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MedGemma is Google's revolutionary open AI model for healthcare, offering unprecedented control and data sovereignty. Its self-hosting capability ensures privacy and governance, enabling custom fine-tuning with proprietary data. This democratizes advanced medical AI, empowering organizations to revolutionize patient care and research with purpose-built precision.
In an era dominated by cloud computing, there are still compelling reasons to host AI models on-premises. While cloud-based solutions offer scalability and convenience, certain environments demand more control, reliability, and privacy. Hosting models locally ensures greater data governance, allows compliance with industry or regulatory standards, and enhances security by keeping sensitive information within a closed network. It also becomes essential in situations where internet connectivity is unreliable or unavailable, such as in remote facilities, secure government operations, or offline field deployments. Additionally, on-prem hosting can offer reduced latency, cost predictability, and full control over model execution and updates—making it a critical choice for organizations with strict operational or compliance requirements.
This will show you how to run a basic document Q&A offline using:
Ollama + local LLM (Gemma3, Mistral, Llama3.3, etc.)
LangChain
FAISS (vector DB)
SentenceTransformers (embeddings)
PyPDF (PDF loading)
PaLM 2 is Google's next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI.
It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than previous state-of-the-art LLMs. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements.
This article offers a quick and straightforward method for leveraging the PaLM 2 API to extract knowledge and ask questions from text.
Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.
In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.
Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.
Get started with your machine learning adventures by using state-of-the-art tools. We will talk about how to utilize the amazing work done by others to jump start your projects. We will also talk about making them more scalable and getting to a solution that can be used for production as well.
Discover how to achieve superpowers by embracing machine learning in JavaScript using TensorFlow.js in the browser and beyond in this speedy 45 minute talk. Get inspired through a whole bunch of creative prototypes that push the boundaries of what is possible in the modern web browser and then take your own first steps with machine learning in minutes. By the end of the talk everyone will understand how to recognize an object of their choice which could then be used in any creative way you can imagine. Familiarity with JavaScript is advantageous, but no background in machine learning is required. Come take your first steps with TensorFlow.js!
This talk provides an overview of TensorFlow Lite and its awesome tools and resources to help you create intelligent apps. I will walk through end-to-end computer vision examples with TFLite: from model training, conversion, optimization to model deployment on mobile and edge devices.