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)"

  • Skills you'll gain: Fine-tuning, MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, GitHub Copilot, Unit Testing, Data Engineering, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

  • Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Google Cloud Platform, Model Evaluation, Model Training, DevOps, Cloud Deployment, Devops Tools, Continuous Deployment, CI/CD, AI Workflows, Automation

  • Status: New

    Skills you'll gain: Model Deployment, Databricks, MLOps (Machine Learning Operations), Cloud Deployment, Feature Engineering, Apache Spark, Model Training, AI Workflows, Machine Learning Software, Data Lakes, CI/CD, Model Evaluation, Applied Machine Learning, Data Store, Machine Learning, Development Environment, Statistical Machine Learning, Data Science, Machine Learning Algorithms, Python Programming

  • Status: New

    Skills you'll gain: MLOps (Machine Learning Operations), Model Evaluation, Model Deployment, AI Orchestration, AI Workflows, Generative AI, Google Cloud Platform, Data Modeling, Continuous Monitoring, Data Pipelines, Model Training, Feature Engineering, Model Optimization, DevOps, Agentic Workflows, Generative AI Agents, Cloud Deployment, Devops Tools, Data Store, Continuous Deployment

  • Status: New

    Skills you'll gain: Model Deployment, AWS SageMaker, MLOps (Machine Learning Operations), Serverless Computing, Google Cloud Platform, Cloud Deployment, AI Integrations, Docker (Software), Cloud Platforms, Containerization, Cloud Computing, Application Deployment, Amazon Web Services, Restful API, DevOps, CI/CD, Microsoft Azure, Public Cloud, Microservices, Devops Tools

  • Status: New

    Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Data Architecture, Model Training, Apache Airflow, Data Pipelines, Apache Kafka, DevOps, CI/CD, Apache Spark, Pandas (Python Package), Deep Learning, Data Governance, Machine Learning, Supervised Learning, Flask (Web Framework), Grafana, Python Programming, Unsupervised Learning, Automation

  • Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Data Modeling, Google Cloud Platform, Feature Engineering, Model Training, Data Store, Data Preprocessing, Data Processing, Data Management, Data Storage Technologies

  • Duke University

    Skills you'll gain: MLOps (Machine Learning Operations), GitHub Copilot, Responsible AI, Model Deployment, Containerization, Web Frameworks, Rust (Programming Language), AI Workflows, DevOps, Hugging Face, Applied Machine Learning, Cloud Solutions, Cloud-Native Computing, Machine Learning, Serverless Computing, Application Deployment, GitHub, Command-Line Interface, Big Data

  • Status: New

    Skills you'll gain: Feature Engineering, Model Evaluation, Model Deployment, Fine-tuning, Data Preprocessing, Model Training, Deep Learning, Machine Learning Methods, Model Optimization, Scikit Learn (Machine Learning Library), PyTorch (Machine Learning Library), Scalability, Hugging Face, Docker (Software), Supervised Learning, Machine Learning Algorithms, MLOps (Machine Learning Operations), Applied Machine Learning, Software Development, Machine Learning

  • Skills you'll gain: MLOps (Machine Learning Operations), Data Preprocessing, AWS SageMaker, Model Evaluation, Model Deployment, Artificial Intelligence and Machine Learning (AI/ML), Amazon Web Services, Model Training, Predictive Modeling, Machine Learning Methods, Applied Machine Learning, Data Processing, Machine Learning, Supervised Learning, Machine Learning Algorithms, Unsupervised Learning, Classification Algorithms

  • Skills you'll gain: Pandas (Python Package), MLOps (Machine Learning Operations), NumPy, Unit Testing, Model Deployment, Data Manipulation, Test Script Development, Software Testing, Data Import/Export, Applied Machine Learning, Test Automation, Data Wrangling, Python Programming, Code Reusability, Data Processing, Debugging, Data Structures, Machine Learning, Object Oriented Programming (OOP), Scripting

  • Skills you'll gain: MLOps (Machine Learning Operations), Continuous Delivery, Applied Machine Learning, Cloud Engineering, Google Cloud Platform, Model Deployment, Cloud Applications, Cloud API, Cloud Deployment, Machine Learning, Microsoft Azure, Model Training, AI Workflows, Software Engineering, Computer Vision, Application Programming Interface (API)