Informações sobre o curso

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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 19 horas para completar
Inglês
Legendas: Inglês

Habilidades que você terá

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível avançado
Aprox. 19 horas para completar
Inglês
Legendas: Inglês

oferecido por

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IBM

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

6 horas para concluir

Feedback loops and Monitoring

6 horas para concluir
5 vídeos (Total 19 mín.), 16 leituras, 4 testes
5 videos
Feedback Loops and Unit Tests7min
Performance Monitoring and Business Metrics1min
Performance Drift5min
Performance Monitoring Case Study1min
16 leituras
Feedback loops and unit tests: Through the eyes of our Working Example3min
Feedback loops4min
Unit tests4min
Unit testing in Python3min
Test-Driven Development (TDD)3min
CI/CD3min
Performance Monitoring: Through the eyes of our Working Example3min
Logging3min
Minimal requirements for log files4min
Logging in Python (hands-on)30min
Model performance drift4min
Performance Drift Notebook Review25min
Security and Machine Learning Models10min
Performance Monitoring Case Study: Through the eyes of our Working Example4min
Getting started (hands-on)2h
Summary/Review6min
4 exercícios práticos
Check for Understanding30min
Check for Understanding30min
Check for Understanding30min
End of Module Quiz5min
Semana
2

Semana 2

4 horas para concluir

Hands on with Openscale and Kubernetes

4 horas para concluir
3 vídeos (Total 22 mín.), 6 leituras, 3 testes
3 videos
Kubernetes Explained10min
Kubernetes vs. Docker: It's Not an Either/Or Question8min
6 leituras
Watson OpenScale: Through the eyes of our Working Example4min
Getting started (hands-on)1h
Kubernetes Explained: Through the eyes of our Working Example4min
Introduction to Kubernetes4min
Getting started (hands-on)1h 30min
Summary/Review4min
3 exercícios práticos
Check for Understanding30min
Check for Understanding30min
End of Module Quiz5min
Semana
3

Semana 3

3 horas para concluir

Capstone: Pulling it all together (Part 1)

3 horas para concluir
10 leituras
10 leituras
Capstone: Through the eyes of our Working Example4min
What is in the Capstone and associated Review?4min
Review of Course 1: Business Priorities and Data Ingestion4min
Review of Course 2: Data Analysis and Hypothesis Testing5min
Review of Course 3: Feature Engineering and Bias Detection5min
Review of Course 4: Machine Learning, Visual Recognition, and NLP12min
Review of Course 5: Enterprise Model Deployment4min
About the data3min
Capstone Assignment 1: Through the eyes of our Working Example4min
Capstone Part 1: Getting Started (hands-on)2h
1 exercício prático
Capstone - Part 1 Quiz30min
Semana
4

Semana 4

6 horas para concluir

Capstone: Pulling it all together (Part 2)

6 horas para concluir
4 leituras
4 leituras
Capstone Assignment 2: Through the eyes of our Working Example4min
Capstone Part 2: Getting started (hands-on)2h
Capstone Part 3: Getting started (hands-on)2h
Solution Files1min
2 exercícios práticos
Capstone - Part 2 Quiz30min
Capstone - Part 3 Quiz30min

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Sobre Programa de cursos integrados IBM AI Enterprise Workflow

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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