Final Exam: ML Engineer

Machine Learning    |    Intermediate
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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 predictions
    apply data clustering models to perform predictive analysis
    describe how adopting an AI strategy requires proper expectations and buy-in
    recall the critical processes that are involved in training machine learning models
    describe the challenges facing management when developing an AI solution and how it can impact personnel describe the common elements of an organizational AI strategy
    define the predictive analytics and describe its process flow identify the business problems that can be resolved using predictive modeling
    describe the challenges facing management when developing an AI solution and how it can impact personnel
    describe the Human-Computer Collaboration automation design principle describe the Human Intervention automation design principle
    define Pearson's correlation measures and specify the possible ranges for Pearson's correlation
    identify the machine learning algorithm for a particular purpose
    recognize the value proposition of code refactoring
    demonstrate the tree-based methods that can be used to implement regression and classification
    describe the causes of technical debt
    distinguish features and views of the 4+1 Architectural View
    identify the actions required in Layered Architect design
    launch the Microsoft Azure Machine Learning Studio and work with datasets, train models, and projects
    define the security considerations involved in choosing to use a hybrid cloud strategy
    describing Service Oriented Architecture Maturity and Adoption levels
    Design and refine a Machine Learning architecture for production readiness
    create, train, and deploy simple machine learning models using the Amazon SageMaker to
    recognize the essential stages of machine learning processes that need to be adopted by enterprises
    describe the Display Status automation design principle describe the Human-Computer Collaboration automation design principle describe the Human Intervention automation design principle
    describe Machine Learning reference architecture blocks
    implement refactoring techniques
    build data pipelines that can be used for machine learning deployments
    describe Azure machine learning tools, services, and capabilities recall the machine learning tools, services, and capabilities provided by AWS
    use Logistic Regression for predictive analytics
    recognize the predictive modeling process, including how to explore and understand data, prepare for and model data, and evaluate and deploy the model
    work with refactoring techniques
    apply random forests for predictive analytics
  • identifying the elements of a consumer-driven contract
    identify the essential stages of machine learning processes that need to be adopted by enterprises
    describe the steps used in planning and designing machine learning algorithms
    describe the best practices for implementing predictive modeling
    describe AWS Services for hybrid cloud implementations
    describe personnel training and how an AI implementation requires training
    describe task runners in software design and development
    apply the phases of a machine learning project
    identify reference architectures and their capabilities
    specify methods that can be used to manage missing values and outliers in datasets
    describe conceptual Machine Learning Software architecture
    implement AWS hybrid cloud implementation from the perspective of provisioning
    compare hosting environments for on-premise, hosted, and cloud deployment
    describe the architecture of Amazon SageMaker as well as the internal AWS components used in Amazon SageMaker with focus on algorithm, training, and hosting services
    define the VM creation pipeline in the Azure Stack
    set up and work with Git to facilitate team-driven development and coordination across the enterprise
    distinguish the different cloud deployment models
    work with Python Rope to implement code refactoring
    describe the features of Lex, Polly, and Transcribe and their roles in machine learning implementation
    build and manage machine learning pipelines with Azure Machine Learning Service
    distinguish the three categories of machine learning software development patterns
    use the Amazon SageMaker to create, train, and deploy simple machine learning models
    build generalized low rank models using H2O and integrate them into a data science pipeline to make better predictions
    implement visualization for machine learning using Python
    enable CI/CD for machine learning projects with Azure Pipelines
    identify methods for random sampling and use hypothesis testing, Chi-square tests, and correlation
    use AWS Services for resource and deployment management
    describe automated testing in software design and development
    describe dimensions of architecture to maximize benefits and minimize overhead and costs
    define and identify different hybrid cloud adoption scenarios

IN THIS COURSE

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