Put your hands up if your business is running AI/ML workloads. Everyone? Yep. And you’re using Kubernetes, right?
So we’ll skip the intro where we tell you how important AI is, and why Kubernetes is the best platform to run AI workloads on.
Let’s get to the important stuff: how to manage your Kubernetes environments to best support the needs of your AI workloads.
There are five key capabilities we believe you need. Let’s take a run through, and along the way we’ll give you some pointers about how our Palette platform can help you tick each box.
1. Effective compute and storage management
Running Kubernetes AI and ML workloads requires massive computational power - even if you’re not like Microsoft, consuming all the output from a nuclear power plant, you’ll find that the compute requirements of training and performing inference tasks are crazy.
Many different types of processing units are involved: CPUs, GPUs, TPUs, and FPGAs. GPUs dominate training workloads, CPUs are better suited for inference tasks, and FPGAs are ideal for low latency, energy efficient deep learning applications.
Storage needs are just as critical. AI/ML workflows depend on distributed storage to handle the datasets, pre-trained models, and logs. It’s not an easy task to maintain efficiencies while ensuring consistent access to these resources.
Configuring access for your cluster to specialist hardware often requires deploying a whole stack of software. We’ve worked to build pre-configured packs for this software, making it quicker to access GPUs and integrate with some of the most popular storage backends like Ceph, NFS, AWS EBS, and Azure Disks.
Through our custom policies, you’re able to fully control resource allocation, ensuring GPUs and CPUs are allocated efficiently, based on workload requirements. Dynamic scaling is automated, allowing your infrastructure to grow or shrink as needed, without manual intervention.
So whether you’re running training tasks for a complex model overnight, or running inference in real time, with Palette you can avoid bottlenecks and downtime.
2. Automation and orchestration of workloads
Building and deploying an AI/ML model isn’t the easiest thing. There’s data preprocessing, training, validation, and deployment. Trying to manage these workflows manually is a tough job where you’ll be spending a lot of time pounding your head into the desk. Let’s just say that’s not the best use of anyone’s time. And if you try to add in scaling the workflows to support demand, you’re looking at a potential nightmare trying to do this without any help.
This is where choosing Kubernetes to help will make you look like a hero. Kubernetes integrates with tools like Kubeflow and Argo Workflows, which help to enable end-to-end pipeline automation. For example, you could have an AI/ML model pipeline that preprocess datasets stored in PVs, are then trained on GPU nodes, validated for accuracy, and then deployed using something like KServe, for real-time inferencing, all in automated procedures.
With Kubernetes helping to simplify the automation aspect of the pipeline, our Palette platform takes it to the next level, by:
- Offering pre-configured templates, or Cluster Profiles, that include tools like Kubeflow and Argo Workflows, which help users accelerate deployment and setup.
- Providing observability tools to monitor pipeline performance and resource usage in real time.
- Automating the lifecycle management of your AI/ML tools, which helps to eliminate issues with compatibility and updates.
One real-world example: a US federal agency uses Spectro Cloud to automate pipelines for natural disaster response. Satellite imagery is processed with AI models to assess damage and guide resource deployment. Kubeflow automates the entire pipeline, from data ingestion to inference, while Spectro Cloud Palette ensures consistent updates and monitoring across distributed locations.
3. Cost optimization
AI/ML consumes massive amounts of compute and storage resources, which costs a ton of money. And if you over-provision your resources, that only adds to the cost.
Cluster autoscaling is therefore your best friend when it comes to using Kubernetes for your AI/ML workloads. It dynamically adjusts resources based on model demands, and can use spot instances, which helps to lower your cloud bills too.
Add Palette into the mix, and you get even better visibility and control of costs. It:
- Gives you native cost dashboards to track expenses across workloads and clusters, and makes it easy to deploy third-party cost management tools like cast.ai and Kubecost.
- Automates scaling policies so you avoid over-provisioning.
- Makes it easy to deploy your workloads to diverse environments as needed: for example, training can run on low cost on-premises resources while inference can scale in the cloud during peak demand.
We work with one retail company to train its “recommendation” models overnight on cheaper spot instances. The autoscaling feature ensures that only the nodes that are necessary are used, which reduces compute costs and maintains performance.
4. Flexibility to run in diverse environments
Where should you run your AI/ML workloads? Common question, and the answer: it depends.
You can go the route of the cloud and benefit from its easy scalability. Keep your workloads on-prem to meet security concerns. Run at the edge for real-time decisionmaking. Or all of the above.
Since Kubernetes is infrastructure-agnostic, it can support all of these different infrastructure environments, whether individually or in combination to form a true hybrid approach.
Kubernetes can work on diverse sets of hardware platforms and integrate with the different processing capabilities, whether using GPUs for training or FPGAs for low-latency inference. And Kubernetes has the option to add in Custom Resource Definitions (CRDs), which allow you to fine-tune your infrastructure for specific AI/ML workflows.
The challenge of course is making that multi-environment nirvana real. That’s where Palette can help. It provides a single platform to consistently manage multi-cloud, on-premises, and edge deployments, with the right choice of software elements for each location, including lightweight K8s distributions like K3s.
One logistics company we work with trains its models in the cloud and deploys inference workloads to edge clusters for real-time fleet optimization. Spectro Cloud ensures consistent management across those environments.
5. Intelligence at the edge
Real-time data analysis is one of the most important features in many business applications today, from cancer detection using MRI machines, intelligence gathering from remote military drones, and autonomous vehicles navigating the streets, to retail companies offering personalized shopping experiences.
These are all instances where every millisecond is important, whether it’s saving lives or providing customers a better experience. None of it can be done effectively without bringing AI to the edge. Running AI-powered applications directly on distributed edge computing infrastructure, closest to where the data is generated, is the only way instant decisions can be made.
It also happens that processing data at the edge eliminates the risk of network failure, limits security risks such as interception, and avoids ingress/egress costs incurred by cloud processing.
While there are clear benefits, deploying AI workloads at the edge does come with its own long list of challenges, from hardware limitations and security threats, to the sheer scale of edge deployments.
Spectro Cloud's Palette EdgeAI solution addresses these challenges with:
- Repeatable templates for consistent deployments across edge locations.
- Automated updates to AI models, ensuring continuous improvement.
- Robust security features, including hardened configurations and data encryption.
The US military leverages Spectro Cloud to deploy AI-driven reconnaissance systems across distributed edge devices such as drones, mobile command units, and battlefield sensors. These edge deployments enable real-time object detection, facial recognition, and threat assessment directly on-site, reducing latency and ensuring rapid decision-making in mission-critical scenarios.
Spectro Cloud’s Palette EdgeAI ensures consistent updates to AI models across all edge devices, while its centralized management platform enables secure and seamless oversight of thousands of remote deployments, even in disconnected environments.
Your next step
If you’re to get full advantage from AI/ML workloads with Kubernetes, you need to solve issues like resource management, workload orchestration, and cost control. We’re confident that we can help you get to the next level: whether managing resource-intensive training, automating pipelines, or deploying AI at the edge, Spectro Cloud simplifies the process and ensures consistency across environments.
So take the next step. Learn how Spectro Cloud can help you scale AI/ML with Kubernetes by exploring our AI/ML solutions or scheduling a demo today.
Learn more about AI at the edge
https://www.spectrocloud.com/solutions/edge-ai
Watch our webinar on how AI at the edge can transform missions https://www.brighttalk.com/webcast/19922/606987