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: Real Time Data, Scalability, Data Pipelines, Applied Machine Learning, MLOps (Machine Learning Operations), Machine Learning

  • Status: Preview

    Skills you'll gain: Big Data, Data Analysis, Applied Machine Learning, MLOps (Machine Learning Operations), Machine Learning, Predictive Modeling

  • Status: Preview

    Skills you'll gain: CI/CD, Apache Airflow, Data Pipelines, MLOps (Machine Learning Operations), Google Cloud Platform, Tensorflow, Kubernetes, Metadata Management, Machine Learning Methods, Containerization, Data Processing, Data Validation

  • Skills you'll gain: Feature Engineering, Tensorflow, MLOps (Machine Learning Operations), Data Processing, Keras (Neural Network Library), Data Pipelines, Data Transformation, Machine Learning, Applied Machine Learning, Data Modeling, Statistical Methods

  • Status: New

    Skills you'll gain: Jupyter, Google Cloud Platform, MLOps (Machine Learning Operations), Computing Platforms, Machine Learning, Development Environment, Exploratory Data Analysis

  • Status: New

    Skills you'll gain: MLOps (Machine Learning Operations), Tensorflow, Google Cloud Platform, Systems Design, Applied Machine Learning, Machine Learning, Systems Architecture, Data Validation, Performance Tuning, Distributed Computing, Scalability, Data Pipelines, Debugging

  • Status: Preview

    Skills you'll gain: CI/CD, Apache Airflow, Data Pipelines, MLOps (Machine Learning Operations), Tensorflow, Continuous Deployment, Google Cloud Platform, Machine Learning Methods, Continuous Integration, Metadata Management, PyTorch (Machine Learning Library), Applied Machine Learning, Feature Engineering, Containerization, Scalability, Data Validation

  • Status: New

    Skills you'll gain: Jupyter, MLOps (Machine Learning Operations), Google Cloud Platform, Machine Learning, Integrated Development Environments, Development Environment

  • Status: Preview

    Skills you'll gain: Tensorflow, Data Pipelines, Keras (Neural Network Library), Google Cloud Platform, MLOps (Machine Learning Operations), Data Cleansing, Deep Learning, Application Deployment, Applied Machine Learning, Data Transformation, Artificial Neural Networks, Machine Learning

  • Status: Preview

    Skills you'll gain: MLOps (Machine Learning Operations), CI/CD, Google Cloud Platform, Data Pipelines, Kubernetes, Tensorflow, Metadata Management, PyTorch (Machine Learning Library), Containerization

  • Skills you'll gain: Application Deployment, Image Analysis, Google Cloud Platform, Computer Vision, Anomaly Detection, MLOps (Machine Learning Operations), Predictive Modeling

  • Skills you'll gain: Large Language Modeling, Image Analysis, Cloud Services, Applied Machine Learning, Computer Vision, MLOps (Machine Learning Operations), Artificial Intelligence, Generative AI, Natural Language Processing, Document Management, Integrated Development Environments, Data Integration, Application Deployment

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