When we talk about DevOps, many people are familiar with the concept of automating various tasks, such as development, building, testing, and deploying, to help developers work faster and deliver software to users efficiently.
But what if we apply the same concept to AI or Data Science work? This is what we call MLOps (Machine Learning Operations), which is essentially DevOps in the world of Data and AI.
DevOps To MLOps
DevOps is about automating repetitive tasks in software development to increase speed and reduce errors. Similarly, MLOps is about automating the pipeline of tasks related to Data and Machine Learning, such as
- Data Collection → Gathering data from various sources
- Data Cleaning → Cleaning the data to make it ready for use
- Model Training → Using data to train a machine learning model
- Deployment → Deploying the trained model into production
- Monitoring & Retraining → Monitoring performance and automatically retraining the model when new data is available
Why many of Companies Needs MLOps
The main benefits of MLOps include:
- Reducing Redundant Work: Tasks like Data Cleaning and Model Retraining can be automated.
- Speed of Development: The process from data collection → training → deployment → monitoring can be done faster.
- Improved Model: Quality Models can continuously learn from new data (Continuous Learning).
- Cost Reduction: Using data scientist resources efficiently allows teams to focus on strategic analysis rather than manual tasks.
Use Case Scenario
- Year 1: There may not be much data available → The model is not yet highly accurate.
- Next Year: A lot of new data becomes available → With MLOps, you can automatically retrain the model without repeating every step manually.
- Result: The model is always updated and ready for use quickly and accurately.
Conclusion
MLOps is not just a technical tool, but a strategic approach to making AI and Data Science truly valuable for business. Once the entire process is automated, organizations can deploy AI into production faster, more reliably, and at a more cost-effective rate.
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