Final Exam: ML Engineer
- 1 Video | 35s
- 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
apply data clustering models to perform predictive analysisapply random forests for predictive analyticsapply the phases of a machine learning projectbuild and manage machine learning pipelines with Azure Machine Learning Servicebuild data pipelines that can be used for machine learning deploymentsbuild generalized low rank models using H2O and integrate them into a data science pipeline to make better predictionscompare hosting environments for on-premise, hosted, and cloud deploymentcreate, train, and deploy simple machine learning models using the Amazon SageMaker todefine and identify different hybrid cloud adoption scenariosdefine Pearson's correlation measures and specify the possible ranges for Pearson's correlationdefine the predictive analytics and describe its process flow identify the business problems that can be resolved using predictive modelingdefine the security considerations involved in choosing to use a hybrid cloud strategydefine the VM creation pipeline in the Azure Stackdemonstrate the tree-based methods that can be used to implement regression and classificationdescribe automated testing in software design and developmentdescribe AWS Services for hybrid cloud implementationsdescribe Azure machine learning tools, services, and capabilities recall the machine learning tools, services, and capabilities provided by AWSdescribe conceptual Machine Learning Software architecturedescribe dimensions of architecture to maximize benefits and minimize overhead and costsdescribe how adopting an AI strategy requires proper expectations and buy-indescribe Machine Learning reference architecture blocksdescribe personnel training and how an AI implementation requires trainingdescribe task runners in software design and developmentdescribe the architecture of Amazon SageMaker as well as the internal AWS components used in Amazon SageMaker with focus on algorithm, training, and hosting servicesdescribe the best practices for implementing predictive modelingdescribe the causes of technical debtdescribe the challenges facing management when developing an AI solution and how it can impact personneldescribe the challenges facing management when developing an AI solution and how it can impact personnel describe the common elements of an organizational AI strategydescribe the Display Status automation design principle describe the Human-Computer Collaboration automation design principle describe the Human Intervention automation design principledescribe the features of Lex, Polly, and Transcribe and their roles in machine learning implementation
describe the Human-Computer Collaboration automation design principle describe the Human Intervention automation design principledescribe the steps used in planning and designing machine learning algorithmsdescribing Service Oriented Architecture Maturity and Adoption levelsDesign and refine a Machine Learning architecture for production readinessdistinguish features and views of the 4+1 Architectural Viewdistinguish the different cloud deployment modelsdistinguish the three categories of machine learning software development patternsenable CI/CD for machine learning projects with Azure Pipelinesidentifying the elements of a consumer-driven contractidentify methods for random sampling and use hypothesis testing, Chi-square tests, and correlationidentify reference architectures and their capabilitiesidentify the actions required in Layered Architect designidentify the essential stages of machine learning processes that need to be adopted by enterprisesidentify the machine learning algorithm for a particular purposeimplement AWS hybrid cloud implementation from the perspective of provisioningimplement refactoring techniquesimplement scatter plots and describe the capability of scatter plots in facilitating predictionsimplement visualization for machine learning using Pythonlaunch the Microsoft Azure Machine Learning Studio and work with datasets, train models, and projectsrecall the critical processes that are involved in training machine learning modelsrecognize the essential stages of machine learning processes that need to be adopted by enterprisesrecognize the predictive modeling process, including how to explore and understand data, prepare for and model data, and evaluate and deploy the modelrecognize the value proposition of code refactoringset up and work with Git to facilitate team-driven development and coordination across the enterprisespecify methods that can be used to manage missing values and outliers in datasetsuse AWS Services for resource and deployment managementuse Logistic Regression for predictive analyticsuse the Amazon SageMaker to create, train, and deploy simple machine learning modelswork with Python Rope to implement code refactoringwork with refactoring techniques
IN THIS COURSE
1.ML Engineer36sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platformDigital badges are yours to keep, forever.