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In this video, we talk with a Solutions Architect (formerly Databricks and LabelBox) who provides a technical product demo of LabelBox and Databricks. This is very common in the solutions architect interview process and is usually done after your other technical rounds.
A Solutions Architect or Solution Engineer position requires candidates to present a business or technical case study or give a product demo for the company they are interviewing with. This demo typically involves a platform allowing data annotation for machine learning, such as Label Box.
Chapters (Powered by ChapterMe) -
00:00 - Intro
00:56 - Overview of Smart Foods platform
01:30 - Databricks platform overview and demo for Smart Foods
05:03 - New formats, robust annotation, collaboration
07:02 - Easy Databricks/Labelbox integration for machine learning
09:07 - Databricks integration with S3 bucket lists
10:02 - Metadata support for images in Labelbox
10:27 - Databricks API key and Labelbox connector setup
10:49 - Data set setup with pandas and Labelbox
12:26 - Metadata cleanup benefits in LabelBox
13:11 - Metadata-based data discovery for selfdriving cars
15:00 - Creating and testing labels for self-driving cars
16:37 - LabelBox distributes image labels efficiently
17:17 - Super simple labeling and segmentation tool
18:01 - Auto segmentation reduces labeler workload
18:42 - Quick annotations for weather, masks, and objects
19:43 - Data sets with annotation types
20:21 - Databricks annotations for machine learning
22:19 - Simplifying workflow with auto-labeling
23:57 - Set up with managed free trial for self-driving car project success
27:47 - Demo went well, questions spaced out, engaging
30:37 - Conclusions and Feedback
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