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所在平台: Udemy |
课程主页: https://www.udemy.com/course/aws-certified-machine-learning-engineer-associate-5-tests/
课程评论：没有评论
**AWS 机器学习工程师 (MLA-C01) 认证备考指南** 本课程提供五套高质量的练习测试，旨在帮助您自信地通过 AWS 机器学习工程师 (MLA-C01) 认证考试，并加深您对 AWS 机器学习概念、最佳实践和实施策略的理解。 **课程主题涵盖：** 1. **机器学习数据准备 (28%)** * **数据摄取：** 学习从 S3、数据库和数据湖加载数据，处理各种数据格式 (CSV, JSON, Parquet)，并使用 AWS Glue 进行高效 ETL。 * **数据清洗和转换：** 掌握预处理技术，处理缺失值和异常值，进行数据缩放和类别数据编码，并使用 AWS Glue 和 SageMaker Data Wrangler 进行转换。 * **特征工程：** 学习创建有影响力的特征，应用特征选择方法优化模型复杂度，并使用 SageMaker Processing 进行自动化特征工程。 * **数据分割和分层：** 理解数据分割技术，并通过分层抽样实现平衡的模型训练、验证和测试。 2. **机器学习模型开发 (26%)** * **选择建模方法：** 根据数据和问题类型（回归、分类、聚类）探索合适的算法，并熟悉监督学习、无监督学习和强化学习。利用 SageMaker 的内置或自定义模型获得最佳结果。 * **模型训练：** 使用 SageMaker 训练模型，优化超参数，管理分布式环境，并通过高级监控避免过拟合。 * **模型优化：** 应用 SageMaker 自动模型调优和交叉验证技术来增强模型的鲁棒性和可靠性。 * **性能评估：** 衡量和解释模型性能指标（准确率、精确率、召回率、F1 分数、AUC），并利用 Amazon SageMaker Debugger 获取见解。 3. **机器学习工作流的部署和编排 (22%)** * **选择部署基础设施：** 理解实时、批量和异步推理的选项，并在 SageMaker Endpoints 上为各种用例部署模型。 * **基础设施即代码：** 使用 AWS CloudFormation 或 AWS CDK 自动化基础设施部署，促进可扩展性和一致性。 * **持续集成/持续部署 (CI/CD)：** 使用 AWS CodePipeline 和 CodeBuild 构建 CI/CD 流水线，自动化模型版本控制，并通过 SageMaker Model Monitor 监控性能。 4. **机器学习解决方案的监控、维护和安全 (24%)** * **模型监控：** 使用 SageMaker Model Monitor 检测模型漂移，设置警报，并维护的生产模型性能。 * **优化基础设施：** 利用 SageMaker Endpoints 的自动缩放、Spot 实例和 Amazon EKS 实现成本效益高的机器学习解决方案。 * **AWS 资源安全：** 通过 IAM 角色管理访问权限，使用 AWS KMS 加密数据，并遵循 AWS 最佳实践来确保安全性。 本课程旨在模拟真实的 AWS MLA-C01 考试体验，非常适合希望在机器学习领域提升职业生涯的初学者和经验丰富的专业人士。我们精心设计的题目将帮助您掌握 AWS 机器学习认证要求的每一个领域。 **立即报名，即可获得：** * 终身访问权限，包括所有未来更新。 * 对每道题目的详细解释。 * 全面的反馈，以跟踪您的进步。 * 30 天退款保证（无条件）。
Are you preparing for the AWS Certified Machine Learning Engineer (MLA-C01) certification? Our comprehensive course of five high-quality practice tests is designed to help you pass with confidence and deepen your knowledge of AWS Machine Learning concepts, best practices, and implementation strategies.Course Topics Covered:1. Data Preparation for Machine Learning (28%)Data Ingestion: Learn to load data from sources like S3, databases, and data lakes, handle various data formats (CSV, JSON, Parquet), and use AWS Glue for efficient ETL tasks.Data Cleaning and Transformation: Master preprocessing techniques, handle missing values and outliers, scale and encode categorical data, and use AWS Glue and SageMaker Data Wrangler for transformations.Feature Engineering: Discover how to create impactful features, apply feature selection methods to optimize model complexity, and use SageMaker Processing for automated feature engineering.Data Split and Stratification: Understand data splitting techniques and apply stratified sampling for balanced model training, validation, and testing.2. ML Model Development (26%)Selecting Modeling Approaches: Explore suitable algorithms based on data and problem types (regression, classification, clustering), and get familiar with supervised, unsupervised, and reinforcement learning. Utilize SageMaker's built-in or custom models for optimal results.Model Training: Train models using SageMaker, optimize hyperparameters, manage distributed environments, and avoid overfitting with advanced monitoring.Model Refinement: Apply SageMaker Automatic Model Tuning and cross-validation techniques to enhance model robustness and reliability.Performance Evaluation: Measure and interpret model performance metrics (accuracy, precision, recall, F1 score, AUC) and leverage Amazon SageMaker Debugger for insights.3. Deployment and Orchestration of ML Workflows (22%)Selecting Deployment Infrastructure: Understand options for real-time, batch, and asynchronous inference, and deploy models on SageMaker Endpoints for various use cases.Infrastructure as Code: Automate infrastructure deployment using AWS CloudFormation or AWS CDK, promoting scalability and consistency.Continuous Integration/Continuous Deployment (CI/CD): Build CI/CD pipelines using AWS CodePipeline and CodeBuild, automate model versioning, and monitor performance with SageMaker Model Monitor.4. ML Solution Monitoring, Maintenance, and Security (24%)Monitoring Models: Detect model drift with SageMaker Model Monitor, set up alerts, and maintain production model performance.Optimizing Infrastructure: Leverage autoscaling with SageMaker Endpoints, spot instances, and Amazon EKS for cost-effective ML solutions.Security of AWS Resources: Ensure security by managing access with IAM roles, encrypting data with AWS KMS, and adhering to AWS best practices.This course is designed to simulate the real AWS MLA-C01 exam experience and is ideal for both beginners and experienced professionals looking to advance their careers in machine learning. Our expertly curated questions will help you master each domain of AWS Machine Learning certification requirements.Enroll now and get:Lifetime access including all future updatesDetailed explanations for every questionComprehensive feedback to track your progress30-day money-back guarantee (no questions asked!)
