MLops is the hot new cloud computing career path

ClearML, an open-source MLops platform, has released a new research report, MLops in 2023: What Does the Future Hold? In this study, we surveyed 200 US machine learning decision makers to explore key trends in machine learning and MLops (machine learning operations).

Potential vendor self-service bias aside, MLops are now widely adopted within the enterprise, according to ClearML research. His 85% of respondents said he has his MLops budget dedicated in 2022. Another 14% said they had not set a budget, but expected one in 2023.

In case you haven’t noticed, ops seem to be the new focus of cloud computing work. There are cloudops (cloud operations), finops (financial operations), devops (development and operations), and secops (security operations). You can see the trend.

There are good reasons for this. Building and deploying cloud solutions or migrating existing solutions to the cloud is a necessary task. One usually completes them. The focus then shifts to operations to keep the value of that work returning to the business. As many companies have discovered over the last few years, simply throwing things up at a public cloud provider and hoping for the best is not worth it. Ignoring operations (all operations) leads to significant cost overruns and little ROI.

MLops are a key component of the machine learning lifecycle, enabling organizations to manage and operate machine learning models in production. The MLops process ensures that models are deployed, monitored, and updated in a consistent and efficient manner, allowing your organization to reap the full benefits of machine learning. Applications that can leverage ML as a transformative differentiator can add tremendous value to your business, well beyond your investment in ML-enabled systems.

MLops is becoming the hottest career path these days due to its new reliance on AI/ML augmented business systems that drive intelligent supply chains, detect fraud, and provide marketing and sales analytics. . Of course, the excitement around ChatGPT shows the interest and potential for weaponizing AI for greater benefit, but this has really evolved over the last 20 years.

What are your main tasks related to MLops?

  • Deploying the model: Deploy machine learning models to production and make them accessible to business applications
  • Model monitoring: Evaluate model performance after deployment to ensure desired results
  • version control: Track different versions of the model as it evolves and improves over time
  • Retraining the model: Update the model with new data so that the model remains accurate and relevant when the data becomes outdated, performs poorly, or becomes skewed
  • test: Ensure the model works optimally
  • automation: Automate tasks such as model deployment, monitoring, and retraining to reduce the time and effort required to manage models, freeing up valuable resources for other tasks

I’ve done each of these tasks at some point in my career, so none of the ones I’ve listed are difficult to follow. and special training on company-specific ML systems. Then simply follow the processes and procedures to keep your ML system running and updated.

Another reason why this is now an employment issue is that machine learning systems can cause major problems for your business if they are not properly operated and maintained. These range from misguided marketing campaigns that cost millions of dollars to lawsuits stemming from the bias of machine learning systems in approving or denying family mortgages. Many things can and will go wrong. Having the right MLops talent in place can reduce risk.

Are MLops Right For You? If you’re looking for a new, high-paying career that requires ongoing training, and you’re interested in ML as a technology, this could be the most fun and lucrative job you can do right now.

Copyright © 2023 IDG Communications, Inc.

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