AWS Certified Machine Learning Specialty: Implementation and Operations Competency (Intermediate Level)

Topics covered

• analyze images and videos in applications to distinguish assets and extract meaningful information
• conduct A/B testing for models trained on Amazon Reviews dataset using production variants
• convert a data frame to a sparse matrix
• demonstrate how to reduce cost while training machine learning algorithms using Spot instances
• demonstrate how to run hyperparameter tuning jobs with SageMaker using Python and Amazon Reviews dataset
• deploy a machine learning model using API endpoints
• describe how to enhance applications by giving them a voice
• describe how to monitor an AWS system using CloudWatch
• describe the at rest and in-transit encryption approaches used for security in SageMaker
• distinguish between various AWS data storage services
• enumerate several built-in SageMaker algorithms
• evaluate a trained machine learning algorithm
• extract insights and patterns from unstructured text using natural language processing
• identify the advantages and disadvantages of collaborative filtering
• increase information accessibility of apps by introducing an AI-powered search engine
• increase the usability of apps by adding speech-to-text features
• monitor API usage in real-time
• outline Amazon's real-life problem formulation practice for commercial use of recommender systems
• outline how to create S3 buckets for data storage
• perform data quality checks on Amazon Reviews dataset using Python and SageMaker
• recognize the difference between various SageMaker data formats
• tackle forecasting problems using DeepAR
• work with Amazon Forecast to accurately forecast time series without any machine learning (ML) experience
• work with Amazon Ground Truth for data labeling jobs
• work with Amazon Textract to parse millions of documents in no time and integrate them with Augmented AI
• work with BlazingText for optimized text classification capabilities
• work with feature engineering and machine learning experimentations using Python and SageMaker
• work with S3 buckets to read a dataset using Python and SageMaker
• work with SageMaker data accessing approaches, including VPCs, IAM, logs, and monitoring
• work with SageMaker's built-it semantic segmentation algorithms
• work with seq2seq models in SageMaker for natural language processing (NLP)