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.
38credentials
2online degrees
169courses

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Results for "mlops (machine learning operations)"

  • Status: New
    Status: Free Trial

    Skills you'll gain: Generative AI Agents, Automation, No-Code Development, Prompt Engineering, MLOps (Machine Learning Operations), Process Optimization, System Monitoring, Performance Analysis, Performance Management

  • Status: Free Trial

    Skills you'll gain: Large Language Modeling, Data Management, Data Validation, Data Cleansing, Natural Language Processing, MLOps (Machine Learning Operations), Data Transformation, Verification And Validation, Data Quality, Performance Tuning

  • Status: Free Trial

    Skills you'll gain: MLOps (Machine Learning Operations), Data Processing, Data Pipelines, Database Development, Data Infrastructure, Data Analysis, Google Cloud Platform, Data Architecture, Data Visualization, Data Modeling, Machine Learning, Data Warehousing, Cloud Security, Scalability

  • Status: Preview

    Skills you'll gain: Responsible AI, AI Product Strategy, Stakeholder Management, Project Management, Data Ethics, Workforce Management, Risk Mitigation, Project Management Life Cycle, Agile Methodology, Risk Analysis, Artificial Intelligence, Team Management, MLOps (Machine Learning Operations), Team Building, Scalability, Diversity and Inclusion, Budget Management

  • Skills you'll gain: MLOps (Machine Learning Operations), Time Series Analysis and Forecasting, Google Cloud Platform, Forecasting, Feature Engineering, Data Transformation, Predictive Modeling, Applied Machine Learning, Data Pipelines, Deep Learning

  • Status: Preview

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

  • Skills you'll gain: Feature Engineering, Dataflow, Tensorflow, Data Processing, MLOps (Machine Learning Operations), Data Pipelines, Keras (Neural Network Library), Data Transformation, Dimensionality Reduction, Machine Learning

  • 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: CI/CD, Google Cloud Platform, Apache Airflow, MLOps (Machine Learning Operations), Data Pipelines, Tensorflow, Kubernetes, Metadata Management, Scikit Learn (Machine Learning Library), Containerization

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

  • 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: 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