ML Ops stands for Machine Learning Operations, and it focuses on streamlining and orchestrating process building, deployment, and monitoring machine learning models for production.
A collaborative function where Data Scientists, Data Engineers, and DevOps play a crucial role, ML Ops is highly effective in increasing model development and production.
It enables the implementation of continuous integration and deployment practices with proper monitoring, validation, and governance of ML models.
In sectors such as Oil and Gas, manufacturing units and power plants have an enormous amount of data flowing into the systems, but with many inconsistencies. In that case, many models come into play for predictive maintenance and virtual testing of the setups. When we have a multi-model environment with data preprocessing pipeline, MLOps helps provide a standardized approach to retraining and fine-tuning the models.
Visualize a simple setup from the oil and gas industry called Oil and Gas Skid.
This is a Net Oil and Gas Skid - a setup that moves Oil from one well to another to drill and extract petroleum. The Petroleum mines have Natural Gas over Petroleum oil mixed with mud and other impurities.
Modular process skids typically contain controls, electrical wiring, flanges, flowmeters, heat exchangers, instrumentation, insulation, piping, pumps, tanks, tubing, and valves.
All these components need monitoring for breakdowns. Components like heat exchangers, pumps, and valves produce data, such as temperature, pressure, volumes, velocity, fluid density, and more. These skids are also mostly connected to separator tanks, scrubbers, etc. Now, you can imagine the number of models we need for predictive maintenance.
So, if we have to do predictive maintenance for three-phase separators, we will need the following models.
- To predict water level after time t has given level of water until the time t-1. This particular model will take in time series data.
- To predict the oil level after time t has given oil level at time t-1. This model will also take time-series data.
- We need a regression model to predict the gas pressure over the tank after a given time- again Time-series data.
- We will have different models that will predict the failure of components like the valves, pumps, and nozzles using categorical and numeric data about these components.
There will be other models that for predicting mechanical failure caused due to leakages and punctures. There will also be models for virtual testing of the apparatus. These models will have options to input custom data. We need a pipeline to monitor the performance of all these models for the whole setup to work seamlessly. This is where ML Ops comes into the picture.
With a monitoring pipeline in place, we can keep a continuous watch on the changes caused in the model metrics. We can also check how the changes in data (data drift) affect the model predictions. With the right setup, we can dynamically retrain models and process data for the smooth working of the setup.
Now that’s how ML Ops can change the way of equipment monitoring in industries.
Codvo announced today that The Globee® Awards, organizers of world’s premier business awards programs & business ranking lists, has named Codvo, a winner in the 7th Annual American Best in Business.
Codvo announced today The Globee® Awards, organizers of world’s premier business awards programs & business ranking lists, has named Codvo, a winner in the 7th Annual American Best in Business Award.
Amit Verma, Managing Partner at Codvo, a leading pure-play product engineering company, has been accepted into Forbes Technology Council.