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Statistics.comX: MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course - MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services.

MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services
4 semanas
5–6 horas por semana
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Comienza el 18 nov
Termina el 31 dic

Sobre este curso

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Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course: MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services. In this course you will learn how to set up automated monitoring of your data pipeline for prediction. Data drift, model drift and feedback loops can impair model performance and model stability, and you will learn how to monitor for those phenomena. You will also learn about setting triggers and alarms, so that operators can deal with problems with model instability. You will also cover ethical issues in machine learning and the risks they pose, and learn about the "Responsible Data Science" framework.

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Lo que aprenderás

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You will learn how to set up automated monitoring of your data pipeline for prediction and get hands on experience with topics like data pipelines, drift and feedback loops, model stability, triggers & alarms, model security, responsible AI and much more.

But most importantly, by the end of this course, you will know…

  • How to meet the differing requirements of model training versus model inference in your pipeline
  • How to check for model drift, data drift, and feedback loops
  • How to apply the principles of Continuous Integration (CI), Continuous Delivery (CDE) and Continuous Deployment (CD)

Plan de estudios

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Week 1 – Drift and Feedback Loops

  • Module 1: Training Versus Inference Pipelines
  • Module 2: Drift & Feedback Loops

Week 2 – Triggers, Alarms & Model Stability

  • Module 3: Triggers & Alarms
  • Module 4: Model Stability

Week 3 – CI/CD (Continuous Integration & Continuous Deployment/Delivery)

  • Module 5: CI/CD

Week 4 – Model Security and Responsible AI

  • Module 6: Responsible AI

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

Este curso es parte del programa Machine Learning Operations with Amazon Web Services (MLOps with AWS) Professional Certificate

Más información 
Instrucción por expertos
3 cursos de capacitación
A tu ritmo
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3 meses
5 - 7 horas semanales

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