Why an MLOps solution can accelerate your business in 2021
When asked about the infrastructure they’re using to support ML, respondents reported improved outcomes when they use third-party machine learning operations (MLOps) solutions to support their infrastructure needs.
This post explores the trend in detail, and what it means for enterprises as they head further into 2021.
A wide range of infrastructure approaches—and needs
It’s 2021, and infrastructure needs are more complex than ever before. Our survey revealed that 71% of all organizations have hybrid environments, and a full 42% have hybrid environments with both cloud and on-premises solutions. This is a significant increase from the 16% that reported such hybrid cloud and on-premises environments just the previous year.
So how are organizations handling these increasingly complex infrastructure needs? In our survey, respondents reported using a wide range of approaches to support infrastructure for model deployment and management:
- Building and maintaining their own system from scratch
- Integrating open-source components into a system that’s maintained in-house
- Integrating commercial point solutions into a system that’s maintained in-house
- Using a third-party platform supported by a vendor
In the early days of machine learning, option #1 was essentially the only option available to organizations that wanted to invest in ML. To get started, they basically had to build and maintain their own infrastructure from scratch to deploy and manage models. This limited ML to the select few businesses that had the resources to do this.
As the market has matured, the barrier to entry has gotten lower, and organizations now have a range of commercial tools available to them to support their ML infrastructure needs. When we look at options #3 and #4, this is what we see. Organizations that either integrate commercial point solutions into their systems or use a third-party platform don’t have to build a solution themselves. They’ve purchased a solution from a vendor that supports their ML infrastructure needs.
In other words, they’ve invested in a machine learning operations (MLOps) solution.
And when compared to those that build and maintain their own systems from scratch, these organizations are spending an average of 19-21% less on infrastructure costs, 31% fewer days to put a trained model into scaled production, and a smaller percentage of their data scientists’ time on model deployment.
What is MLOps and why do you need an MLOps solution?
MLOps is a set of tools and processes for delivering ML at scale by leveraging your existing software development lifecycle (SDLC). Simply put, it’s what enables organizations to scale their production capacity to a point of generating significant business value and delivering results. MLOps solutions help organizations deploy and manage ML models at scale, supporting their unique infrastructure needs while accelerating time-to-value for ML and protecting their businesses.
2021 is an important year to be investing in machine learning. Our survey revealed that organizations are realizing the potential of AI and ML to deliver top- and bottom-line impacts to their businesses during a time of economic uncertainty. Year-on-year, 83% of organizations have increased their budgets for AI and ML and the average number of data scientists employed has increased by 76%.
The data tells us that MLOps solutions can improve outcomes—and that 2021 will be an important year for ML. So, whether you have 100 models in production or are just getting started, you should look for ways to support your operational needs with MLOps solutions this year. Doing so will help you free up valuable resources to focus on what really matters: Growing your business.
Discover all 10 enterprise ML trends
This is only one of the trends that we discovered in our survey of 400+ business leaders. Check out the full report today to explore all 10 trends we uncovered in our research. 2021 will be a crucial year for ML initiatives—set yourself up for success by understanding where the industry is headed and how you can make the most of it.
More from the ML trends blog series
- ML trend: Enterprises are dialing up their machine learning investments for 2021
- Why you should pay off your technical debt for machine learning in 2021
- Why governance should be a crucial component of your 2021 ML strategy
- ML trend: I&O leaders are the most common decision-makers in cross-functional ML initiatives
- New report: Discover the top 10 trends in enterprise machine learning for 2021