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๐Ÿ—ž๏ธApril 04, 2024

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April 04, 2024

2024-04-04 - (from GCP latest releases)

Anthos clusters on AWS Announcement You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches: 1.28.7-gke.1700 1.27.11-gke.1600 1.26.14-gke.1500 Anthos clusters on Azure Announcement You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches: 1.28.7-gke.1700 1.27.11-gke.1600 1.26.14-gke.1500 BigQuery Feature The allow_non_incremental_definition option and max_staleness option for materialized views are now generally available (GA). The allow_non_incremental_definition option supports an expanded range of SQL queries to create materialized views, and the max_staleness option provides consistently high performance with controlled costs when processing large, frequently changing datasets. Feature You can now perform model monitoring in BigQuery ML. The following model monitoring functions are now in preview: ML.DESCRIBE_DATA: compute descriptive statistics for a set of training or serving data. ML.VALIDATE_DATA_SKEW: compute the statistics for a set of serving data, and then compare them to the statistics for the data used to train a BigQuery ML model in order to identify anomalous differences between the two data sets. ML.VALIDATE_DATA_DRIFT: compute and compare the statistics for two sets of serving data in order to identify anomalous differences between the two data sets. ML.TFDV_DESCRIBE: compute fine-grained descriptive statistics for a set of training or serving data. This function provides the same behavior as the TensorFlow tfdv.generate_statistics_from_csv API. ML.TFDV_VALIDATE: compute and compare the statistics for training and serving data, or two sets of serving data, in order to identify anomalous differences between the two data sets. This function provides the same behavior as the TensorFlow validate_statistics API. Feature BigQuery data clean rooms with analysis rules and enhanced usage metrics are now generally available (GA). Data clean rooms provide a security-enhanced and privacy-preserving environment for multiple parties to share and augment data without moving or revealing the underlying data. Feature Join restrictions, list overlap, differential privacy with privacy budgeting, and aggregation thresholding are now generally available (GA) and enforceable in BigQuery data clean rooms using analysis rules. Cloud Data Fusion Changed Cloud Data Fusion is available in the africa-south1 region. For more information, see Pricing. Compute Engine Feature Generally available: Simplify block storage management for Compute Engine instances with Hyperdisk Storage Pools. A Hyperdisk Storage Pool is a pre-purchased collection of disk capacity, throughput, and IOPS which you can then provision to your applications as needed. By managing disks in aggregate, you can save costs while achieving expected capacity and performance growth. For more information, see About Hyperdisk Storage Pools. Dialogflow Feature Vertex AI Conversation: You can now create a data store in one language that is connected to an agent that uses different languages. Google Kubernetes Engine Security A Denial-of-Service (DoS) vulnerability (CVE-2023-45288) was recently discovered in multiple implementations of the HTTP/2 protocol, including the golang HTTP server used by Kubernetes. The vulnerability could lead to a DoS of the Google Kubernetes Engine (GKE) control plane. For more information, see the GCP-2024-022 security bulletin.

[Technology] ๐ŸŒŽ https://cloud.google.com/release-notes#April_04_2024 [๐Ÿง ] [v2] article_embedding_description: {:llm_project_id=>"Unavailable", :llm_dimensions=>nil, :article_size=>7341, :llm_embeddings_model_name=>"textembedding-gecko"}
[๐Ÿง ] [v1/3] title_embedding_description: {:ricc_notes=>"[embed-v3] Fixed on 9oct24. Only seems incompatible at first glance with embed v1.", :llm_project_id=>"unavailable possibly not using Vertex", :llm_dimensions=>nil, :article_size=>7341, :poly_field=>"title", :llm_embeddings_model_name=>"textembedding-gecko"}
[๐Ÿง ] [v1/3] summary_embedding_description:
[๐Ÿง ] As per bug https://github.com/palladius/gemini-news-crawler/issues/4 we can state this article belongs to titile/summary version: v3 (very few articles updated on 9oct24)

๐Ÿ—ฟarticle.to_s 

------------------------------
Title: April 04, 2024
[content]
Anthos clusters on AWS
Announcement
You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:


1.28.7-gke.1700
1.27.11-gke.1600
1.26.14-gke.1500

Anthos clusters on Azure
Announcement
You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:


1.28.7-gke.1700
1.27.11-gke.1600
1.26.14-gke.1500

BigQuery
Feature
The allow_non_incremental_definition option and max_staleness option for materialized views are now generally available (GA).  The allow_non_incremental_definition option supports an expanded range of SQL queries to create materialized views, and the max_staleness option provides consistently high performance with controlled costs when processing large, frequently changing datasets.
Feature
You can now perform
model monitoring in BigQuery ML. The following model monitoring functions are now in
preview:


ML.DESCRIBE_DATA:
compute descriptive statistics for a set of training or serving data.
ML.VALIDATE_DATA_SKEW:
compute the statistics for a set of serving data, and then compare them to
the statistics for the data used to train a BigQuery ML model in order to
identify anomalous differences between the two data sets.
ML.VALIDATE_DATA_DRIFT:
compute and compare the statistics for two sets of serving data in order to
identify anomalous differences between the two data sets.
ML.TFDV_DESCRIBE:
compute fine-grained descriptive statistics for a set of training or
serving data. This function provides the same behavior as the
TensorFlow tfdv.generate_statistics_from_csv API.
ML.TFDV_VALIDATE:
compute and compare the statistics for training and serving data, or two
sets of serving data, in order to identify anomalous differences between
the two data sets. This function provides the same behavior as the
TensorFlow validate_statistics API.

