Will MLOps make Machine learning Model Development Experimental?

Machine Learning is an emerging technology, and its market will expand to $30.6 Billion by 2024. Technology is showing an upward trend for the past few years.

It is essential to effectively manage the ML lifecycle, and MLOps is gradually evolving as a useful approach. Machine Learning Operations, MLOps is a set of methods, practices, and technologies to automatically deploy and manage ML applications in the production environments. The practice brings together the Data Science and Operations team to work in collaboration for better quality and reliability of the outcomes. The below figure shows the rising interest in MLOps in the past year

MLOps Components and Framework

MLOps cover the entire lifecycle of the ML projects and applications, beginning with the use case discovery. It is essential to understand and define the problem to map it to suitable solutions. Business analysts, operations teams, and data scientists collaborate in this phase to define, analyze, and translate the problem to resolve it using ML technologies.

Data Engineers and Scientists then acquire and prepare the datasets for modelling.

MLOps introduced continuous integration and deployment in the ML lifecycle. ML pipeline stage provides a platform for multiple experimentation and testing. Production deployment and production monitoring include seamless deployment in the choice of server with constant performance measurements.

Why does MLOps matter?

Machine Learning technologies are capable of resolving the existing user/business problems irrespective of the complexity levels. However, a solid framework is an absolute necessity to achieve the goals and objectives of ML. The automation of model creation and deployment as per the best practices under MLOps improves the release time frames. It also brings down the operational costs with better scalability of the entire lifecycle.

Constant communication and collaboration of the Operations team with the Data Science resources pave the path for continuous improvements and ease of execution.

Despite the endless possibilities with ML technologies, only 22% of the ML models could successfully deploy in the production environment. In a survey, 43% of respondents agreed to face difficulties in scaling up ML models as per the business needs. This is where MLOps comes into the picture. MLOps improves deployment with the integration of multiple languages and teams right from the model creation phase. It defines a standard process to scale up the model from development to the production stage. MLOps also includes regular updates and constant monitoring to reduce the rate of risks.

Currently, ML projects have numerous issues in the monitoring stage. Several organizations and teams do not have a consistent process to monitor the ML projects. Many Data Scientists adopt manual processes to validate and track the model performance. MLOps overcomes such issues by introducing automatic and centralized monitoring. Even if the data scientists successfully determine model decay, the regular updates and changes are not easy to implement. This is because the process is usually very resource-intensive.

MLOps lifecycle management eases out the model updates and change procedure in production. It also has the mechanisms to deal with the issues in governance. Model audit trails, traceable results, and easy upgrades come in handy with MLOps.

Benefits of MLOps

MLOps brings together the data processing teams, operations team, analysts, management, and IT teams. The collaboration improves the problem analysis and problem resolution capabilities. It also enhances the speed of development and deployment with parallel validation and testing. The following are some of the benefits possible with the use and implementation of MLOps.

Easy tracking of the resources through dataset and model registries
Machine Learning pipelines for easier integration and development for specific phases, such as training pipelines, environment pipelines, testing pipelines, etc.
Automatic scaling and management
Easier implementation of the pipelines to come up with reproducible models and consistent deliveries
Quick deployment of the models with high accuracy and reliability
Secure migration of the models from deployment to production
Seamless integration with Azure DevOps and GitHub actions to easily administer and manage the workflows
Simple re-training of the models and easier integration in the existing processes
Easy tracking and control. Of version history for effective auditing
Audit trails to maintain compliance with the regulatory requirements

MLOps has a lot in store for every team in the ML lifecycle. The benefits specific to the resources/teams are:

Data Scientists and Engineers: MLOps provides an easier, flexible, and effective way to develop, train, and manage the models. It gives an option to choose suitable frameworks, platforms, and languages for faster and reliable deployments. They also bridges the gap between the stakeholder teams so that the data science teams can focus well on the data processing, training, visualizing, and analyzing.
IT Leaders and Managers: MLOps practices provide a centralized hub for deployment and monitoring. Approval flows, production access controls, version storage, and control, etc., make it easier for the leaders and the management to carry out effective governance data drift, and proactive alerts to the stakeholders also maintain constant information sharing for better management.
Risk and Compliance Professionals: MLOps supports full version control, easy rollback to the previous versions, A/B testing for model comparison, logging, and a lot more to constantly measure the risk and compliance levels.

MLOps Solutions

MLOps solutions can be classified viz. end-to-end and custom-built solutions.

End-to-end offers fully managed services to assist the developers, data scientists, engineers, and other team members to quickly create, train, and deploy the ML models. Microsoft Azure MLOps suite is one of the popular commercial end-to-end solutions available comprising of Azure Pipelines, Azure Monitor, Azure Kubernetes Services, and Azure Machine Learning, amazon Sagemaker and Google Cloud MLOps suite are also popular options.

Custom solutions classify the MLOps pipeline in several microservices. It introduces robustness and better fault tolerance in the project lifecycle. Project Jupyter, Kuberflow, MLFlow, and Cortex are some of the popular options available.

MLOps is a new trend in the field of Machine Learning with an extensive set of capabilities and benefits. The involvement and implementation of MLOps can introduce standardization of the processes with better monitoring, deployment, testing, and management of the entire ML lifecycle.