ML/DL Best Practices: Building Pipelines with Applied Rules

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description

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.

Expected Duration (hours)
1.1

Lesson Objectives

ML/DL Best Practices: Building Pipelines with Applied Rules

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

Course Number:
it_mlmdbpdj_02_enus

Expertise Level
Intermediate