Search result for Machine learning engineering for production (mlops) specialization Online Courses & Certifications
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MLOps (Machine Learning Operations) Fundamentals
by Google Cloud Training- 4
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Approx. 16 hours to complete
Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact. Software Engineers looking to develop Machine Learning Engineering skills. ML Engineers who want to adopt Google Cloud for their ML production projects. Machine Learning Lifecycle...
Introduction to Machine Learning in Production
by Andrew NgTop Instructor , Cristian Bartolomé ArámburuTop Instructor- 4.8
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Approx. 10 hours to complete
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application....
Machine Learning Data Lifecycle in Production
by Robert Crowe- 4.5
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Approx. 20 hours to complete
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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills....
ML Pipelines on Google Cloud
by Google Cloud Training- 3.7
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Approx. 11 hours to complete
The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Custom components and CI/CD for TFX pipelines...
AI Workflow: AI in Production
by Mark J Grover , Ray Lopez, Ph.D.- 4.5
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Approx. 17 hours to complete
There is an introduction to IBM Watson Machine Learning. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. Security and Machine Learning Models...
