Final Exam: ML Engineer
Machine Learning | Intermediate
- 1 video | 32s
- Includes Assessment
- Earns a Badge
Final Exam: ML Engineer will test your knowledge and application of the topics presented throughout the ML Engineer track of the Skillsoft Aspire ML Programmer to ML Architect Journey.
WHAT YOU WILL LEARN
Implement scatter plots and describe the capability of scatter plots in facilitating predictionsapply data clustering models to perform predictive analysisdescribe how adopting an ai strategy requires proper expectations and buy-inrecall the critical processes that are involved in training machine learning modelsdescribe the challenges facing management when developing an ai solution and how it can impact personnel describe the common elements of an organizational ai strategydefine the predictive analytics and describe its process flow identify the business problems that can be resolved using predictive modelingdescribe the challenges facing management when developing an ai solution and how it can impact personneldescribe the human-computer collaboration automation design principle describe the human intervention automation design principledefine pearson's correlation measures and specify the possible ranges for pearson's correlationidentify the machine learning algorithm for a particular purposerecognize the value proposition of code refactoringdemonstrate the tree-based methods that can be used to implement regression and classificationdescribe the causes of technical debtdistinguish features and views of the 4+1 architectural viewidentify the actions required in layered architect designlaunch the microsoft azure machine learning studio and work with datasets, train models, and projectsdefine the security considerations involved in choosing to use a hybrid cloud strategydescribing service oriented architecture maturity and adoption levelsdesign and refine a machine learning architecture for production readinesscreate, train, and deploy simple machine learning models using the amazon sagemaker torecognize the essential stages of machine learning processes that need to be adopted by enterprisesdescribe the display status automation design principle describe the human-computer collaboration automation design principle describe the human intervention automation design principledescribe machine learning reference architecture blocksimplement refactoring techniquesbuild data pipelines that can be used for machine learning deploymentsdescribe azure machine learning tools, services, and capabilities recall the machine learning tools, services, and capabilities provided by awsuse logistic regression for predictive analyticsrecognize the predictive modeling process, including how to explore and understand data, prepare for and model data, and evaluate and deploy the modelwork with refactoring techniquesapply random forests for predictive analytics
identifying the elements of a consumer-driven contractidentify the essential stages of machine learning processes that need to be adopted by enterprisesdescribe the steps used in planning and designing machine learning algorithmsdescribe the best practices for implementing predictive modelingdescribe aws services for hybrid cloud implementationsdescribe personnel training and how an ai implementation requires trainingdescribe task runners in software design and developmentapply the phases of a machine learning projectidentify reference architectures and their capabilitiesspecify methods that can be used to manage missing values and outliers in datasetsdescribe conceptual machine learning software architectureimplement aws hybrid cloud implementation from the perspective of provisioningcompare hosting environments for on-premise, hosted, and cloud deploymentdescribe the architecture of amazon sagemaker as well as the internal aws components used in amazon sagemaker with focus on algorithm, training, and hosting servicesdefine the vm creation pipeline in the azure stackset up and work with git to facilitate team-driven development and coordination across the enterprisedistinguish the different cloud deployment modelswork with python rope to implement code refactoringdescribe the features of lex, polly, and transcribe and their roles in machine learning implementationbuild and manage machine learning pipelines with azure machine learning servicedistinguish the three categories of machine learning software development patternsuse the amazon sagemaker to create, train, and deploy simple machine learning modelsbuild generalized low rank models using h2o and integrate them into a data science pipeline to make better predictionsimplement visualization for machine learning using pythonenable ci/cd for machine learning projects with azure pipelinesidentify methods for random sampling and use hypothesis testing, chi-square tests, and correlationuse aws services for resource and deployment managementdescribe automated testing in software design and developmentdescribe dimensions of architecture to maximize benefits and minimize overhead and costsdefine and identify different hybrid cloud adoption scenarios
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