Kubernetes Use Cases: AI/ML

Tyler Au
5 minutes
June 6th, 2024
Tyler Au
5 minutes
June 6th, 2024

Why AI/ML is One of the Most Compelling Kubernetes Use Cases

In 2023, you would have trouble looking through social media or watching the news without mention of artificial intelligence (AI) and machine learning (ML). With the widespread knowledge of generative AI, last year became a pivotal year in the development and adoption of AI/ML solutions. A report from McKinsey saw that AI adoption has jumped to 72% in 2023 alone, breaking a 6 year trend of hovering at 60%. More and more organizations are adopting AI/ML for business uses and are reporting decreased costs and increased revenue, creating a case for one of the most interesting Kubernetes use cases in recent years.

Image courtesy of McKinsey and Company

Despite the benefits presented by AI/ML developing, refining, and using the tech is extremely resource intensive. The International Data Corporation (IDC) predicts that by 2025, 40% of a company’s IT spend will be allocated toward AI-related efforts. Gartner predicts that global spending on AI-related projects will reach $297 billion by 2027 - the enthusiasm towards AI/ML shows no signs of slowing down, so do the resources allocated to creating AI solutions.

Outside of staggering R&D costs, AI/ML operations at a bare minimum require these resources to run:

  • Processors: central processing units (CPUs), graphics processing units (GPUs), tensor processing units (TPUs) and field-programmable gate arrays (FPGAs) are industry standards
  • Memory and Storage: cloud storage, object storage, disk storage, and storage-area networks (SANs) are all great options and offer different benefits
  • Infrastructure and Operating Systems: Linux VMs is a go-to
  • Network: Enables system clustering 

Whether you’re in the training phase or in operations, these resources are the minimum for many AI/ML projects. As those projects scale, these resources must grow to support consumption and growing workloads, thus driving up costs significantly. Not only is the development and operation of AI/ML workloads extremely resource intensive, but also infamously time consuming, despite saving time for users.

Where AI/ML operations falter, Kubernetes is able to pick up the slack. Kubernetes, one of the leading open-source container orchestration services, has tons of utility in supporting AI/ML; OpenAI has even used the tech to support their projects. AI/ML as a Kubernetes use case presents a couple of the biggest points for adoption of Kubernetes and container tech because of how AI/ML workloads become optimized and resource efficient as a result. 

Kubernetes Artificial Intelligence and Kubernetes Machine Learning

Where Kubernetes optimize AI/ML workloads is through the use of containers. Containers package all the necessary components of AI/ML development into a lightweight and portable asset, allowing for isolated capabilities and flexibility in an often clunky AI workload. Kubernetes orchestrates these containers, automating many of the scaling, deployment, and monitoring processes that teams would normally have to tackle manually.

Here are some ways Kubernetes can revolutionize our approach to AI/ML pipelines:

Automated Scaling

Typically, the AI/ML training processes would follow the journey of data collection, data pre-processing, model selection, training, and then evaluation. This process is oftentimes lengthy (taking anywhere from around a couple hours to days) and consumes tons of data processing and storage power. In most cases, companies training multiple models don’t have the devices and resources at hand to train all of the models at once.

Utilizing Kubernetes load balancers during training, data scientists can scale ML workloads across different devices and train them parallel to each other. This also allows for faster experimentation times: a researcher at OpenAI was able to get their experiment running in a matter of days by scaling their experiment across hundreds of GPUs- a feat that would typically take a couple of months. In fact, autoscaling with Kubernetes enables faster results and innovations, letting DevOps teams move from A to Z faster than before. 

The container orchestration tool also scales resources up and down to optimize consumption, ensuring that all resources are allocated fairly throughout training without the need to interfere. This sentiment also extends to application of AI solutions, allowing resource consumption to be adjusted based on real-time data and ensuring optimized resource usage and high solution uptimes.

Managing and Scheduling

At its purest form, Kubernetes is a tool designed for the management and scaling of containerized apps and Kubernetes clusters. With regards to AI/ML workloads, this idea is still upheld.

