Final Exam: ML Engineer


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level


Overview/Description

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.



Expected Duration (hours)
0.0

Lesson Objectives

Final Exam: ML Engineer

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

    • Course Number:
    it_femla_03_enus

    Expertise Level
    Intermediate