If Google were to kill it, you could easily run it on any other hosted Kubernetes service.
I haven't used Cloud AI Platform Pipelines, but have spent a lot of time working with Kubeflow Pipelines and its pretty great!
 https://www.kubeflow.org/docs/aws/ (Deploy to AWS)
 https://www.kubeflow.org/docs/azure/ (Deploy to Azure)
Does that mean Cloud Composer might get depreciated?
Different things! Composer (based on Apache Airflow, which we contribute to) is a general purpose workflow system. Cloud AI Platform Pipelines is really focused on ML pipelines, specifically, like Kubeflow (the related OSS project that we contribute to) is meant to be.
Instead, I would argue that Kubeflow has lots of overlap with Airflow. But I don't think either will be cancelled :).
I got bored and stopped before finishing 2012, but you can go back and find more.
I am no Google fanboy and get frustrated by a lot of things they do. But I think the Google kills everything argument for B2B products is getting tiresome. Especially in a Google Cloud Platform context.
Their actions on the Google Maps API absurd price hike and the recent GKE pricing structure change debacle is a whole other story and worth a lot of criticism.
Was it a paid business tool? (If not, it's not really a business tool, it's a tool someone was relying on for business, which is different).
Then secondarily, was there a migration cost? Which there sometimes isn't if, for example, two tools are API compatible.
> a list of Google Cloud Products or even just Business to Business related products
They asked for GCP or B2B products, not only GCP products.
It used to be here https://cloud.google.com/prediction/ which now redirects to what I assume is its replacement.
With the Google Maps API price change they demonstrated that they are very capable & willing of abusing their market position with shocking price hikes and with the GKE structural price change that they are no longer interested in the trust & business from small & medium size companies and unless you are enterprise size you can expect unpleasant price changes going forward.
“Starting with the Python 3 runtime, the App Engine standard environment no longer includes bundled App Engine services such as Memcache and Task Queues. Instead, Google Cloud provides standalone products that are equivalent to most of the bundled services in the Python 2 runtime. For the bundled services that are not available as separate products in Google Cloud, such as image processing, search, and messaging, you can use third-party providers or other workarounds as suggested in this migration guide.
Removing the bundled App Engine services enables the Python 3 runtime to support a fully idiomatic Python development experience. In the Python 3 runtime, you write a standard Python app that is fully portable and can run in any standard Python environment, including App Engine.”
Most Google's Cloud Services have been very flaky and inconsistent.
I wish Cloud AI offered an API for the kind of results that I get when I use Google Lens. None of the ML offerings seem to come even close with labels.
this isn't an explicit kill-off, but certainly purposefully offering bad support
* Remember kubeflow - the open-source ML platform? Well, on GCP, it doesn't look so open-source anymore.
* It looks like TensorFlow Extended has been subsumed (killed off?) by this new managed platform. No Beam support - to be replaced by tf.data service?
And where did you get that TFX has been replaced by this? You can run Kubeflow pipelines (including those created by TFX) on this.
Also of interest may be this overview blog post  by yours truly :)
 ACM KDD '17 "TFX: A TensorFlow-Based Production-Scale Machine Learning Platform" https://dl.acm.org/doi/10.1145/3097983.3098021
I don't know the _eventual_ direction of Kubeflow/TFX -- but in our TFX pipelines you still get to choose where it runs. From the docs:
> Apache Beam is an open source, unified model for defining both batch and streaming data-parallel processing pipelines. TFX uses Apache Beam to implement data-parallel pipelines. The pipeline is then executed by one of Beam's supported distributed processing back-ends, which include Apache Flink, Apache Spark, Google Cloud Dataflow, and others.
You can also choose to run it on Kubeflow itself.