Building Your Projects with MLOps in
Data Science & Analytics

Building Your Projects with MLOps in

Rosemary Jackson  •  

High Customer-Centricity:The customer is all in and is there to make this technology frictionless in its adoption. Up and running quickly to secure rapid, accurate decisions, becomes your pivot point providing expert knowledge, superior experience, and clear direction in completing your company’s business transition.

Data Pipeline Development: Extract, transform, and load. The Pipeline incorporates a machine learning transformation into the pipeline, training and serving data in real time using core practices from DevOps protocols. A data pipeline makes machine learning models reliable and easy to manage.

Cross-functional Inter-app Engagement: The data pipeline is built to handle input from all your teams including geos, reservoir engineers, completions engineers, and finance. The platform pulls these inputs together to facilitate tradeoff decision-making.

Technology Has No Endgame: is an ever-green platform changing with technological advancements including digital and new science discoveries. The platform is built to be adaptable and agile, robust and accurate, timeless and timely. Digital velocity and geomechanical constraints live at the heart of this machine learning software.

Machine Learning Operations, MLOps is the technology that will define both AI and the O&G Industry, as machine learning models are merged with ever-changing data into a collaborative, robust and agile prediction tool. Where DevOps provided a process to deploy software into a business production environment, MLOps takes the moving pieces of training ML models and a dynamic data pipeline and strategically deploys the system into the business production environment. uses MLOps to achieve a high level of predictive accuracy with well trained ML models and a complete understanding of data collection, data aggregation, and data deployment. believes that the adoption of technology like the platform requires four main pieces: High customer-centricity to fully integrate the ML platform, cross-functional inter-app engagement to pull together your teams and their inputs to create tradeoffs, data pipeline development to fulfill your specific business needs, and always moving you forward because technology has no endgame. Onsite company success is dependent upon those four aspects being realized so that where-to-drill next decisions are managed within the new pace of Machine Learning Operations.

The pace of change in the business technology realm is important to understand. O&G exists primarily in the traditional world where “business is anchored firmly on the ground with changes in business models being reliant on Traditional technology that takes quarters and years to implement.”

But that speed was yesterday.Today, in businesses that use digital technologies, change occurs in weeks or even days. And an even faster future is almost here, where new technologies emerge like MLOps and adoption of change will be breathless by traditional standards. What can be done will be the catalyst to company strategy and daily analysis.’s history is the story of rapid technology adoption in the pursuit of frictionless platforms that allow O&G companies to make rapid digital transitions.

We started 7 years ago in state-of-the-art Business Intelligence template construction. We saw the limitations. We moved on to a DevOps model where we created a software platform with cloud deployment and no BI dependency. We saw we could do even more.

MLOps has accelerated the Artificial Intelligence approach to unconventional well spacing prediction modeling, into the creation of a platform with apps like Pad Designer that have a 95% accuracy. This is an extraordinary feat in a marketplace where “only 22 percent of companies using machine learning have successfully deployed a model” and “translating [those models] from science projects into reliable, scalable software that brings businesses value is still hard.”

In a recent Forbes article entitled, “Why MLOps is Critical to the Future of Your Business,” Dale Markowitz from Google noted that with the MLOps process “we are making AI more accessible and useful by:

  • Shortening development cycles, and as a result, decreasing time to market
  • Improving collaboration between teams across all levels of technical expertise
  • Increasing reliability, performance, scalability, and security of ML systems
  • Streamlining operational and governance processes
  • Increasing return on investment of ML projects.”

Adoption of technology at this dizzying pace is a difficult transition for industries like O&G. But rapid market fluctuations, scarce investor capital, and the shorter-term nature of unconventional wells means that the decision-making speed and ability to predict need a tighter more-timely timeframe. And certainty. How can you make that change? is ready to transform the industry to the MLOps new reality—unique customer interfaces, teams and their specific models integrated, and always future-casting to tomorrow’s technologies. It’s our determination to help you build with personalized models that drive accurate, reliable and scalable business decisions in this complex unconventional environment of difficult productivity tradeoffs.

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