About this Course
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100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 6개 강좌 중 6번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 14시간 필요

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

영어

자막: 영어

귀하가 습득할 기술

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 6개 강좌 중 6번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 14시간 필요

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

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

Feedback loops and Monitoring

5개 동영상 (총 19분), 15 readings, 4 quizzes
5개의 동영상
Feedback Loops and Unit Tests7m
Performance Monitoring and Business Metrics1m
Performance Drift5m
Performance Monitoring Case Study1m
15개의 읽기 자료
Feedback loops and unit tests: Through the eyes of our Working Example3m
Feedback loops4m
Unit tests4m
Unit testing in Python3m
Test-Driven Development (TDD)3m
CI/CD3m
Performance Monitoring: Through the eyes of our Working Example3m
Logging3m
Minimal requirements for log files4m
Logging in Python (hands-on)30m
Model performance drift4m
Performance Drift Notebook Review25m
Performance Monitoring Case Study: Through the eyes of our Working Example4m
Getting started (hands-on)2h
Summary/Review6m
4개 연습문제
Check for Understanding2m
Check for Understanding2m
Check for Understanding2m
End of Module Quiz5m
2
완료하는 데 3시간 필요

Hands on with Openscale and Kubernetes

3개 동영상 (총 22분), 6 readings, 3 quizzes
3개의 동영상
Kubernetes Explained10m
Kubernetes vs. Docker: It's Not an Either/Or Question8m
6개의 읽기 자료
Watson OpenScale: Through the eyes of our Working Example4m
Getting started (hands-on)1h
Kubernetes Explained: Through the eyes of our Working Example4m
Introduction to Kubernetes4m
Getting started (hands-on)1시 30분
Summary/Review4m
3개 연습문제
Check for Understanding2m
Check for Understanding2m
End of Module Quiz5m
3
완료하는 데 3시간 필요

Capstone: Pulling it all together (Part 1)

10 readings, 1 quiz
10개의 읽기 자료
Capstone: Through the eyes of our Working Example4m
What is in the Capstone and associated Review?4m
Review of Course 1: Business Priorities and Data Ingestion4m
Review of Course 2: Data Analysis and Hypothesis Testing5m
Review of Course 3: Feature Engineering and Bias Detection5m
Review of Course 4: Machine Learning, Visual Recognition, and NLP12m
Review of Course 5: Enterprise Model Deployment4m
About the data3m
Capstone Assignment 1: Through the eyes of our Working Example4m
Capstone Part 1: Getting Started (hands-on)2h
1개 연습문제
Capstone - Part 1 Quiz6m
4
완료하는 데 5시간 필요

Capstone: Pulling it all together (Part 2)

4 readings, 3 quizzes
4개의 읽기 자료
Capstone Assignment 2: Through the eyes of our Working Example4m
Capstone Part 2: Getting started (hands-on)2h
Capstone Part 3: Getting started (hands-on)2h
Solution Files1m
2개 연습문제
Capstone - Part 2 Quiz6m
Capstone - Part 3 Quiz6m

강사

Image of instructor, Mark J Grover

Mark J Grover

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

Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

IBM 정보

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....

IBM AI Enterprise Workflow 전문 분야 정보

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

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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