ML/DL Best Practices: Building Pipelines with Applied Rules
Machine Learning
13 Videos | 1h 8m 48s - Includes Assessment
- Earns a Badge
This course examines how to troubleshoot deep learning models, and build robust deep learning solutions. In 13 videos, learners will explore the technical challenges of managing diversified kinds of data with ML (machine learning), and how to work with its challenges. This course uses case studies to demonstrate the impact of adopting deep learning best practices, and how to deploy deep learning solutions in an enterprise. First, you will learn the best approach for architecting, building, and implementing scalable ML services, and rules to build ML pipelines into applications. Then learners will examine critical challenges and patterns associated with deploying deep learning solutions in an enterprise. You will learn to use feature engineering to apply rules and features in an application, and how to use feature engineering to manage slowed growth, training-serving skew, optimization refinement, and complex models in ML application management. Finally, you will examine the checklists that are recommended for project managers to prepare and adopt when implementing machine learning.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
list deep learning model troubleshooting steps and recommended data and model checklists for building robust deep learning solutions
recognize machine learning technical challenges and the best practices for dealing with the identified challenges
use case studies to analyze the impacts of adopting best practices for deep learning
identify the challenges and patterns associated with deploying deep learning solutions in the enterprise
describe approaches for deploying deep learning solutions in the enterprise using case study scenarios
describe approaches for architecting and building machine learning pipelines to implement scalable machine learning systems -
specify the rules that should be applied while building machine learning pipelines into applications
identify the rules that should be applied when using feature engineering to pull the right features into applications
specify the causes of training-serving skew and the rules that should be considered to manage training-serving skew
define the rules for managing slowed growth, optimization refinement, and complex models in machine learning application management
describe checklists for machine learning projects that are to be prepared and adopted by project managers
summarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 13sUP NEXT
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2.Troubleshooting Deep Learning and Using Checklists5m 44s
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3.ML Technical Challenges and Best Practices7m
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4.Case Study to Analyze Impacts of Best Practices5m 3s
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5.Deployment Challenges and Patterns10m 59s
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6.Case Study of Deployment Approaches5m 47s
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7.Architecting and Building ML Pipelines6m 42s
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8.Rules for Building Machine Learning Pipelines4m 9s
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9.Feature Engineering Rules3m 39s
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10.Training-Serving Skew3m 19s
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11.Rules for Managing Optimization Refinement4m 16s
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12.ML Project Checklists for Project Managers4m 8s
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13.Course Summary1m 20s
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