AI/ML startups are entering an era of unprecedented opportunity, with nearly one in three VC deals going to AI companies in 2024.
Yet, the more sophisticated your AI becomes, the more pressure it puts on your infrastructure. Serious compute, flexibility, and cost control is vital to train, deploy, and iterate models quickly and outpace the competition.
That means it is more important that ever for AI/ML startups to streamline infrastructure through effective DevOps and FinOps. Aligning your cloud operations with technical and business goals to faciliate experimentation and maintain smooth operations.
Here are five tips to help you overcome infrastructure challenges using DevOps and FinOps:
1. Embrace Infrastructure as Code (IaC)
IaC lets you define and manage cloud infrastructure using configuration files to provision resources automatically, rather than setting up servers, databases, and networks manually.
This script-based approach delivers the speed, consistency, and repeatability AI/ML startups crave by letting you reuse your specifications across various environments. For example, you can define the architecture for a cluster of agents once in Terraform and replicate it across staging, testing, and production environments.
Find out more about the benefits of IaC in Automat-it’s AI/ML Startups Guide to Overcoming Infrastructure Challenges
2. Optimize Cloud Spend Without Sacrificing Performance
To make models and products run smoothly without breaking the bank, you need just the right amount of cloud computing power. DevOps and FinOps help you strike the balance between delivering performance and avoiding waste.
Three essential strategies to do that are:
- Auto-scaling – automatically increasing (or decreasing) cloud computing power in real time based on demand. Allowing you to set rules for scaling infrastructure depending on spikes or lulls.
- Right-sizing – analyzing historical trends in your data usage to align resource allocation with actual needs.
- Using spot instances – discounted virtual machines offered at off-peak times. This is ideal for non-critical or batch workloads.
3. Integrate Best DevOps Practices for ML Pipelines
With agentic AI raising the bar for computing power even further, the principles of DevOps are now becoming mission-critical in this field. Specifically, using DevOps practices to overcome AI/ML infrastructure challenges involves:
- Emphasizing CI/CD for rapid testing, deployment, and safe rollbacks if necessary.
- Automation for monitoring, so that models and agents perform reliably and securely across environments.
4. Leverage Cloud-Native AI/ML Tools
Given AI/ML startups’ heavy reliance on cloud infrastructure, using cloud-native tools is key to making that infrastructure work harder, smarter, and as efficiently as possible. These tools allow your team to focus less on managing logistics and more on building models and products that matter.
Cloud-native tools offer several advantages, including smooth integration and auto-scaling. Download your free Guide for more insights on the advantages.
5. Consider Partnering for DevOps Expertise
It is fairly common for AI/ML startups to face a shortage of specific DevOps and FinOps expertise. It’s understandable that they tend to prioritize hiring data science specialists and engineers at the outset as they develop core products. But this can create knowledge gaps around infrastructure management which can cause bottlenecks or even outages as they scale.
Supplementing in-house talent with on-tap DevOps expertise offers a quick, flexible, and robust solution. DevOps as a Service, as offered by Automat-it, provided immediate access to pre-built tools and a wealth of DevOps knowledge and engineers so the AI/ML startup team can:
- Accelerate time to market
- Reduce cloud costs
- Avoid infrastructure pitfalls
This article is taken from Automat-it’s AI/ML Startups Guide to Overcoming Infrastructure Challenges. Download your free copy here.
If you’re an AI/ML startup looking to overcome infrastructure challenges, get in touch with us here.
