Whether you are starting your AI engineering journey or expanding your existing skills, MammothClub offers a practical catalog of courses, paths, and focused learning opportunities.
Test your knowledge and prepare for the certification exam with these comprehensive mock exam questions!
Test your knowledge and prepare for the certification exam with these comprehensive mock exam questions!
Test your knowledge and prepare for the certification exam with these comprehensive mock exam questions!
Featuring 48 questions and unlimited attempts! Test your knowledge on best practices of an MLOps strategy, advantages of using ML runtimes, how AutoML facilitates model and feature selection, and the advantages AutoML brings to the model development process.
Featuring 50 questions and unlimited attempts! Test your knowledge on the benefits of creating feature store tables at the account level in Unity Catalog vs at the workspace level, how to create a feature store table in Unity Catalog, write data to a feature store table, train a model with features from a feature store table, and score a model using features from a feature store table.
Featuring 48 questions and unlimited attempts! Test your knowledge on the differences between online and offline feature tables, how to identify the best run using the MLflow Client API, manually log metrics, artifacts, and models in an MLflow run, and identify information available in the MLflow UI.
Featuring 48 questions and unlimited attempts! Test your knowledge on how to compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries, remove outliers from a Spark DataFrame based on standard deviation or IQR, create visualizations for categorical or continuous features, and compare two categorical or two continuous features using the appropriate method.
Featuring 50 questions and unlimited attempts! Test your knowledge on using ML foundations to select the appropriate algorithm for a given model scenario, identifying methods to mitigate data imbalance in training data, comparing estimators and transformers, developing a training pipeline, and using Hyperopt's fmin operation to tune a model's hyperparameters.
Build eCommerce decentralized apps with AssemblyScript and JavaScript!
