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From ML-"Oops" to MLOps for Energy

Updated: Oct 4, 2023

At Activera Consulting, we've recently connected and brainstormed with one of our ecosystem partners, Domino Data Lab, to uncover what we collectively see in the Energy Industry regarding Machine Learning Operations (MLOps) pain points.

Energy companies have a unique make-up of employees compared to other industries. The helicopter view is that there's a high percentage of engineering talent, significant unstructured data, and a federated system of business units, given the global nature of these multinational corporations. These facts can exacerbate technology and analytics teams' challenges in delivering value through data science.

Let's take a look at each of these problems in more depth and then discuss how an effective MLOps Platform can work to resolve:


Citizen Development

Problem: Energy companies have a high percentage of engineering talent. These engineers are trying to build and use machine learning applications. Still, they need help accessing required computing or determining the proper requirements for executing jobs on the right infrastructure.

Solution: Engineering talent needs a product that can effectively abstract away the complexity of and provide access to CPU, GPU, and distributed computing (such as Spark, Ray, Dask, etc.) and centralize it under one System of Record platform for audibility, reproducibility, and reusability.


High-Performance Computing

Problem: Energy companies, especially in the Upstream sub-surface space, have substantial 'heavy' seismic data that makes Cloud optimization challenging. Our collective experiences show a recent trend of repatriating Cloud resources on-premises for these types of workloads, given the cost-savings you can realize from a local GPU cluster.

Solution: An MLOps Platform should support multi-cloud environments for connecting to any Cloud provider, region (data sovereignty), and on-prem. The bottom line is that it shouldn't matter where your data is or where you want to perform your computations.

Additionally, your MLOps Platform should have the capability of showing you which computer is supportive of your workload and provide visibility into Cloud costs for effective processing & savings.


Federated Connectivity for Generative AI

Problem: Given their distributed nature, energy companies typically have federated business units and functions - there are so many data sources, and it's challenging to extract/vectorize that data to train industry-specific Large Language Models (LLMs) for generative AI benefit.

Solution: An MLOps Platform should have the versatility for model connection to any data source. When combined with something like Trino (enterprise-ready query engine), you can merge this data into a single connection point and access it by your MLOps Platform without migration into a Data Lake, saving time and money.


These three challenges are the tops for Energy, but there are several additional pain points across all analytics teams. Other problems include more standardization across projects, ineffective model monitoring, little-to-no model reusability, and increased time-to-value, given the inability to put models into production quickly. A good MLOps Platform should solve these issues too.


Another elephant-in-the-room challenge is the cost/time of implementing an MLOps Platform. Our research shows that the typical payback period falls in the 5-to-6-month range, and an average 3-year ROI can be upwards of 600-to-700 percent.



That's not to mention the intangible benefits of employee morale and productivity.

If your analytics team has grown to double-digits, or you expect that growth in the coming months, it behooves you to start thinking about scale.


Or, if you're in an engineering-heavy industry with intelligent folks who like to tinker with Python and ML, it might be best to support them with a comprehensive solution or risk shadow systems being stood-up. In either case, an effective ML Ops Platform can answer your woes.

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