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Karl Weinmeister
648 posts
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Karl Weinmeister
@kweinmeister
Cloud Engineering @ Google. AI/ML/Data, Blue Devil & Longhorn, wanna-be at home improvement. Opinions are my own.
Austin, Texas, USA
Датум придруживања: May 2009
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  • user avatar
    Karl Weinmeister
    @kweinmeister
    28. мар 2025.
    Gemini Pro 2.5 Exp, Google's state-of-the-art thinking model, is now live on Vertex AI! 👉 Try it out: console.cloud.google.com/freetrial?redi…
    00:00
    13K
  • user avatar
    Karl Weinmeister
    @kweinmeister
    27. окт 2020.
    "Kids, what you really should say is Trick XOR Treat!" 🤓 #Halloween2020 #AlwaysBeLearning
    user avatar
    Miyamol⌬宮元一賢
    @38mo1
    24. окт 2020.
    #ハロウィン でわかる論理演算 #Halloween
  • user avatar
    Karl Weinmeister
    @kweinmeister
    9. апр 2025.
    Are you ready to try out @Google's new #Agent Development Kit (ADK)? Build and deploy your first agent on #CloudRun in less than 5 minutes! 👉 medium.com/google-cloud/h…
    7K
  • user avatar
    Karl Weinmeister
    @kweinmeister
    17. јан 2020.
    Want to try out AutoML on structured data? My new blog post shows how, using the SDK.
    Build a model automatically in your Jupyter Notebook with AutoML Tables | Google Cloud Blog
    Од cloud.google.com
  • user avatar
    Karl Weinmeister
    @kweinmeister
    16. окт 2020.
    Questions we’ll answer in my latest blog post: 1. What is an #ML model card? 2. How can I use @scikit_learn on @GCPcloud to build a model? 3. How can I create my own model card?
    Create a Model Card with Scikit-Learn | Google Cloud Blog
    Од cloud.google.com
  • user avatar
    Karl Weinmeister
    @kweinmeister
    12. јул 2024.
    A new approach to RAG that can increase accuracy by up to 13%, and decrease latency by 51%? Check out Speculative RAG from researchers at @googlecloud , @GoogleDeepMind , and @UCSanDiego. Here's how it works: 1. Retrieve relevant documents based on the user's question. 2.
    7K
  • user avatar
    Karl Weinmeister
    @kweinmeister
    13. авг 2021.
    Finding associations in data can be useful. Causal inference provides another layer of understanding💡. cloud.google.com/blog/topics/de… #datascience #machinelearning #causalinference
    The Brexit vote: A case study in causal inference using machine learning | Google Cloud Blog
    Од cloud.google.com
  • user avatar
    Karl Weinmeister
    @kweinmeister
    27. авг 2024.
    How quickly can you ingest all of your project source code, and ask Gemini questions in @Google AI Studio? #LLM #AI #Development #Gemini
    00:00
    6K
  • user avatar
    Karl Weinmeister
    @kweinmeister
    14. апр 2025.
    Looking for a step-by-step map for #AI agents on @GoogleCloud? I've got you covered, from building with #ADK to accessing tools with #MCP and interoperability with #A2A. Blog post: medium.com/google-cloud/b… #Gemini #VertexAI #CloudRun #Agents
    Build AI Agents your way on Google Cloud
    Од medium.com
    2K
  • user avatar
    Karl Weinmeister
    @kweinmeister
    13. апр 2020.
    My new blog post takes #AutoML one step further. We'll start with raw data that needs to be pre-processed. Then, we'll create an end-to-end pipeline, resulting in a #MachineLearning model without writing any Python or SQL.
    Turn your raw data into a machine learning model without Python or SQL | Google Cloud Blog
    Од cloud.google.com
  • user avatar
    Karl Weinmeister
    @kweinmeister
    9. апр 2021.
    #PyTorch Lightning and #TPUs are a great match, offering fast #ML model development and training. Find out how to use them together in this latest @GoogleCloudTech blog post.
    Train ML models with Pytorch Lightning on TPUs | Google Cloud Blog
    Од cloud.google.com
  • user avatar
    Karl Weinmeister
    @kweinmeister
    4. феб 2021.
    I enjoyed using @pytorchlightnin! It helped me organize and accelerate #PyTorch development. I shared my experiences in this latest blog post.
    Increase your productivity using PyTorch Lightning | Google Cloud Blog
    Од cloud.google.com
  • user avatar
    Karl Weinmeister
    @kweinmeister
    7. јан 2025.
    SWOT analysis is a perfect task for an AI agent. An agent can gather multiple sources of information, synthesize the findings, and then validate the results. Learn how I built an agent to do just that on a modern stack, including @pydantic AI, @FastAPI , and #Gemini on Vertex AI
    GIF
    2K
  • user avatar
    Karl Weinmeister
    @kweinmeister
    14. мај 2025.
    How can you stretch #Gemini 2.5's huge 1M token context window even further? I took the 56M token @googlecloud python-docs-samples repo and made it fit – and documented the entire process! Here's a thread on the step-by-step. 🧵
    2K