MLOps Case Study

In this case study, we showcase our expertise in streamlining machine learning operations (MLOps) for enhanced efficiency. See how we’ve transformed data processes to drive data-driven success. Dive into the full case study for insights into this journey.

About the Industry

The recent years in the oil and gas industry have evolved considerably. It’s been six years since the oil price saw a decent rise in the price. Many energy firms have decided to streamline their resource portfolio, considering the disruptions occurring now and then. Commitment to delineated climate change, an initiative to drive a transformed business model, and a greater emphasis on healthier financial results have created a new arena of opportunities for the organizations. As per the current scenario analysis, the energy transition plans will see a tremendous boost due to the increasing oil prices.

About the Client

Our client is one of the world’s largest publicly traded energy service provider companies. They deploy next-gen technologies in producing high-quality chemical products. Our client has a specific governance framework that makes the business more viable and productive. The company holds excellence in producing unleaded gasoline and diesel fuel products. They have an array of service-offering products such as credit cards, gift cards, and mobile payment applications to cater to the daily needs of the customer segment. Our client has also targeted many untapped geographies across the globe for their business expansion.

The Business Challenges

  • Absence of solutions to automate the manual and redundant processes
  • Delayed deployment of ML-enabled solutions by weeks
  • Absence of solutions to automate the manual and redundant processes
  • Absence of extra features like multiple experiments execution, hyperparameter tuning, code versioning etc
  • Need for user Interface Application that interacts with the model API and makes predictions for production data
  • Retraining the Machine Learning Model in case of trivial circumstances data drift was the crucial enterprise need
  • End-to-end tracking of the entire model lifecycle was one of the significant roadblocks for our client

The Business Solution

  • Proposed an MLOps solution on Microsoft Azure cloud platform solution to develop a scalable solution
  • Modularized the project code to set up the orchestration of data engineering & data science pipelines
  • Helped the client with Service Creation, Hyperparameter Tuning, Continuous Integration, Continuous Training, Continuous Deployment & UI Application deployment pipelines for the project
  • The Hyperparameter tuning of the model using “Optuna” automated the trial-anderror process, and helped client obtain the best parameters for the BERT model
  • Developed the Dash UI Application to make predictions for production data

Key Results

  • Automated MLOps process eliminated the manual dependencies and reduced the project cycle time to a great extent
  • Streamlined the ML lifecycle from developing models to the deployment and management of ML apps
  • Auto retraining of the model critically reduced the occurrence of the problems like data drifting and prediction inaccuracies
  • Our client can now focus on building new models and conducting meaningful experiments
  • Technical documentation helped the organization in understanding the entire framework very effectively

Azure App Link

Looking for similar solutions

Talk to our Experts
Check Also

Related Case Studies