About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

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Advanced Level

Approx. 9 hours to complete

Suggested: This course requires 7.5 to 9 hours of study....

English

Subtitles: English

Skills you will gain

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 9 hours to complete

Suggested: This course requires 7.5 to 9 hours of study....

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Deploying Models

3 videos (Total 11 min), 17 readings, 4 quizzes
3 videos
Introduction to Spark5m
Model Management and Deployment in Watson Studio2m
17 readings
Data at scale: Through the eyes of our Working Example4m
Optimizing performance in Python5m
High performance computing4m
Apache Spark30m
Spark-submit4m
Docker containers: Through the eyes of our Working Example3m
On containers and Docker2m
Docker installation and setup2m
NVIDIA Docker4m
Getting started with Docker4m
Getting started with Flask4m
Putting it all together (hands-on tutorial)45m
More on containers3m
Watson Machine Learning: Through the eyes of our Working Example3m
Getting Started (hands-on)20m
Tutorial (hands-on)40m
Summary/Review10m
4 practice exercises
Check for Understanding2m
Check for Understanding2m
Check for Understanding2m
End of Module Quiz10m
Week
2
2 hours to complete

Deploying Models using Spark

4 videos (Total 12 min), 11 readings, 4 quizzes
4 videos
Spark Recommendations1m
Recommenders6m
Introduction to Model Deployment Case Study2m
11 readings
Spark Machine Learning: Through the eyes of our Working Example4m
Spark Pipelines4m
Spark supervised learning4m
Spark unsupervised learning2m
Model4m
Spark Recommenders: Through the eyes of our Working Example4m
Recommendation systems4m
Recommendation systems in production4m
Model Deployment: Through the eyes of our Working Example3m
Getting Started (hands-on)1h
Summary/Review
4 practice exercises
Check for Understanding2m
Check for Understanding2m
Check for Understanding2m
End of Module Quiz10m

Instructors

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

About IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

About the IBM AI Enterprise Workflow Specialization

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow....
IBM AI Enterprise Workflow

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.