ML OPS: Make the most of machine learning
Introduction
The explosion of data has made machine learning (ML) a crucial part of an organization’s growth strategy irrespective of its size. However, the success of the ML projects depends on how well they have implemented MLOps. The revolutionary DevOps method was created to tackle the gaps in a software development cycle, similarly MLOps has come into the picture to optimize the machine learning model development cycle. MLOps, when implemented well, provides an organization with the much-needed competitive advantage.
MLOps is the fusion of machine learning and operations. Machine learning is a form of science in which computers learn from the ocean of data available and improve what they have learned on their own as more and more quality data becomes available.
Every organization has an IT or an Operations team responsible for deploying a code or a machine learning model onto the production environment. They are also responsible for maintaining the production servers. MLOps therefore brings the development and the deployment (operations) teams together.
Machine learning model development cycle
Generally, a machine learning project starts with setting the objectives and goals. Subsequently, the continuous process of collection of data begins, which is followed by cleaning of data. This is the most crucial step as the quality of data obtained has a direct effect on the performance of the ML model and the objectives of the project.
Then the entire machine learning models are developed and trained with the available data and, finally it is deployed onto the live environment. If the model fails to achieve its objectives in the production environment, then the cycle is repeated. The process does not end with deploying, the model needs to be continuously monitored.
Challenges faced by operations team
- Unlike a software or an application, the success of an ML project depends on the quality of data available. Therefore, the ML model has to be retrained frequently as both the quantity and quality of data available increases with time. Following a traditional process cycle would be time-consuming, lengthy and expensive.
- In a traditional environment, the data engineers develop ML models using diverse tools and languages. This adds another layer of complexity in the process of deployment.
Advantages of MLOPS
MLOps shortens the cycle between development and deployment, any change made by the developers in the ML model is immediately implemented by the operations team on the live environment, and a quick feedback is provided.
They improve the reliability as the models are tested on the production environment immediately rather than waiting till the end of the cycle.
Organizations fail to reap the benefits of machine learning because of the inherent communication issues that exist between the Operations and the Development teams. MLOps tries to resolve this issue by bridging the two sections together, by following best practices and automating the process at each stage.
Other benefits include:
- Changes can be implemented quickly
- Makes the entire process dynamic
- Shortens the development cycle
- Improvement in speed through automation of the processes at each stage.
- Machine Learning Pipelines are created for organizing and tracking the processes. For example, a data pipeline is built for data validation, processing. Training pipeline is built for streamlining the processes involved in forming models from data.
- With the help of MLOps, a CI/CD pipeline is created. CI/CD is the acronym of continuous integration / continuous delivery. This pipeline helps in integrating changes in machine learning models and in automating the processes involved in deploying the changes onto the production environment. Any change in the model or data activates the CI/CD pipeline.
- As mentioned above, an MLOps team combining the data scientists, operations engineers is formed for a collaborative approach.
- The machine learning model cycle does not stop with the deployment, it needs to be constantly monitored. ML models, when deployed onto the live environment, often loses their efficiency, therefore monitoring the models along with the defining KPIs of performance such as errors encountered is essential to reap the benefits of ML.
- Continuous training (CT) pipeline automates the process of training the models in a constantly changing environment.
Conclusion
As more and more organizations are taking the path of AI driven analytics it is advisable to look at leveraging MLOps to take complete advantage of the application. At Kanerika, we believe organizations should not compromise on two things when developing a ML model:
- Data preparation that entails accurate data extraction, cleaning, and analysis
- Automation of data pipelines for continuous delivery
MLOps is an innovative way to drive and put insights quickly into action. It requires a complete shift in becoming a data-driven organization and needs to be an iterative work involving developers and data scientists.
