your entire infrastructure.
Other tools that can aid in enhancing your AI infrastructure include:
- TwinGraph is an AWS tool that allows workflow orchestration without relying on custom scripts.
- CodeGuru is another AWS tool that spots inefficiencies and vulnerabilities in your code.
- Apache Airflow is a Python-based open-source platform that helps developers create and maintain complex workflows, including machine learning orchestration.
- Metaflow is a framework designed to help data scientists build AI and ML models more efficiently.
Automate AI Infrastructure Provisioning With DuploCloud
Developing successful AI infrastructure requires understanding a complex array of systems, all working in concert to provide better, more efficient results. Luckily, you won’t have to go it alone.
DuploCloud is the ultimate DevOps Automation Platform, designed to put secure and compliant AI infrastructure at your fingertips. With out-of-the-box infrastructure provisioning and subject-matter experts, robust monitoring, and just-in-time access, DuploCloud’s automation capabilities unlock deeper visibility into your ML workloads so you can iterate faster and deliver more reliable results for your customers.
Want to learn more? Contact us today to request a 30-minute live demo.
