个结果，搜索词为 "mlops (machine learning operations)"
状态：免费试用Duke University
您将获得的技能: Data Management, 大数据, 云计算, 机器学习, 微软 Azure, GitHub, DevOps, 云部署, 负责任的人工智能, AWS SageMaker, 单元测试, 数据管理, GitHub 飞行员, 数据分析, 微调, Microsoft Azure, Pandas（Python 软件包）, MLOps（机器学习运营）, Python 编程, NumPy, 拥抱的脸, 模型部署
状态：免费试用Google Cloud
您将获得的技能: CI/CD, 人工智能工作流程, Google Cloud 平台, DevOps, 持续部署, 自动化, 模型培训, MLOps（机器学习运营）, Model Evaluation, 模型评估, 模型部署
状态：新状态：免费试用KodeKloud
您将获得的技能: AWS SageMaker, Model Deployment, MLOps (Machine Learning Operations), Data Governance, Data Management, Model Training, Data Security, Amazon Web Services, Application Deployment, General Data Protection Regulation (GDPR), Model Optimization, Information Privacy
状态：新状态：免费试用您将获得的技能: Model Evaluation, MLOps (Machine Learning Operations), Regression Testing, Kubernetes, Docker (Software), Feature Engineering, Containerization, Software Documentation, Data Pipelines, Git (Version Control System), Apache Airflow, Technical Documentation, Microservices, CI/CD, Unit Testing, API Design, Performance Metric, Python Programming, Version Control, Verification And Validation
状态：新状态：免费试用您将获得的技能: Model Evaluation, Model Deployment, Model Training, Model Optimization, Data Preprocessing, MLOps (Machine Learning Operations), Data Cleansing, Microservices, Data Pipelines, Feature Engineering, Data Quality, Containerization, Application Deployment, Service Level, Extract, Transform, Load, Data Transformation, System Monitoring, Machine Learning Methods, Performance Tuning, Machine Learning Algorithms
状态：新状态：免费试用您将获得的技能: Model Deployment, MLOps (Machine Learning Operations), Model Training, Amazon Web Services, Apache Airflow, Data Pipelines, Apache Kafka, DevOps, CI/CD, Apache Spark, Pandas (Python Package), Deep Learning, Machine Learning, Data Governance, Supervised Learning, Flask (Web Framework), Grafana, Python Programming, Automation, Unsupervised Learning
是什么让您今天来到 Coursera？
状态：免费试用多位教师
您将获得的技能: 预测建模, 机器学习, Transfer Learning, 负责任的人工智能, 监督学习, 决策树学习, 分类算法, 无监督学习, 人工智能, 模型培训, 迁移学习, 机器学习方法, 深度学习, 机器学习算法, Model Evaluation, 模型评估, 张力流, Jupyter, 数据伦理, NumPy, 应用机器学习, Scikit Learn（机器学习库）
状态：新状态：免费试用您将获得的技能: OAuth, Unit Testing, Apache Airflow, Fine-tuning, Model Evaluation, Data Validation, API Design, Test Driven Development (TDD), Data Integrity, Extract, Transform, Load, Multimodal Prompts, Natural Language Processing, MLOps (Machine Learning Operations), OpenAI API, Data Presentation, Hugging Face, Token Optimization, LLM Application, CI/CD, Tensorflow
状态：新状态：免费试用您将获得的技能: MLOps (Machine Learning Operations), Responsible AI, API Design, Multimodal Prompts, Data Preprocessing, Vision Transformer (ViT), Data Processing, Solution Architecture, Kubernetes, Apache Airflow, Transfer Learning, PyTorch (Machine Learning Library), Data Pipelines, CI/CD, Model Evaluation, Deep Learning, Natural Language Processing, Computer Vision, Docker (Software), Ethical Standards And Conduct
状态：新状态：免费试用您将获得的技能: Feature Engineering, Data Pipelines, Apache Airflow, MLOps (Machine Learning Operations), Service Level, AI Orchestration, Data Store, Data Transformation, Change Control, Key Performance Indicators (KPIs)
状态：新状态：免费试用您将获得的技能: MLOps (Machine Learning Operations), Feature Engineering, Model Optimization, Model Training, Workflow Management, Predictive Modeling
状态：新状态：免费试用您将获得的技能: MLOps (Machine Learning Operations), Continuous Integration, Artificial Intelligence and Machine Learning (AI/ML), Service Level Agreement, Performance Stress Testing, Performance Measurement, Performance Analysis
总之，以下是 10 最受欢迎的 mlops (machine learning operations) 课程
- MLOps | 机器学习运营: Duke University
- 机器学习操作 (MLOps)：入门指南: Google Cloud
- Deploy ML Models to Production: KodeKloud
- Gradient to Production: MLOps & Model Serving: Coursera
- Machine Learning Made Easy for Software Engineers: Coursera
- Hands-On MLOps Fundamentals for ML Engineers: KodeKloud
- 机器学习: DeepLearning.AI
- Tokens to Deployment: NLP, Language Models, & Production API: Coursera
- Multimodal Intelligence - Vision, Audio & Language in Action: Coursera
- Orchestrate, Analyze, and Evaluate ML Pipelines: Coursera
