Explore

AI Workflow: Enterprise Model Deployment

Approx. 9 hours to complete

Save Course

Go to Course

Course Summary

In this course, you will learn about the AI workflow, including data preparation, model creation, and deployment. You will also gain hands-on experience with IBM Watson Studio and Watson Machine Learning.

Key Learning Points

Understand the AI workflow and how to apply it to real-world problems
Learn to prepare data for machine learning models
Create and evaluate machine learning models using IBM Watson Studio
Deploy machine learning models using IBM Watson Machine Learning

Learning Outcomes

Ability to apply the AI workflow to real-world problems
Hands-on experience with IBM Watson Studio and Watson Machine Learning
Ability to create and deploy machine learning models

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online Self-paced
Video Lectures
Hands-on Projects
Quizzes

Similar Courses

IBM AI Engineering Professional Certificate
Applied Data Science with Python Specialization

Related Education Paths

Notable People in This Field

Yann LeCun
Andrew Ng

Related Books

Description

This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.

This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

Outline

Deploying Models
Introduction to Data at Scale
Introduction to Spark
Model Management and Deployment in Watson Studio
Data at scale: Through the Eyes of Our Working Example
Optimizing Performance in Python
High Performance Computing
Apache Spark (Hands-On)
Spark-submit
Docker Containers: Through the Eyes of our Working Example
On Containers and Docker
Docker Installation and Setup
Getting Started with Docker
Getting Started with Flask
Putting it all Together (Hands-On Tutorial)
Watson Machine Learning: Through the Eyes of Our Working Example
Getting Started (Hands-on)
Tutorial (Hands-on)
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz

Deploying Models using Spark
Introduction to Spark Machine Learning
Spark Recommendations
Recommenders
Introduction to Model Deployment Case Study
Spark Machine Learning: Through the Eyes of Our Working Example
Spark Pipelines
Spark Supervised Learning
Spark Unsupervised Learning (Hands-On)
Model
Spark Recommenders: Through the Eyes of Our Working Example
Recommendation Systems
Recommendation Systems in Production
Model Deployment: Through the Eyes of Our Working Example
Getting Started (Hands-On)
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz

Summary of User Reviews

This course is a great resource for anyone interested in learning about IBM AI workflow and machine learning model deployment. Many users found the practical exercises and real-world examples to be particularly helpful.

Pros from User Reviews

In-depth coverage of key AI concepts
Hands-on experience with IBM AI tools
Practical exercises and real-world examples
Flexible learning options

Cons from User Reviews

Some users found the course material to be challenging
The course is quite intensive and requires a significant time commitment
Some users felt that the course could have benefited from more interactive elements
The course is tailored to IBM tools and may not be suitable for those using other platforms

Recommended for you

DevOps Tutorial: Complete Beginners Training - 5 in 1 Bundle

Devops Engineer DevOps AWS DevOps Docker DevOps Jenkins DevOpsGIT , DevOps Vagran DevOps Maven What is this course about: In this course You will learn Hands on Devops Technology Concepts....

Save Course

Business Application of Machine Learning and Artificial Intelligence in Healthcare

The future of healthcare is becoming dependent on our ability to integrate Machine Learning and Artificial Intelligence into our organizations. 1....

Save Course

Tools for Data Science

What are some of the most popular data science tools, how do you use them, and what are their features? Data Scientist's Toolkit...

Save Course

AI Workflow: Machine Learning, Visual Recognition and NLP

This is the fourth course in the IBM AI Enterprise Workflow Certification specialization.     Out-of-the-box Watson models for natural language understanding and visual recognition will be used....

Save Course