Amid the popularity of ChatGPT, MLops spending will surge in 2023 as leaders increase investments in machine learning. Cloud pros should take a look.
ClearML, an open source MLops platform announced its new research report: “MLOps in 2023: What Does the Future Hold?” This study polled 200 machine learning decision-makers in the United States, examining key trends in machine learning and MLops (machine learning operations).
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Putting the potential vendor self-serving bias aside for now, ClearML’s study found that MLops now enjoys wide-scale adoption within enterprises; 85% of respondents said they had a dedicated MLops budget in 2022. And 14% said they did not have budgets in place but expected they would in 2023. So, businesses are going to MLops now or soon.
In case you hadn’t noticed, ops seems to be the new focus of cloud computing work. We have cloudops (cloud operations), finops (financial operations), devops (development and operations), and secops (security operations). You can see the trend.
This is for good reason. Building and deploying cloud solutions or migrating existing solutions to the cloud are needed tasks. Normally, they are one and done. Then the focus shifts to operations to keep the value of that work coming back to the business. As many enterprises found out in the past few years, just tossing stuff out on a public cloud provider and hoping for the best doesn’t return value. Neglecting operations—all operations—leads to huge cost overruns and little ROI.
MLops is a critical component of the machine learning life cycle, enabling organizations to manage and operate machine learning models in production. MLops processes ensure that models are deployed, monitored, and updated in a consistent and efficient manner, enabling organizations to reap the full benefits of machine learning. Applications that can leverage ML as an innovative differentiator can add a huge amount of value to the business, well beyond the investment in the ML-enabled systems.
MLops is becoming the hottest career path lately due to the new reliance on AI/ML-augmented business systems that drive intelligent supply chains, detect fraud, and provide marketing and sales analytics. Of course, we need only look at the excitement around ChatGPT to see the interest and the potential of weaponizing AI to bring larger profits, but this has really been evolving for 20 years.
What are the major tasks involved with MLops? What would you be working on day to day if you moved to an MLops-related job?
- Model deployment: deploying machine learning models in a production environment, making them accessible to business applications
- Model monitoring: evaluating the model’s performance once it’s deployed to ensure it delivers the desired results
- Versioning: keeping track of different versions of the models when they evolve and improve over time
- Model retraining: updating the model with new data to ensure it remains accurate and relevant when the data becomes stale, declines in performance, or shows bias
- Testing: ensuring that a model performs optimally
- Automation: automating tasks such as model deployment, monitoring, and retraining to reduce the time and effort required to manage models and to free up valuable resources for other tasks
Having done each of these tasks at some point in my career, nothing I’ve listed is that hard to figure out. Usually, MLops is part of the existing cloudps team, but it will require special training in machine learning in general, as well as on the company’s specific ML systems. Then it’s just a matter of following processes and procedures to keep the ML system running and updated.
Another reason this is becoming a hot employment ticket right now: If machine learning systems are not properly operated and maintained, the business can experience major problems. These can range from a misdirected marketing campaign that loses millions of dollars, to lawsuits stemming from a bias in a machine learning system that approves or denies families for home loans. A lot can go wrong and will go wrong. Having the right MLops talent in place will lower the risk.
Is MLops right for you? If you’re looking for a higher-paying career that requires some new and ongoing training, and you’re interested in ML as a technology, this could be the most fun and lucrative job you can get right now.
By: David Linthicum
Source: InfoWorld
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