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Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.
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高級
完成課程大約需要 4 個月
建議進度:5 小時/週
英語(English)
可分享的證書
完成後獲得證書
100% 在線課程
立即開始,按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
高級
完成課程大約需要 4 個月
建議進度:5 小時/週
英語(English)

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此專項課程包含 4 門課程

課程1

課程 1

Introduction to Machine Learning in Production

4.8
558 個評分
109 條評論
課程2

課程 2

Machine Learning Data Lifecycle in Production

4.4
115 個評分
16 條評論
課程3

課程 3

Machine Learning Modeling Pipelines in Production

4.7
19 個評分
2 條評論
課程4

課程 4

Deploying Machine Learning Models in Production

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deeplearning.ai

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