More businesses than ever are turning toward artificial intelligence and Machine Learning Operations (MLOps) to tackle complex problems. Historically, machine learning advancements have been focused on developing first-rate models, with the actual application being left as an afterthought. In many cases, machine learning models deliver very little value to individual business requirements.
If you’re eager to ensure as many models as possible make it into production, adopting MLOps is essential. Below, we break down the key reasons why companies need to focus on MLOps.
Keep Data Science Teams Motivated
There’s no quicker way to demotivate a team than by forcing them to idle away on fruitless development projects. Rather than focus on development that never materializes into actual products, focus on engaging your teams with projects that result in models that can be actively deployed.
Your teams must see their models in action. This will not only encourage them to commit to future builds but also provide them with invaluable insights that can be used to refine future innovations. Keeping your teams motivated will also improve retention rates. Retaining key personnel for longer will ultimately benefit development, resulting in premier data science teams that give your operation the competitive edge.
Don’t Sink Resources into Dead-End Development
AI-powered solutions should only be developed to bring value to your business. Many businesses invest countless man-hours and eye-watering budgets into developing models, without ever actively deploying them. In many cases, very little value can be obtained from an undeployed model, making them a costly endeavor that simply drains resources from elsewhere in your business.
The only way to realize value from models is to put them into production. MLOps will help you ensure models are consistently deployed. With MLOps, you can develop rigid frameworks and access tools that increase the odds of deployment and enhance the value to your business.
Regulatory Compliance Made Easy
Maintaining regulatory compliance has long been the bane of many businesses. It’s also something you’re going to need to consider when working with artificial intelligence. In Europe, the introduction of GDPR has forced many businesses to rapidly respond to throttling levels of regulation. In the United States, the Algorithm Accountability Act has similar implications for commercial entities working with systems that utilize machine learning and artificial intelligence.
Machine Learning Operations makes it relatively simple to ensure you’re keeping within the confines of regulatory compliance. MLOps means you don’t have to rely on manual efforts to ensure your models are in line with the latest regulatory guidance. Instead, automation provides you with peace of mind that you’re consistently on the right side of regulation.
Autonomous Development Workflows
Without MLOps in place, data science teams regularly encounter situations where operational teams declare certain projects a dead end. For data science teams that have allocated considerable resources to a build, this situation is incredibly frustrating. In addition to wasting time and resources, it also fosters discontent between data science and operational teams.
With MLOps in place, data science teams are afforded more autonomy. They can focus on building multiple models at any given time, committing more resources to projects that have a higher chance of being deployed into production. MLOps also reduces the amount of manual work that data science teams need to commit to model maintenance.
Continuous Integration and Deployment Processes
Most of the time, it’s impossible to determine how well a model will perform until it has been deployed into production. Generally speaking, machine learning engineers need to rely on estimates to steer development. Once a model has been deployed, it can be incredibly difficult and time-consuming to retrain and repurpose it.
Having MLOps in place makes such processes easier, allowing retraining to be completed in very little time. MLOps allows teams to repeat workflows quickly, eliminating bottlenecks and allowing for continuous integration and deployment of new models.
Future-Proofed Technology Stacks
Many data science teams rely on tried and tested tools for every project. In some cases, this may be due to how well they serve an individual job. However, many engineers simply rely on the same tools because they’re familiar.
When establishing tools for use with MLOps, you need to think carefully about the technology stack you are selecting. Sticking to established tools might aid usability in the short term, but it’s likely to lead to compatibility issues down the line. As such, choosing open tools is the best way to go, allowing for full adaptability in the future.
MLOps Make it Easy to Outperform the Competition
To maintain a competitive advantage, you must ensure your models make it into production. However, simply deploying more models into production is not enough to remain competitive. Models should only be deployed when there’s a high degree of confidence in data predictions. However, such assurance is only possible if you have reliable MLOps functionality at the heart of your operation.
The Case for MLOps
MLOps have come a long way in recent years. Having a robust MLOps function at the heart of your operation is vital if you’re looking to succeed. MLOps ensures more models are deployed into production, helping you realize true business value with less wasted resources. With MLOps, you can say goodbye to dead-end development and improve communication between data science and operational teams. Furthermore, MLOps allows you to navigate the complex world of regulatory compliance, all the while maintaining a distinct edge over the competition.