Kubernetes is a great tool for managing cluster workloads, especially in the realm of AI. Within the container orchestrator, users not only enjoy the aforementioned automated scalers, but also different features such as health monitoring, container and pod deleters and creators, and deployment and performance statuses. AI infrastructures are extremely complex- Kubernetes aids in maintaining this infrastructure while providing networking, storage, and security capabilities to best serve your AI solution.

One of the strongest Kubernetes features, the Kubernetes scheduler monitors newly created pods and assigns them to nodes that would best host those pods. Workloads, in this case pods, are scheduled onto nodes with resources allocated based on their node availability, capacity, and other pod requirements. As with the case of OpenAI, schedulers are used to efficiently consume resources, coordinate large volumes of containers, and increase performance- while driving down costs generated by idle nodes.

Portability and Reproducibility

Within a Kubernetes workflow, the stages of an AI/ML pipeline become sectioned off into separate containers. This allows software developers to declare Kubernetes manifests that represent a desired state for their application, with these manifests being able to be reproduced across different environments and workflows. Instead of worrying about configuration and declaring states within each stage of development, developers can enjoy reproducible environments.

Outside of reproducibility, these repeatable environments made possible by Kubernetes also promote solution portability. Through platform-agnostic environments created by Kubernetes manifests, users can develop a single ML model compatible with various environments, clouds, etc. This model will be operable whether you’re working with a public cloud or a private cloud, bypassing vendor lock-in concerns. 

Fault Tolerance

Pairing AI/ML workloads with Kubernetes provides a higher degree of fault tolerance for a variety of reasons:

  • Isolating different stages of the AI development process into containers removes a single point of failure, creates opportunities for backups in case something goes wrong
  • Health monitoring checks for anomalies within containerized applications performance and pull clusters that aren’t performing
  • Kubernetes clusters contain self-healing capabilities: able to remove themselves from workflows and repair themselves, replaced in the meantime to ensure uptime

Improving Large Language Models (LLMs)

One of the most advanced forms of AI programs is the large language model (LLM). Known for identifying and generating text, amongst other capabilities, LLMs have experienced a meteoric rise in popularity over the past few years. Names like GPT, Gemini, and Llama 3 have become household names in the AI space.

In order to improve the performance and efficacy of their LLMs, many companies put their models through retrieval augmented generation (RAG) pipelines. This pipeline involves injecting your own custom data into LLMs, providing them as context for future model responses.

Kubernetes provides a ton of utility within a RAG pipeline. For many, their usage of Kubernetes in the context of these pipelines begins within testing- Kubernetes allows for users to create lightweight clusters of their pipelines that can be tested on environments. 

One of the biggest roles that Kubernetes can play within a RAG pipeline is with traffic distribution. In order to efficiently distribute network traffic to pods, RAG pipelines can integrate Kubernetes and utilize their load balancers. The result is stronger uptime during real-time data processes- which coupled with Kubernetes batch jobs schedulers allow for pods to execute tasks at their max potential.

Revolutionizing Kubernetes AI Workflows with Lyrid

Kubernetes is a great tool for managing tons of containers and mobilizing them with efficiency. In the case of AI/ML workflows, having this orchestration tool that combines scale, flexibility, portability, and security in a single package is critical to reclaiming the time and resources used in a traditional AI process. Kubernetes also provides automation throughout all stages of an AI lifecycle- from training, to development, to testing and deployment, development teams can rest easy with Kubernetes.

Despite Kubernetes offering tons of benefits to users, there is a learning curve to the tool. From migrating to a microservices architecture to even getting started, many new adopters find themselves running into problems with Kubernetes. Kubernetes should be about optimizing app deployment and getting the best out of your containers, not about creating new headaches for you. 

Deploy apps confidently with Lyrid Managed Kubernetes! Our Kubernetes solution hosts all of the best parts of Kubernetes, without the headache. Access automated scaling, self-healing, deployment streamlining, cluster provisioning, and so much more- all in a fully-managed, easy to use managed Kubernetes engine. Aimed at reclaiming development time and saving resources, our Managed Kubernetes solution is perfect for artificial intelligence and machine learning teams looking to make their workflows and project timelines more efficient

To learn more about Lyrid Managed Kubernetes, book a call with one of our product specialists!

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