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.
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Results for "mlops (machine learning operations)"

  • 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)

  • Status: Free Trial

    Skills you'll gain: Responsible AI, Cloud Security, Google Cloud Platform, Data Ethics, Generative AI, Information Privacy, Application Security, Artificial Intelligence, Data Security, Personally Identifiable Information, Artificial Intelligence and Machine Learning (AI/ML), MLOps (Machine Learning Operations), Tensorflow, Safety and Security, Machine Learning, Data Quality

  • Status: Free Trial

    Skills you'll gain: MLOps (Machine Learning Operations), Data Processing, Data Pipelines, Systems Design, Google Cloud Platform, Data Infrastructure, Data Architecture, Data Analysis, Security Controls, Machine Learning, Data Modeling, Cloud Security, Data Visualization, Data Migration, Data Storage Technologies, 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: Feature Engineering, Dataflow, Tensorflow, Data Processing, Applied Machine Learning, MLOps (Machine Learning Operations), Keras (Neural Network Library), Statistical Methods, Data Transformation, Machine Learning

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

  • Status: New
    Status: Preview

    Skills you'll gain: Large Language Modeling, MLOps (Machine Learning Operations), Data Import/Export, Data Processing, Applied Machine Learning, Artificial Intelligence, Data Quality, Exploratory Data Analysis, Data Analysis, Computer Vision, Natural Language Processing

  • 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: MLOps (Machine Learning Operations), Google Cloud Platform, Data Infrastructure, Machine Learning, Systems Design, Data Processing, Data Pipelines, Data Architecture, Big Data, Cloud Engineering, Data Visualization, Cloud Security, Data Storage, Scalability

  • Status: Preview

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

  • 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

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