课程信息

第 5 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为9 小时

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

英语(English)

字幕:英语(English)
User
学习Course的学生是
  • Data Scientists

您将获得的技能

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
User
学习Course的学生是
  • Data Scientists

第 5 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为9 小时

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

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 4 小时

Deploying Models

3 个视频 (总计 11 分钟), 17 个阅读材料, 4 个测验
3 个视频
Introduction to Spark5分钟
Model Management and Deployment in Watson Studio2分钟
17 个阅读材料
Data at scale: Through the eyes of our Working Example4分钟
Optimizing performance in Python5分钟
High performance computing4分钟
Apache Spark30分钟
Spark-submit4分钟
Docker containers: Through the eyes of our Working Example3分钟
On containers and Docker2分钟
Docker installation and setup2分钟
NVIDIA Docker4分钟
Getting started with Docker4分钟
Getting started with Flask4分钟
Putting it all together (hands-on tutorial)45分钟
More on containers3分钟
Watson Machine Learning: Through the eyes of our Working Example3分钟
Getting Started (hands-on)20分钟
Tutorial (hands-on)40分钟
Summary/Review10分钟
4 个练习
Check for Understanding2分钟
Check for Understanding2分钟
Check for Understanding2分钟
End of Module Quiz10分钟
2
完成时间为 2 小时

Deploying Models using Spark

4 个视频 (总计 12 分钟), 11 个阅读材料, 4 个测验
4 个视频
Spark Recommendations1分钟
Recommenders6分钟
Introduction to Model Deployment Case Study2分钟
11 个阅读材料
Spark Machine Learning: Through the eyes of our Working Example4分钟
Spark Pipelines4分钟
Spark supervised learning4分钟
Spark unsupervised learning2分钟
Model4分钟
Spark Recommenders: Through the eyes of our Working Example4分钟
Recommendation systems4分钟
Recommendation systems in production4分钟
Model Deployment: Through the eyes of our Working Example3分钟
Getting Started (hands-on)1小时
Summary/Review
4 个练习
Check for Understanding2分钟
Check for Understanding2分钟
Check for Understanding2分钟
End of Module Quiz10分钟

讲师

Avatar

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

关于 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....

关于 IBM AI Enterprise Workflow 专项课程

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

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