MLOps: How to Accelerate Your Machine Learning Projects with Automation

MLOps automation is advancing machine learning projects through the deployment of efficient ML model pipelines.

This advancement stems from three phases of a successful ML pipeline automation - the creation of workable models, automated data pipelines and an automated CI/CD system.

The unprecedented growth of AI and ML in the last decade can be attributed to the rise of technological advancements, and although their culmination is yet to be reached, enterprises are leveraging these technologies to create favorable business outcomes through ML projects. However, according to Gartner, 85% of these ML projects exhibit a stunted production stage due to several reasons. Enterprises must take the initiative in accelerating ML projects, which can be achieved by delegating their ML models to MLOps automation. Let’s explore the potential that MLOps automation unlocks in the seamless execution of ML models and pipelines.

MLOPS AUTOMATION IN ACCELERATING ML PROJECTS

Data scientists work in silos, detached from engineering and DevOps teams, and rely on manual development processes, such as Jupyter books, which are later converted manually into production-ready ML pipelines. This entails separate teams of ML engineers, DevOps, data engineers and developers to oversee the processes, investing additional time and resources. Moreover, any change pertaining to data preparation and model training will imply a repetition of the entire cycle.

MLOps automation eliminates such shortcomings and enables data scientists and ML engineers to run the production process of ML models efficiently and actively. If any update interrupts the lifecycle, the pipeline updates automatically and provides the best model services to the user. MLOps automation is integrated into ML pipelines, and divided into three phases:

Creation of Workable Models

The initial production and deployment process of the ML model after the adoption of ML is manual. This phase allows engineers and data scientists to focus on the deployment of the trained model, which is a script-driven and interactive process. Data experts assess, analyze and develop experimental codes to create workable models as a predictive service.

Automated Data Pipelines Through MLOps

Once the preliminary stage of model establishment concludes, ML pipeline automation is executed. Automation enables the continuous delivery of model results from data collection, analysis and validation, which accelerates the experimentation process. MLOps also facilitates metadata management, the repository of features, data and model validation and more. Furthermore, MLOps unifies DevOps and modularizes codes of components and pipelines. This generates reproducible codes and continuous delivery of prediction services.

Automated CI/CD System

The seamless automation of CI/CD in ML models is critical for authentic and continuous updates in the production environment. Automated CI/CD systems enable data experts to produce better ideas surrounding feature development, model architecture and more. From enabling perpetual experiments around ML algorithms to the deployment of new pipeline components, this stage ensures newer implementations in pipeline production. Moreover, the integration of automated triggers assists in the execution of pipelines, and finally, the data experts monitor the performance of the model in real-time.

MLOps is a critical component that can drive the success of numerous ML and AI projects and enterprises should grab this opportunity to drive successful business outcomes.