Machine Learning Engineering for Production (MLOps)

开始时间: 12/21/2023 持续时间: Approximately 4 months to complete Suggested pace of 6 hours/week

所在平台: Coursera专项课程

课程主页: https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops

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课程详情

What you will learn
Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.
Skills you will gain
Managing Machine Learning Production Systems
Deployment Pipelines
Model Pipelines
Data Pipelines
Machine Learning Engineering for Production
Human-level Performance (HLP)
Concept Drift
Model baseline
Project Scoping and Design
ML Deployment Challenges
ML Metadata
Convolutional Neural Network
About this Specialization
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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.
Applied Learning Project
By the end, you'll be ready to
• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application
• Build data pipelines by gathering, cleaning, and validating datasets
• Implement feature engineering, transformation, and selection with TensorFlow Extended
• Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas
• Apply techniques to manage modeling resources and best serve offline/online inference requests
• Use analytics to address model fairness, explainability issues, and mitigate bottlenecks
• Deliver deployment pipelines for model serving that require different infrastructures
• Apply best practices and progressive delivery techniques to maintain a continuously operating production system
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Advanced Level
• Some knowledge of AI / deep learning • Intermediate skills in Python • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Hours to complete
Approximately 4 months to complete
Suggested pace of 6 hours/week
Available languages
English
Subtitles: English, French
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Advanced Level
• Some knowledge of AI / deep learning • Intermediate skills in Python • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Hours to complete
Approximately 4 months to complete
Suggested pace of 6 hours/week
Available languages
English
Subtitles: English, French

课程大纲

Course Link: https://www.coursera.org/learn/introduction-to-machine-learning-in-production

Name:Introduction to Machine Learning in Production

Description:Offered by DeepLearning.AI. In the first course of Machine Learning Engineering for Production Specialization, you will identify the various ... Enroll for free.

Course Link: https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production

Name:Machine Learning Data Lifecycle in Production

Description:Offered by DeepLearning.AI. In the second course of Machine Learning Engineering for Production Specialization, you will build data ... Enroll for free.

Course Link: https://www.coursera.org/learn/machine-learning-modeling-pipelines-in-production

Name:Machine Learning Modeling Pipelines in Production

Description:Offered by DeepLearning.AI. In the third course of Machine Learning Engineering for Production Specialization, you will build models for ... Enroll for free.

Course Link: https://www.coursera.org/learn/deploying-machine-learning-models-in-production

Name:Deploying Machine Learning Models in Production

Description:Offered by DeepLearning.AI. In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ... Enroll for free.

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