Google

YouTube search... ...Google search

• Tools and Resources
• Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)
  • Colaboratory (Colab) - Jupyter notebooks
    • Google Developers Codelabs
  • Hands-on labs
  • Google Cloud
    • Google Cloud Platform Marketplace
    • Google Cloud Code and Google Kubernetes Engine (GKE)
• Pixel phone with TPU - Tensor Processing Unit / AI Chip
• TensorFlow
  • TensorBoard ...TensorFlow's visualization toolkit
  • TensorFlow Hub is a library for reusable machine learning modules that you can use to speed up the process of training your model. A TensorFlow module is a reusable piece of a TensorFlow graph. With transfer learning, you can use TensorFlow modules to preprocess input feature vectors, or you can incorporate a TensorFlow module into your model as a trainable layer. This can help you train your model faster, using a smaller dataset, while improving generalization.
• Natural Language:
  • Language Interpretability Tool (LIT) | Google - GitHub
    • Google open-sources LIT, a toolset for evaluating natural language models | Kyle Wiggers - VentureBeat
  • Google Natural Language
  • Google Semantic Reactor
  • Bidirectional Encoder Representations from Transformers (BERT)
  • Gboard on Pixel phones now uses an on-device neural network for speech recognition | Kyle Wiggers - VentureBeat
  • Universal Sentence Encoder lite a model that encodes text into 512-dimensional embeddings. These embeddings can then be used as inputs to natural language processing tasks such as sentiment classification and textual similarity analysis. This module is a TensorFlow.js
• AI Platform
  • All 29 AI announcements from Google Next ‘19: the smartest laundry list
  • Google launches AI Platform, a collaborative model-making tool for data scientists | Khari Johnson - VentureBeat
• AI Hub - explore and use a variety of AI asset categories. AI Hub offers a collection of components for developers and data scientists building artificial intelligence (AI) systems. Use AI Hub to: [1] Find, deploy, and use Kubeflow Pipelines and components, [2] explore code and learn in interactive Jupyter notebooks, [3] explore and reuse TensorFlow modules, Explore, deploy, and use trained models, [4] use prepackaged virtual machine (VM) images to quickly set up your AI environment, and [5] share AI components with your colleagues.
• Kubeflow Pipelines ML workflows on Kubernetes
• Google AutoML
  • Cloud Vision
  • Cloud Vision API - drag & drop picture on webpage
• Automated Learning
• We’re making tools and resources available so that anyone can use technology to solve problems | Google AI
• Dopamine - reinforcement learning algorithms
• Google AI Experiments
• ML Engine
• Prediction API
• Grow with Google
• Learn from ML experts at Google
• Google Internet of Things (IoT)
• Psychlab ...testing virtual agents in 3D environments
• Deep Reinforcement Learning (DRL) ...Importance Weighted Actor-Learner Architecture (IMPALA)
• Google AIY Projects Program ... connect Vision or Voice Kit to a Wi-Fi network right from mobile device
• Android Neural Networks API ...machine learning on Android devices
• Big\Table ...designed with a storage engine for machine learning applications leading to better predictions
• Pub/Sub ...messaging and ingestion for event-driven systems and streaming analytics.
• BiqQuery ML (BQML) ...enables users to create and execute machine learning models in BigQuery by using standard SQL queries.
• Firebase ...mobile and web functionality like analytics, databases, messaging and crash reporting
• DialogFlow ...creating conversational AI applications, including chatbots, voicebots, and IVR bots
• Google Analytics ...measure your web traffic
• Teachable Machine ...train a computer to recognize your own images, sounds, & poses
• Google Coral ...toolkit to build products with local AI. Our on-device inferencing capabilities
• Google Sheets ...access, create, and edit your spreadsheets wherever you go — from your phone, tablet, or computer
• Google Facets ...contains two robust visualizations to aid in understanding and analyzing machine learning datasets
• DeepMind - Open Source
  • Google DeepMind AlphaGo Zero
  • Google DeepMind AlphaStar
  • Protein Folding & Discovery ...Google DeepMind AlphaFold
• People + AI Research (PAIR) ... devoted to advancing the research and design of people-centric AI systems.

AI Infrastructure on GCP (Cloud Next ‘19 UK) Google Cloud’s AI-optimized infrastructure is setting performance records. From a wide range of our GPU accelerators, to our custom-built supercomputers, Cloud TPU Pods, GCP offers a modern infrastructure for your ML workflows. Learn how you to get started with these accelerators through GCP products and services.

Cloud AI & ML (Google Cloud Talks by DevRel) Our high-quality, scalable, continuously improving, and fully managed AI services put you in a position to innovate faster and more efficiently than the competition. Join our Developer Advocate team to get a comprehensive view of Google Cloud AI platform and solutions and learn how it can help turn your ideas to deployment rapidly and efficiently. Learn more about Google Cloud AI Platform: http://cloud.google.com/ai-platform

The Path From Cloud AutoML to Custom Model (Cloud Next '19)

by Sara Robinson

The Path From Cloud AutoML to Custom Model (Cloud Next '19) What comes after AutoML? You've created some models in Cloud AutoML, and they've been useful. But you want to see if there's some room for more improvement and customization. Let's see how to start building custom models and deploy them in production. You'll learn how to take advantage of state-of-the-art models, all while advancing your understanding of your data pipelines and machine learning. Cloud AutoML to Custom Model → http://bit.ly/2UhnfL7 Watch more: Next '19 ML & AI Sessions here → http://bit.ly/Next19MLandAI Next ‘19 All Sessions playlist → http://bit.ly/Next19AllSessions Subscribe to the GCP Channel → http://bit.ly/GCloudPlatform Speaker(s): Yufeng Guo, Sara Robinson

Building custom models in AutoML (DevFest 2019) Kevin Nelson (@knelsoncloud), Developer Advocate for Google Cloud, discusses Machine Learning and building custom models in AutoML. Watch Kevin demonstrate building custom vision and structured data models during this talk. Links: Video Intelligence → https://goo.gle/2RqFWYv Vision → https://goo.gle/2u7h7ss Speech → https://goo.gle/2R32gYX Natural Language → https://goo.gle/2sEDvsY Translation → https://goo.gle/2R32CPh AutoML Tables → https://goo.gle/2FYeZWu AutoML → https://goo.gle/2R3nUwj Bank Marketing → https://goo.gle/2R2aH6Q DevFest on demand 2019 → https://goo.gle/372VJ69 Subscribe to Google Developer Groups → https://goo.gle/GDevGroups