Menyiapkan Data untuk ML API di Google Cloud: Challenge Lab Ulasan

Khushi S. · Diulas 23 hari lalu

The instruction are not clear. Copying the result files to the set location was not easy to figure out.

Pavan D. · Diulas 23 hari lalu

Yeison A. · Diulas 23 hari lalu

Kanchi M. · Diulas 23 hari lalu

Sindhu 2. · Diulas 23 hari lalu

Jyoti B. · Diulas 23 hari lalu

kimiko G. · Diulas 23 hari lalu

Rashmi S. · Diulas 23 hari lalu

Nikhil S. · Diulas 23 hari lalu

SIVA GANESH M. · Diulas 23 hari lalu

Hariharan R. · Diulas 23 hari lalu

Yustin Eduardo P. · Diulas 23 hari lalu

Shafat A. · Diulas 23 hari lalu

Trâm N. · Diulas 23 hari lalu

Yishang Z. · Diulas 23 hari lalu

Shirisha A. · Diulas 23 hari lalu

harry G. · Diulas 23 hari lalu

Vishnuram G. · Diulas 23 hari lalu

A solid, practical module covering how to prepare and format data appropriately for consumption by Google Cloud's Machine Learning APIs, likely touching on data cleaning, formatting requirements, and preprocessing steps specific to different API types (Vision, Speech, Natural Language, etc.). It walks through key concepts around ensuring data quality and compatibility before sending it to ML APIs, which is a foundational but often overlooked step in building reliable ML-powered applications. The hands-on approach helps reinforce best practices for data preprocessing across different API contexts. Some prior familiarity with basic data handling and Google Cloud ML APIs is helpful for smoother comprehension. Overall, a useful module for building foundational data preparation skills for ML integration on Google Cloud.

Beny C. · Diulas 23 hari lalu

tidak ada kendala di lab ini

Resha R. · Diulas 23 hari lalu

Thulasi G. · Diulas 23 hari lalu

Olena L. · Diulas 23 hari lalu

Shivaansh G. · Diulas 23 hari lalu

lab fails on task 2 when us=central1 is the specified version

Beshoy T. · Diulas 23 hari lalu

Jeet P. · Diulas 23 hari lalu