How MLOps Enables Machine Learning Production at Scale
AI adoption remains top-of-mind for organizations. Although companies are keen to gain competitive advantage by leveraging AI to more rapidly bring innovations to market, they are often unable to see end results as quickly as they’d like.
Difficulties faced when moving models into production include cost and a lack of automation – cited by over 55% of respondents to a recent IDC study.1 The complexity of building expertise, managing multiple tools and platforms across the ML pipeline, and staying on top of an ever-expanding repository of production models are noted as further obstacles.
In a challenging economy, agility, speed, and efficiency are vital. Companies need reliable AI predictions that meet business goals so they can make informed decisions and quickly respond to change. This is why businesses are increasingly investing in machine learning operations (MLOps): IDC predicts by 2024, 60% of enterprises will have operationalized their ML workflows by using MLOps.2
What Is MLOps and How Does It Help?
MLOps combines people, processes, best practices and technologies that automate the deployment, monitoring and management of ML models into production. Through adopting MLOps practices and tools, organizations can drastically change how they approach the entire ML lifecycle and deliver tangible benefits.
The benefits of adopting MLOps tools and processes include:
- Faster time to value, and more rapid feature roll-out, through better planning and deployment practices;
- Better risk mitigation for production models through ongoing monitoring, governance, and refresh for underperforming models;
- Accelerated delivery through improved collaboration for multi-functional teams usually involved in the ML lifecycle, such as data scientists, data engineers, and IT;
- Scalable AI strategies that can support dozens or even hundreds of production models.
Should You Build or Buy an MLOps Platform?
There are key considerations when looking into MLOps. Understand how your organization works with ML – and where it should head. Identify needs regarding building, deploying, monitoring, and governing your ML models on a holistic basis.
IDC recommends treating models as source code to improve collaboration, model reuse, and tracking. Ask further questions to help your organization plan to improve efficiency and agility when working with ML models. How would it cope with scale and managing additional models? How can you best avoid duplicating effort when managing ML models across departments with different needs, and deliver more value?
Working with a vendor will be beneficial. Use a cost-benefit analysis to explore ROI and risk. Doing nothing or moving too slowly could rapidly and negatively impact your business. By contrast, injecting pace into your ML efforts can future-proof your organization and keep it ahead of the competition.
You’ll find opportunities and cost trade-offs – and clear advantages in purchasing an MLOps solution. These might include:
- more rapidly generating business returns
- better leveraging learnings
- reduced need for specialized personnel
- elastic inferences for cost management
- automatic scale across your organization
- efficient model operations from a central management system
How Is DataRobot MLOps Uniquely Positioned to Take on ML Challenges?
When you work with an established and trusted software provider, it’s important to choose one that will save you time and money, and help you efficiently and effectively deal with the many challenges that come with establishing AI projects or accelerating AI adoption. With DataRobot MLOps, you get a center of excellence for your production AI – a single place to deploy, manage and govern models in production, regardless of how they were created or when and where they were deployed.
This full suite of ML lifecycle capabilities delivers model testing, deployment and serving, performance monitoring and granular model-level insights, approval workflows, and a higher level of confidence for decisions informed by models. Data science teams can then better address challenges associated with the ML lifecycle.
Although it’s packed with features, DataRobot MLOps is also easy to use. Among its many highlights are:
- A single pane of glass management console that consolidates reporting, with easily digestible charts, workflow review, and quality metrics;
- Custom AI project governance policies, giving you complete control over access, review, and approval workflows across your organization;
- Automating much of the ML development process, including monitoring, production diagnostics, and deployment, to improve the performance of existing models;
- Running your models anywhere, through DataRobot MLOps being able to deploy your model to a production environment of choice;
- The industry leading DataRobot AutoML, which builds and tests challenger models – and alerts you and provides insights when one outperforms the champion;
- A humility feature, which configures rules to enable models that recognize in real-time when they make certain predictions;
- Detailed and user-defined insights, which let you, for example, compare drift across two scoring segments of a model, for any time period, to gain the context required to efficiently make critical decisions that keep models relevant in a fast-changing world.
MLOps is a necessity to remain competitive in today’s challenging economic environment. DataRobot MLOps helps you more rapidly take advantage of the fantastic opportunities ML brings, and efficiently and effectively manage the lifecycle of production models holistically across your entire enterprise.
1 Source: “IDC MarketScape: Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment,“ doc #US48325822 , December 2022
2 Source: “IDC FutureScape: Worldwide Artificial Intelligence and Automation 2022 Predictions,” IDC#US48298421, October 2021