Feature
BigQuery data clean rooms with analysis rules and enhanced usage metrics are now generally available (GA). Data clean rooms provide a security-enhanced and privacy-preserving environment for multiple parties to share and augment data without moving or revealing the underlying data. 
Feature
Join restrictions, list overlap, differential privacy with privacy budgeting, and aggregation thresholding are now generally available (GA) and enforceable in BigQuery data clean rooms using analysis rules.
Cloud Data Fusion
Changed
Cloud Data Fusion is available in the africa-south1 region. For more information, see Pricing.
Compute Engine
Feature
Generally available: Simplify block storage management for Compute Engine instances with Hyperdisk Storage Pools. A Hyperdisk Storage Pool is a pre-purchased collection of disk capacity, throughput, and IOPS which you can then provision to your applications as needed. By managing disks in aggregate, you can save costs while achieving expected capacity and performance growth. For more information, see About Hyperdisk Storage Pools.
Dialogflow
Feature
Vertex AI Conversation: You can now create a data store in one language that is connected to an agent that uses different languages.
Google Kubernetes Engine
Security
A Denial-of-Service (DoS) vulnerability (CVE-2023-45288) was recently discovered in multiple implementations of the HTTP/2 protocol, including the golang HTTP server used by Kubernetes. The vulnerability could lead to a DoS of the Google Kubernetes Engine (GKE) control plane.

For more information, see the GCP-2024-022 security bulletin.
[/content]

PublishedDate: 2024-04-04
Category: Technology
NewsPaper: GCP latest releases
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Announcement

\n

You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:

\n\n\nAnthos clusters on Azure\n

Announcement

\n

You can now launch clusters with the following Kubernetes versions. Click on the following links to see the release notes associated with these patches:

\n\n\nBigQuery\n

Feature

\n

The allow_non_incremental_definition option and max_staleness option for materialized views are now generally available (GA). The allow_non_incremental_definition option supports an expanded range of SQL queries to create materialized views, and the max_staleness option provides consistently high performance with controlled costs when processing large, frequently changing datasets.

\n

Feature

\n

You can now perform\nmodel monitoring in BigQuery ML. The following model monitoring functions are now in\npreview:

\n\n
    \n
  • ML.DESCRIBE_DATA:\ncompute descriptive statistics for a set of training or serving data.
    • \n
  • ML.VALIDATE_DATA_SKEW:\ncompute the statistics for a set of serving data, and then compare them to\nthe statistics for the data used to train a BigQuery ML model in order to\nidentify anomalous differences between the two data sets.
    • \n
  • ML.VALIDATE_DATA_DRIFT:\ncompute and compare the statistics for two sets of serving data in order to\nidentify anomalous differences between the two data sets.
    • \n
  • ML.TFDV_DESCRIBE:\ncompute fine-grained descriptive statistics for a set of training or\nserving data. This function provides the same behavior as the\nTensorFlow tfdv.generate_statistics_from_csv API.
    • \n
  • ML.TFDV_VALIDATE:\ncompute and compare the statistics for training and serving data, or two\nsets of serving data, in order to identify anomalous differences between\nthe two data sets. This function provides the same behavior as the\nTensorFlow validate_statistics API.
    • \n
\n

Feature

\n

BigQuery data clean rooms with analysis rules and enhanced usage metrics are now generally available (GA). Data clean rooms provide a security-enhanced and privacy-preserving environment for multiple parties to share and augment data without moving or revealing the underlying data.

\n

Feature

\n

Join restrictions, list overlap, differential privacy with privacy budgeting, and aggregation thresholding are now generally available (GA) and enforceable in BigQuery data clean rooms using analysis rules.

\nCloud Data Fusion\n

Changed

\n

Cloud Data Fusion is available in the africa-south1 region. For more information, see Pricing.

\nCompute Engine\n

Feature

\n

Generally available: Simplify block storage management for Compute Engine instances with Hyperdisk Storage Pools. A Hyperdisk Storage Pool is a pre-purchased collection of disk capacity, throughput, and IOPS which you can then provision to your applications as needed. By managing disks in aggregate, you can save costs while achieving expected capacity and performance growth. For more information, see About Hyperdisk Storage Pools.

\nDialogflow\n

Feature

\n

Vertex AI Conversation: You can now create a data store in one language that is connected to an agent that uses different languages.

\nGoogle Kubernetes Engine\n

Security

\n

A Denial-of-Service (DoS) vulnerability (CVE-2023-45288) was recently discovered in multiple implementations of the HTTP/2 protocol, including the golang HTTP server used by Kubernetes. The vulnerability could lead to a DoS of the Google Kubernetes Engine (GKE) control plane.

\n\n

For more information, see the GCP-2024-022 security bulletin.

\n\n ",
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"created_at"=>Fri, 05 Apr 2024 07:24:40.777880000 UTC +00:00,
"updated_at"=>Mon, 21 Oct 2024 18:55:50.723091000 UTC +00:00,
"newspaper"=>"GCP latest releases",
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