Exploring MLOps? Here’s a Brief Introduction

MLOps refers to practices that help in the efficient development, testing, deployment, and maintenance of ML-based applications. While in theory, MLOps is similar to DevOps, which brings development and operations teams together, MLOps also brings data scientists and ML engineers into the mix. Data scientists collect the right data sets and ML engineers train the ML models on these data sets. Just like DevOps, the practices emphasize automation and monitoring of the entire ML lifecycle, which in turn can lead to continuous improvements in collaboration, faster time to market, and increased reliability and stability.

At the end of the day, MLOps and DevOps, are both software development practices. However, there are some major differences, largely because ML systems are remarkably different from software systems, in their development as well as their behavior.

With MLOps, organizations can get the following benefits:

• Reduced Technical Debt: In a paper, Google argues that ML systems are more susceptible to incurring technical debt because, in addition to the traditional challenges of software, these systems also have ML-specific issues. MLOps provides a standard approach to reduce ML-specific technical debt at the system level.
• Higher Productivity: Earlier, ML engineers or data scientists used to deploy ML models in production. With MLOps, they can rely on the automation experience of DevOps professionals, while they focus on their core tasks. This approach significantly reduces the burden on the team and increases productivity.
• Reduced Time to Market: By automating the model training and retraining processes, MLOps effectively implements СI/CD practices for deployment and updating of the machine learning pipelines. This expedites the release cycles of ML-based products.
• Accuracy: With automated data and model validation, real-time evaluation of performance in the production, and continuous training against fresh datasets, ML-based models improve significantly over a period.

If you are assessing your organization’s readiness for MLOps and are unsure about the different steps in the process, Google’s guide on the automation of ML pipelines can provide a head start. It advises three different approaches to MLOps depending on your organization’s maturity and requirement of ML algorithms.

• MLOps Level 0: It is recommended for organizations that have in-house data scientists that can rely on manual ML workflows. If your organization needs to update ML models less frequently (less than once a year), you can explore this approach.
• MLOps Level 1: This approach requires a pipeline for continuous training. The pipeline automatically validates data and models and triggers retraining whenever there is a performance degradation or availability of new data sets. It can be useful for businesses that need to update models based on frequently changing market data.
• MLOps Level 2: This approach goes one step further, automating both the ML training pipelines as well as the deployment of ML models in the production. It is recommended for tech-driven organizations who need to update their models on an hourly basis and also make deployments across multiple servers simultaneously.

Gathr helps software teams enhance automation and monitoring across their CI/CD pipelines with several out-of-the-box and custom apps.