Chevron Left
Zurück zu Optimize ML Models and Deploy Human-in-the-Loop Pipelines

Bewertung und Feedback des Lernenden für Optimize ML Models and Deploy Human-in-the-Loop Pipelines von deeplearning.ai

4.7
Sterne
89 Bewertungen

Über den Kurs

In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting. Lastly, you will set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud....

Top-Bewertungen

KK

15. Feb. 2022

Highly technical but beneficial course that allows you to explore resource constraints of an ML application. Thanks for simplifying as much as possible, enjoyed every bit!

SH

14. Sep. 2021

I have worked in data science field for some years, so make me easier to appreciate the contents prepared by course mentors. Thanks! :)

Filtern nach:

1 - 22 von 22 Bewertungen für Optimize ML Models and Deploy Human-in-the-Loop Pipelines

von Alexander M

29. Aug. 2021

von Diego M

20. Nov. 2021

von Sanjay C

17. Jan. 2022

von Mark P

13. Sep. 2021

von Parag K

22. Okt. 2021

von YANGYANG C

4. Sep. 2021

von Chris D

28. Aug. 2021

von Kaan G K

16. Feb. 2022

von phoenix c

12. Sep. 2021

von lonnie

22. Juli 2021

von Martin H

23. März 2022

von Simon h

14. Sep. 2021

von yugesh v

5. Jan. 2022

von James H

27. Mai 2022

von Kee K Y

7. Aug. 2021

von k b

31. Jan. 2022

von Daniel M

16. Jan. 2022

von Iakovina K

13. Mai 2022

von Muhammad D

18. Aug. 2022

von Mauricio S V F

27. Nov. 2022

von Antony W

17. Aug. 2021

von Siddharth S

31. März 2022