This 13-video course explores various standards and frameworks that can be adopted to build, deploy, and implement machine learning (ML) models and workflows. Begin with a look at the critical challenges that may be encountered when implementing ML. Examine essential stages of ML processes that need to be adopted by enterprises. Then explore the development lifecycle exclusively used to build productive ML models, and the essential phases of ML workflows. Recall the critical processes involved in training ML models; observe the various on-premises and cloud-based platforms for ML; and view the approaches that can be adopted to model and process data for productive ML deployments. Next, see how to set up a ML environment by using H2O clusters; recall various data stores and data management frameworks used as a data layer for ML environments; and specify the processes involved in implementing ML pipelines and using visualizations to generate insights. Finally, set up and work with Git to facilitate team-driven development and coordination across the enterprise. The concluding exercise concerns ML training processes.
ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment
discover the key concepts covered in this course
list critical challenges that may be encountered when implementing machine learning
recognize the essential stages of machine learning processes that need to be adopted by enterprises
describe the development lifecycle exclusively used to build productive machine learning models
specify the essential phases of machine learning workflows and the functional flow of each phase
recall the critical processes that are involved in training machine learning models
list the various on-premise and cloud platforms that can be used to develop and deploy machine learning projects
describe the approaches that can be adopted to model and process data for productive machine learning deployments
set up a machine learning development and deployment environment using H2O clusters
recall the various data stores and data management frameworks that can be used as a data layer for machine learning environments
specify the processes involved in implementing machine learning pipelines and using visualizations to generate insights
set up and work with Git to facilitate team-driven development and coordination across the enterprise
specify processes involved in training machine learning models, recall the prominent cloud platforms used to build and deploy machine learning projects, and set up machine learning deployment environment on AWS