MLOps: Are businesses repeating the same DIY mistakes?
Translator | Cui Hao
Reviewer | Sun Shujuan
Opening
- Inability to hire ML engineers quickly: The demand for ML engineers has become very strong , job openings for ML engineers are growing 30 times faster than IT services. Sometimes having to wait months or even years for positions to be filled, MLOps teams need to find an efficient way to support more ML models and use cases without increasing the headcount of ML engineers to meet the demand for ML applications. But this step introduces a second bottleneck...
- There is a lack of repeatable, scalable best practices for deploying models no matter where and how they are built: the modern enterprise data ecosystem The reality is that different business units use different data platforms based on their data and technology requirements (for example, product teams may need to support streaming data, while finance needs to provide a simple query interface for non-technical users). In addition, data science also requires decentralizing applications across business units rather than centralizing applications. In other words, different data science teams have a unique set of model training frameworks for the use cases (domains) they focus on, which means that a one-size-fits-all training framework cannot be established for the entire enterprise (including multiple departments/domains) of.
Translator Introduction
Cui Hao, 51CTO community editor and senior architect, has 18 years of software development and architecture experience and 10 years of distributed architecture experience. Formerly a technical expert at HP. He is willing to share and has written many popular technical articles with more than 600,000 reads. Author of "Principles and Practice of Distributed Architecture".
Original title:MLOps | Is the Enterprise Repeating the Same DIY Mistakes?
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