🔵🔴 Edge#134: Run:AI's 2021 AI Infrastructure Survey
A fascinating and extensive look at the true state of AI maturity
More interesting content formats in TheSequence! Today we’d like to share the results of the 2021 AI Infrastructure Survey performed by our partner Run:AI
🧲 Insights: Run:AI's 2021 AI Infrastructure Survey
Most research around the state of the AI industry talks about how the majority of initiatives are still immature and models rarely make it to production. To discover whether these pervasive ideas are still gospel in 2021, Run:AI commissioned a survey of more than 200 industry pros from 10 countries: data scientists, MLOps/IT practitioners and system architects. Conducted over the summer of 2021, the survey responses came primarily from experts at large enterprise companies with over 5,000 employees, and some with as many as 10,000. Respondents opened up about the technologies they use, the challenges they face with AI and the size of not only their AI budget but also their confidence in bringing AI into production.
The results are a fascinating and extensive look at the true state of AI maturity. They show an industry that despite being in the early stages of maturity, holds an exciting amount of potential. Three-quarters of those surveyed are looking to expand their AI infrastructure, and 38% have more than $1 million in annual budget to make that happen. In the frantic pursuit of innovation (and desire to outpace the competition), companies are putting considerable investment into AI initiatives, despite the fact that many still face early-stage hurdles with AI infrastructure setup, data preparation, and even goal-setting. With so much invested in making AI successful, it’s clear that early adopters have a lot to gain by choosing the right technology and approach to solving their infrastructure and orchestration issues.
The survey data shows some intriguing commonalities among companies of all sizes, and at times, juxtapositions emerge between the ambitious corporate vision of AI in production, and the day-to-day reality of scaling AI workflows. Here are a few key findings:
AI Is a Cloud-Native World
AI was clearly born with the cloud in mind, with 81% of companies using containers and cloud technologies for their AI workloads.
Along with containers comes the adoption of Kubernetes and other cloud-native tools for container management.
A sizable 42% of respondents are already on Kubernetes, another 13% on OpenShift, and 2% on Rancher /SUSE. These numbers are considerably higher than container adoption for non-AI workloads, making AI a leader in cloud-native adoption.
Big Spenders, But a Lack of Confidence
The survey shows that 38% of companies have a budget of more than $1M per year for AI infrastructure alone, and 59% have more than $250k per year. These huge budgets should indicate high confidence among the companies surveyed that they can get AI models into production.
However, for 77% of companies, less than half of models make it to production.
Further, 88% of companies say that they are not fully confident in their AI infrastructure set-up and aren’t sure that they can move their models to production in the timeline and budget provided.
Infrastructure Challenges Weigh Heavily on AI Teams
Lack of confidence in AI infrastructure extends to hardware utilization, with more than 80% of surveyed companies not fully utilizing their GPU and AI hardware, and 83% of companies admitting to idle resources or only moderate utilization.
Only 27% say that GPUs can be accessed on demand by their research teams as needed, with a sizable percentage of those who responded relying on manual requests for allocating compute resources.
AI Is Still a Relatively Immature Market
The top challenges for today’s AI teams are data collection (61%), infrastructure/compute (42%) and defining business goals (36%). All three of the biggest challenges are early-stage problems for teams working with AI, which speaks to market immaturity. In addition, the tools used to manage infrastructure for AI teams include home-grown tools (23%) and even Excel spreadsheets (16%), again showing that in many ways, AI still lacks maturity.
Budgets are growing, despite challenges
AI challenges are relevant across all respondents, regardless of company size, industry, AI spend, or infrastructure location (cloud, hybrid, or on-premises). Infrastructure utilization is an issue for between 85%-90% of respondents, even among companies that have $10M or more budgeted for AI each year. Despite this, most companies are not limiting their budgets until their challenges are solved, with 74% planning to increase spend on AI infrastructure in the next year.
There is strong pressure on enterprises to launch AI projects and see value from Artificial Intelligence. While the challenges may still be early-stage issues like goal setting and infrastructure set-up, the spend is far from immature. The financial support is in place to propel AI projects, but it needs to be channeled to the right places, improving the systems used for AI infrastructure management, solving hardware utilization challenges, and supporting research teams in gaining both confidence and access to resources.
These survey findings indicate that an organization’s ability to move ML models to production on time and on budget is largely dependent on effective allocation and high utilization of GPU resources. A logical next step is to evaluate existing AI orchestration tools and processes to find areas of improvement.
*👉 For enterprises seeking to solve infrastructure or compute challenges, Run:AI provides a compute resource management platform built specifically for AI workloads. With Run:AI, data scientists get access to all the pooled compute power they need to accelerate AI experimentation - whether on-premises or cloud. Run:AI’s Kubernetes-based platform provides IT and MLOps with real-time visibility and control over scheduling and dynamic provisioning of GPUs – and gains of more than 2X in utilization of existing infrastructure. Run:AI recently announced a new Researcher UI for simpler jobs submission, as well as new AI technologies, ‘Thin GPU Provisioning’ and ‘Job Swapping’ which together completely automate the allocation and utilization of GPUs, bringing AI cluster utilization to near 100% and ensuring no resources are sitting idle.