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Google Cloud Machine Learning
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Google Cloud Machine Learning might be a bit more popular than Drupal. We know about 41 links to it since March 2021 and only 28 links to Drupal. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
For developers building on Gemini API or Vertex AI, the practical question is whether Google exposes the rendering signals that power Neural Expressive at the API level - structured output types, response format hints, media embedding signals - so that third-party applications can build the same adaptive rendering behavior rather than always falling back to raw text. That API surface isn't publicly documented yet,... - Source: dev.to / about 2 months ago
TPU 8t and TPU 8i will be available to Cloud customers later in 2026. You can request more information now to prepare for their general availability. The chips are integrated into Google's AI Hypercomputer stack, supporting JAX, PyTorch, vLLM, and XLA. Deployment options range from Vertex AI managed services to GKE for teams that want infrastructure-level control. - Source: dev.to / 3 months ago
Across the five axes, automation depth is functional via API tool-calling. Session persistence is absent outside the Vertex AI ecosystem. Data residency introduces real exposure for regulated workloads. The standard Gemini API routes data through Google's shared infrastructure, and Google's data usage policies may use API inputs for service improvement unless you're under an enterprise agreement with explicit data... - Source: dev.to / 3 months ago
The survivors get sent to Gemini 2.5 Pro on Vertex AI. DeepZero Pipeline Source Code - Contains the Python-based triager, Ghidra extractor script, Semgrep rules, and the LangChain DeepAgents reasoning loop. - Source: dev.to / 3 months ago
VertexAI - Innovate faster with enterprise-ready generative AI. - Source: dev.to / 5 months ago
I would be interested in some good migration tools, paid ones are also ok. I found a post about this on drupal.org, but it didn't seem like an easy process. It is a multilanguage site with many content types, and a totally custom theme. Source: over 3 years ago
You got already good advice, but wanted to point the guide of drupal.org where you can see some tools listed with instructions and channels https://www.drupal.org/community/contributor-guide/reference-information/talk/tools. Source: over 3 years ago
There is a service call GitPod that provides a temporary container Drupal environment. If you are familiar with what is going on around the future of how Drupal modules will eventually be offered up, you will likely have seen the "Project Browser" module as a contrib demo of the approach. It is used for people to give feedback to the developers. So they set up the typical 'SimplyTestMe' but also a GitPod... Source: almost 4 years ago
For reviews, it depends entirely on what you mean by "review". I believe core has a simple comment module, although it may have been deprecated for D9? There are likely many review-style modules on drupal.org that might work, or if you just want to link out to third-party reviews then it could just be a repeating-value link field on the Product content type. Source: almost 4 years ago
They should also use standards tools like Github. The drupal.org platform was certainly impressive 10 years ago, today it's a pain to use it. They ducktape it with gitlab, but really it sucks to have to read documentation to simply do a pull request. Source: almost 4 years ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
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Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
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NumPy - NumPy is the fundamental package for scientific computing with Python
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