MLOps: A brief introduction
Machine learning is being used by many companies and businesses to improve their products and services. A machine learning model is created after gathering the required data, fitting it on an appropriate model, and fine-tuning the hyper-parameters. However, a model is useless unless it is deployed in production.
This is where MLOps comes into play. MLOps automates the whole process of training, testing, deploying, monitoring, and maintaining machine learning models in production by utilizing various tools and technologies. MLOps strives to efficiently increase the quality of production models while also adhering to all business and regulatory requirements.
THE NEED FOR MLOPS
Previously, data sizes were manageable, and machine learning models were created on a limited scale. However, the amount of data available now is massive, giving rise to large-scale models. Furthermore, the data generated is not static and is constantly changing. This can lead to a problem known as model drift, which causes the model’s performance to deteriorate over time. Model drift can be caused by changes in seasonality, customer preferences, the addition of new products, and so on.
To address this issue, the deployed model must be tracked and monitored. Any drop in performance should trigger the process of retraining the model with new data and then deploying the retrained model.
Developing a machine learning model is only a small portion of what is required to properly implement and deploy a model in production. MLOps defines a set of principles that aids in the efficient integration of all the many parts involved in the development and deployment of machine learning models. This ensures that the quality of production model is always optimal.
MLOPS vs DEVOPS
MLOps can be seen as a combination of data engineering, machine learning, and DevOps. DevOps is centered on traditional software engineering and employs CI/CD techniques to bring high-quality software to production as quickly as possible.
MLOps applies the same principles to assist in the deployment of ML models. However, the machine learning lifecycle differs from the usual software development life cycle. Software is merely code, but machine learning models combine both code and data. Because data is dynamic in nature, it is vital to continually update and retrain the model to avoid performance loss. MLOps encompasses the full machine learning lifecycle to ensure that there is no friction and that things flow smoothly between all the components.
Components of Machine learning lifecycle:
- Exploratory data analysis (EDA)
- Data Preparation and Feature Engineering
- Model training and tuning
- Model evaluation and validation
- Model inference and serving
- Model monitoring
- Automated model retraining
CONCLUSION
As machine learning is advancing at such a rapid pace, MLOps is now more important than ever. MLOps is still a new and intriguing field, with tools and processes that are likely to evolve rapidly. However, a good place to start with MLOps would be to become familiar with technologies such as Python, Linux, Docker, Kubernetes, various cloud services, and some MLOps frameworks like Kubeflow and MLFlow.