ML/DL Best Practices: Building Pipelines with Applied Rules

Machine Learning    |    Intermediate
  • 13 videos | 1h 3m 18s
  • Includes Assessment
  • Earns a Badge
Rating 4.8 of 4 users Rating 4.8 of 4 users (4)
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

  • 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

  • 1m 13s
  • 5m 44s
    After completing this video, you will be able to list deep learning model troubleshooting steps and recommended data and model checklists for building robust deep learning solutions. FREE ACCESS
  • Locked
    3.  ML Technical Challenges and Best Practices
    7m
    Upon completion of this video, you will be able to recognize machine learning technical challenges and the best practices for dealing with them. FREE ACCESS
  • Locked
    4.  Case Study to Analyze Impacts of Best Practices
    5m 3s
    In this video, find out how to use case studies to analyze the impacts of adopting best practices for deep learning. FREE ACCESS
  • Locked
    5.  Deployment Challenges and Patterns
    10m 59s
    During this video, you will learn how to identify the challenges and patterns associated with deploying deep learning solutions in enterprise settings. FREE ACCESS
  • Locked
    6.  Case Study of Deployment Approaches
    5m 47s
    Upon completion of this video, you will be able to describe approaches for deploying deep learning solutions in the enterprise by using case study scenarios. FREE ACCESS
  • Locked
    7.  Architecting and Building ML Pipelines
    6m 42s
    Upon completion of this video, you will be able to describe approaches for architecting and building machine learning pipelines to implement scalable machine learning systems. FREE ACCESS
  • Locked
    8.  Rules for Building Machine Learning Pipelines
    4m 9s
    Upon completion of this video, you will be able to specify the rules that should be applied while building machine learning pipelines into applications. FREE ACCESS
  • Locked
    9.  Feature Engineering Rules
    3m 39s
    In this video, find out how to identify the rules that should be applied when using feature engineering to include the right features in applications. FREE ACCESS
  • Locked
    10.  Training-Serving Skew
    3m 19s
    Upon completion of this video, you will be able to specify the causes of training-serving skew and the rules that should be considered to manage training-serving skew. FREE ACCESS
  • Locked
    11.  Rules for Managing Optimization Refinement
    4m 16s
    Learn how to manage slowed growth, optimization refinement, and complex models in machine learning applications. FREE ACCESS
  • Locked
    12.  ML Project Checklists for Project Managers
    4m 8s
    After completing this video, you will be able to describe checklists for machine learning projects that project managers should prepare and adopt. FREE ACCESS
  • Locked
    13.  Course Summary
    1m 20s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course MLOps with MLflow: Using MLflow Projects & Recipes
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Book Operationalizing Machine Learning Pipelines: Building Reusable and Reproducible Machine Learning Pipelines Using MLOps , First Edition
Course AWS Certified Machine Learning: Problem Framing & Algorithm Selection
Rating 4.6 of 20 users Rating 4.6 of 20 users (20)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Advanced Reinforcement Learning: Principles
Rating 4.3 of 8 users Rating 4.3 of 8 users (8)
Course Using Apache Spark for AI Development
Rating 4.2 of 20 users Rating 4.2 of 20 users (20)
Course Linear Models & Gradient Descent: Gradient Descent and Regularization
Rating 3.9 of 7 users Rating 3.9 of 7 users (7)