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: 机器学习, 云计算, 拥抱的脸, AWS SageMaker, 大数据, 云部署, 探索性数据分析, 微软 Azure, 数据分析, Pandas（Python 软件包）, Devops, MLOps（机器学习 Operator）, 负责任的人工智能, NumPy, 数据操作, 数据管理, GitHub, Python 程序设计, 模型部署, 集装箱化
状态：免费试用Google Cloud
您将获得的技能: 机器学习, 数据管道, 持续部署, Model Evaluation, 云部署, Google 云端平台, Devops, 模型部署, 自动化, MLOps（机器学习 Operator）, 持续集成
状态：新您将获得的技能: MLOps (Machine Learning Operations), Model Deployment, Test Data, Model Evaluation, Data Synthesis, Development Environment, Claims Processing, Performance Tuning, Computer Hardware
状态：新状态：免费试用您将获得的技能: MLOps (Machine Learning Operations), Containerization, AI Workflows, Model Deployment, Generative AI Agents, LangGraph, Artificial Intelligence and Machine Learning (AI/ML), CrewAI, BeeAI, Docker (Software), Agentic systems, Google Cloud Platform, AWS SageMaker, CI/CD, Cloud Platforms, DevOps, Azure DevOps Pipelines, Kubernetes, Applied Machine Learning, Scalability
状态：新KodeKloud
您将获得的技能: MLOps (Machine Learning Operations), Apache Kafka, Apache Airflow, Apache Spark, Extract, Transform, Load, Data Lakes, Data Pipelines, Distributed Computing, Real Time Data, DevOps, Big Data, Data Preprocessing, Feature Engineering, Model Deployment, CI/CD, Pandas (Python Package), Data Transformation
状态：新状态：免费试用您将获得的技能: Model Evaluation, Feature Engineering, Data Preprocessing, Financial Data, Predictive Modeling, Customer Demand Planning, Data-Driven Decision-Making, Exploratory Data Analysis, R Programming, Customer Analysis, Predictive Analytics, Profit and Loss (P&L) Management, Risk Modeling, Inventory Management System, Analytics, Advanced Analytics, Supply Chain Management, Business Analytics, Machine Learning, Business
是什么让您今天来到 Coursera？
状态：新状态：免费试用Coursera
您将获得的技能: Model Deployment, Unit Testing, MLOps (Machine Learning Operations), Test Driven Development (TDD), Software Testing, Scalability, Dashboard, Continuous Integration, Testability, Kubernetes, CI/CD, Performance Tuning, Software Engineering, PyTorch (Machine Learning Library), Docker (Software), Tensorflow, Containerization, Scikit Learn (Machine Learning Library), Git (Version Control System), Python Programming
状态：免费试用您将获得的技能: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Supervised Learning, Advanced Analytics, Machine Learning, Machine Learning Algorithms, Unsupervised Learning, Analytics, Random Forest Algorithm, Data Analysis, Predictive Modeling, Model Evaluation, Bayesian Network, Python Programming, Statistical Modeling, Classification Algorithms
状态：免费试用多位教师
您将获得的技能: 机器学习, 人工智能, Model Evaluation, 预测建模, 数据伦理, 无监督学习, 监督学习, Scikit-learn （机器学习库）, 张力流, 数据预处理, 深度学习, 强化学习, Jupyter, Transfer Learning, 应用机器学习, 功能工程, NumPy, 随机森林算法, 分类算法, 决策树学习
状态：新状态：预览Whizlabs
您将获得的技能: Supervised Learning, Unsupervised Learning, MLOps (Machine Learning Operations), Applied Machine Learning, Model Evaluation, AWS SageMaker, Data Preprocessing, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Cloud Computing, Azure DevOps, Model Deployment, Microsoft Azure, Amazon Web Services, Deep Learning, Data Processing
状态：新状态：免费试用Microsoft
您将获得的技能: Responsible AI, MLOps (Machine Learning Operations), Azure DevOps, Generative AI, Microsoft Azure, Model Deployment, AI Workflows, CI/CD, Version Control, Data Ethics, Artificial Intelligence, Model Evaluation, Git (Version Control System), Automation, System Monitoring, Continuous Monitoring
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), DevOps, Service Level Agreement, API Design, Performance Measurement, Performance Analysis
与 mlops (machine learning operations) 相关的搜索
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
- ML Model Development and Tracking: Hands-on Guide: KodeKloud
- AI Agents and MLOps for Production-Ready AI: Packt
- Data Engineering Essentials: KodeKloud
- Apply Machine Learning for Predictive Business Analytics: EDUCBA
- ML Production Systems: Coursera
- The Nuts and Bolts of Machine Learning: Google
- 机器学习: DeepLearning.AI
- Fundamentals of Machine Learning: Whizlabs
