MLOps (Machine Learning Operations)

MLOps (Machine Learning Operations) is an engineering discipline that aims to unify machine learning system development and machine learning system operations. Coursera's MLOps catalogue teaches you how to streamline and regulate the process of deploying, testing, and improving machine learning models in production. You'll learn about essential elements of MLOps such as data and model versioning, model testing, monitoring, and validation, as well as robust strategies for deploying and maintaining ML models. By the end of your learning journey, you will be able to effectively manage the ML lifecycle, understand the role of automation in MLOps, and leverage best practices to bring data science and IT operations together.
42credentials
2online degrees
170courses

Results for "mlops (machine learning operations)"

  • Skills you'll gain: Google Cloud Platform, Google Gemini, Generative AI, MLOps (Machine Learning Operations), Cloud Infrastructure, Tensorflow, Artificial Intelligence and Machine Learning (AI/ML), Big Data, Machine Learning, Predictive Modeling, Natural Language Processing

  • Status: Free Trial

    Skills you'll gain: Data Ethics, MLOps (Machine Learning Operations), Applied Machine Learning, Machine Learning, Responsible AI, Performance Measurement, Predictive Modeling, Leadership and Management, Predictive Analytics, Data Processing, Fraud detection, Business Leadership, Business Transformation, Data-Driven Decision-Making, Business Priorities, Data Science

  • Status: Preview

    Skills you'll gain: MLOps (Machine Learning Operations), Google Cloud Platform, Cloud Management, Application Lifecycle Management, DevOps, Applied Machine Learning, Continuous Deployment, Application Deployment, Automation, Data Processing, Scalability

  • Status: Preview

    Skills you'll gain: MLOps (Machine Learning Operations), Generative AI, Continuous Monitoring, Google Cloud Platform, Predictive Modeling, Responsible AI, Verification And Validation, Machine Learning

  • Skills you'll gain: MLOps (Machine Learning Operations), Generative AI, Continuous Monitoring, Predictive Modeling, System Monitoring, Google Cloud Platform, Data Ethics, Responsible AI, Machine Learning

  • Skills you'll gain: MLOps (Machine Learning Operations), Generative AI, Statistical Methods, Google Cloud Platform, Verification And Validation, Performance Testing, Machine Learning, Responsible AI

  • Skills you'll gain: Generative AI, Continuous Monitoring, MLOps (Machine Learning Operations), Business Metrics, Applied Machine Learning, Google Cloud Platform, Predictive Modeling, Verification And Validation, Responsible AI, Machine Learning

  • Status: Free Trial

    Skills you'll gain: Unsupervised Learning, Scikit Learn (Machine Learning Library), PyTorch (Machine Learning Library), Exploratory Data Analysis, Deep Learning, Microsoft Azure, Data Visualization, Regression Analysis, Predictive Modeling, Data Analysis, Image Analysis, Pandas (Python Package), Jupyter, Artificial Intelligence and Machine Learning (AI/ML), Classification And Regression Tree (CART), Data Science, MLOps (Machine Learning Operations), Machine Learning, Tensorflow, Artificial Neural Networks

  • Status: Preview

    Skills you'll gain: Google Cloud Platform, Google Gemini, Generative AI, MLOps (Machine Learning Operations), Artificial Intelligence, Cloud Infrastructure, Machine Learning, Natural Language Processing, Data Processing

  • Skills you'll gain: Google Cloud Platform, Unstructured Data, MLOps (Machine Learning Operations), Big Data, Data Pipelines, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Cloud API, Jupyter, Machine Learning, Natural Language Processing

  • Skills you'll gain: Google Cloud Platform, Unstructured Data, Tensorflow, MLOps (Machine Learning Operations), Data Pipelines, Big Data, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Jupyter, Natural Language Processing

  • Status: Free

    Skills you'll gain: MLOps (Machine Learning Operations), Continuous Deployment, Application Deployment, Tidyverse (R Package), R Programming, Dashboard, Health Informatics, Applied Machine Learning, Continuous Monitoring, Predictive Modeling, Machine Learning Methods, Docker (Software), Containerization, Application Programming Interface (API)

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