feat: Adopt BigQuery and Vertex AI (#275)

* feat: Move BigQuery, Dataflow, Vertex to ADOPT * fix: Do not adopt dataflow --------- Co-authored-by: Samuel Sjöberg <[email protected]>
extenda · Aug 9, 2024 · 1de2de2 · 1de2de2
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diff --git a/radar/data_management/bigquery.yaml b/radar/data_management/bigquery.yaml
@@ -2,14 +2,17 @@ name: BigQuery
 blip:
   - date: 2020-04-16
     ring: TRIAL
+  - date: 2022-07-25
+    ring: ADOPT
 description: |
   [Bigquery](https://cloud.google.com/bigquery) is a serverless, highly scalable,
   and cost-effective cloud data warehouse designed for business agility.
 rationale: |
-  Analyze petabytes of data using ANSI SQL at blazing-fast speeds,
-  with zero operational overhead.
+  Analyze petabytes of data using ANSI SQL at blazing-fast speeds,with zero operational overhead.
   Run analytics at scale with 26%–34% lower three-year TCO than cloud data warehouse alternatives.
   Democratize insights with a trusted, and more secure platform that scales with your needs.
+
+  We recommend adopting BigQuery for analytics of big data sets.
 license:
   commercial:
     company: Google

diff --git a/radar/data_management/vertex_ai.yaml b/radar/data_management/vertex_ai.yaml
@@ -2,6 +2,8 @@ name: Vertex AI
 blip:
   - date: 2021-06-25
     ring: ASSESS
+  - date: 2022-07-25
+    ring: ADOPT
 description: |
   [Vertex AI](https://cloud.google.com/vertex-ai) is a Google Cloud service and a unified
   AI platform that provides tooling for building, deploying, and scaling pre-trained and custom ML models.
@@ -23,6 +25,9 @@ rationale: |
 
   The main benefit of using Vertex AI is that it significantly simplifies the process
   of building, serving, and scaling ML models on GCP.
+
+  After successfully evaluating all major Vertex AI functionality, we decided to adopt it
+  for implementing all MLOps workflows.
 license:
   commercial:
     company: Google