What are the Challenges in Traditional ML Workflows?

Machine Learning Operations evolved from dealing with the challenges faced in traditional ML workflows. Here are some of the challenges faced in ML workflow:

• Collaboration: In traditional methodology, teams of different expertise work separately which restricts communication among them and can cause a lack of collaboration.
• Version Control: Version controlling becomes troublesome because it gets difficult to manage different versions of models and it becomes a hassle to keep track of changes that took place in a traditional workflow.
• Manual processes might be very time-consuming and prone to human errors.
• Automation is limited in traditional workflows.
• A lack of robust and efficient mechanisms can cause difficulty in ensuring the model’s performance.
• Security of data in the workflow is limited.

Emergence of MLOps as a Solution:

In the continuous process of evolution from the challenges faced in traditional ML workflows, we end up finding solutions that enhance MLOps, which are:

• Adopted DevOps Principles – The first step in the evolution of MLOps was incorporating the principles of DevOps and applying them to machine learning workflows that eventually increased the efficiency of the model and collaboration teams.
• CI/CD Deployment – Mainly CI/CD pipelines concept is borrowed to automate the machine learning workflow to reduce the time consumption of the Machine Learning Life Cycle right from development to production.
• Automation and Orchestration – Automation is the main focus as it is applied to all the repetitive tasks like training the models, testing, production, and monitoring whereas Orchestration makes sure that the workflow is smooth.
• Containerization – Running the production-level models in a normal environment may have high computational costs and can be a burden on the system. Containerization is a best practice to deal with this issue as it provides an isolated environment to run and deploy the applications across different environments.
• Version Control for Models and Data – Version Controls in MLOps is similar to the one in DevOps. This is applied to the Machine Learning models to observe the changes and automate the process accordingly to the change and then be released into production. One can roll back to these models wherever it is necessary.
• Monitoring and Logging – MLOps has robust monitoring and logging tools that help in detecting issues and rectifying them if required without any manual intervention.

The demand for data-related roles has been on a constant rise in recent years. The percentage of people shifting to the data industry from different technical backgrounds is growing constantly. Data Science and Analytics jobs are the highly competitive job roles that most people dream of.

However, other areas in the data industry are less known. Such is Machine Learning Operations (MLOps). Read the article till the end to learn about What is MLOps and to whom it is for.