AWS Certified Machine Learning Specialty: Modeling Competency (Intermediate Level)

Topics covered

• build and train an image classification model in SageMaker
• build machine learning (ML) solutions by selecting existing resources and launch them with a single click in SageMaker Studio
• create a SageMaker notebook to train and finetune an object detection algorithm
• define the importance of the availability of good data and data pipeline design
• define the key characteristics of good machine learning problems
• describe how object detection algorithms built on top of VGG and ResNet work to predict the objects present in the image and their confident score
• describe how to design a good output for a business problem
• describe how to use SageMaker's€™s Object2Vec algorithm that learns low dimensional embeddings of high dimensional objects
• describe hyperparameter tuning jobs in SageMaker and name recommended practices
• describe SageMaker seq2seq algorithm that takes in a sequence and generates a sequence suitable for a range of tasks
• describe the methodology behind principal component analysis (PCA) and the next level of linear learner
• describe the most challenging problems in machine learning (ML)
• evaluate the learning ability of a machine learning model and identify potential risks and biases in the dataset as well as their resulting impact
• identify how to formulate a business problem into a machine learning problem
• implement an anomaly detection system using Random Cut Forest in SageMaker
• outline how supervised algorithms can be used to forecast time series based on past data
• outline how to use Linear Learner and XGBoost (eXtreme Gradient Boosting) for classification and regression problems
• outline how to use SageMaker's most simple classification/regression algorithm named K-NN and an unsupervised algorithm to find IP usage patterns
• outline machine learning (ML) mindset and compare the ML approach to other problem-solving techniques
• outline the basics of SageMaker's Neural Topic Model and Latent Dirichlet Allocation algorithms and list their primary use cases
• practice reinforcement learning workflow with SageMaker
• recognize how to complete clustering tasks in SageMaker
• recognize the use of SageMaker's€™s semantic segmentation algorithm to predict the class of each pixel in an image and get shapes of objects
• specify how to clearly define a business problem and set success and failure criteria
• specify the factors that impact algorithm selection for a particular use case
• use the integrated capabilities in SageMaker to connect EMR clusters with SageMaker Notebooks
• work with Amazon CloudWatch to analyze real-time model performance by viewing training graphs of several performance metrics
• work with SageMaker Clarify to analyze post-training bias of machine learning models
• work with SageMaker Clarify to build explainable machine learning models
• work with SageMaker Debugger to debug, monitor, and profile training jobs in real-time and reduce costs of your machine learning models by optimizing resources
• work with SageMaker Experiments to organize, track, compare, and evaluate iterative machine learning experiments
• work with SageMaker to implement PCA and K-means algorithm for image clustering
• work with SageMaker to tune models over time and manage training and tuning costs by using Spot training
• work with training Keras/Tensorflow models with SageMaker