ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment
Machine Learning
| Intermediate
- 13 videos | 1h 4m 16s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
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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
IN THIS COURSE
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1m 51s
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6m 20sAfter completing this video, you will be able to list critical challenges that may be encountered when implementing machine learning, as well as how to overcome them. FREE ACCESS
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3. Machine Learning Process Stages9m 50sUpon completion of this video, you will be able to recognize the essential stages of machine learning processes that enterprises need to adopt. FREE ACCESS
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4. Machine Learning Development Lifecycle5m 22sAfter completing this video, you will be able to describe the development lifecycle used to build productive machine learning models. FREE ACCESS
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5. Machine Learning Workflow6m 59sAfter completing this video, you will be able to specify the essential phases of machine learning workflows and the functional flow of each phase. FREE ACCESS
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6. Machine Learning Training Process4m 54sUpon completion of this video, you will be able to recall the critical processes involved in training machine learning models. FREE ACCESS
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7. Machine Learning Platforms5m 16sAfter completing this video, you will be able to list the various on-premise and cloud platforms that can be used to develop and deploy machine learning projects. FREE ACCESS
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8. Machine Learning Data Modelling and Processing3m 53sUpon completion of this video, you will be able to describe the approaches that can be adopted to model and process data for productive machine learning deployments. FREE ACCESS
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9. H2O Machine Learning Environment3m 29sLearn how to set up a machine learning development and deployment environment using H2O clusters. FREE ACCESS
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10. Data Source Management4m 5sUpon completion of this video, you will be able to recall the various data stores and data management frameworks that can be used as a data layer for machine learning environments. FREE ACCESS
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11. Machine Learning Pipeline4m 2sUpon completion of this video, you will be able to specify the processes involved in implementing machine learning pipelines and using visualizations to generate insights. FREE ACCESS
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12. Git Code Movement5m 45sFind out how to set up and work with Git to facilitate team-driven development and coordination across the enterprise. FREE ACCESS
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13. Exercise: Machine Learning Training Processes2m 32sAfter completing this video, you will be able to specify processes involved in training machine learning models, recall the prominent cloud platforms used to build and deploy machine learning projects, and set up a machine learning deployment environment on AWS. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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