Review Content of Let’s talk AI 2019 from Google Cloud

Review Content of Let’s talk AI 2019 from Google Cloud
Rate this post

Google AI solving real business problems
Google AI solving real business problems

In 2019, Google AI will solve real business problems about: Retail, Energy, Financial Services, Helthycare & Life Sciences, Media & Entertainment, Government, Manufacturing, Technology.

Contact center AI
Contact center AI

Contact center AI will increase human ingenuity.

Google cloud coop iron mountain
Google cloud coop iron mountain

Document understanding will better by the coop of Google Cloud and Iron mountain.

P/S: 95% of the Fortune 1000 choose Iron Mountain

Powerful AI building blocks
Powerful AI building blocks

Powerful AI building blocks included: Vision, Natural Language, Text-to-speech, Translattion, Speech-to-Text, Video Intelligence.

Cloud AI Platform
Cloud AI Platform

Cloud AI Platform this included: Cloud ML Engine + Kubeflow + Cloud TPUs

10x speed
10x speed

10x this is the impressive number of Google AI speed in 2019

Structure of google AI
Structure of google AI
AI hub alpha
AI hub alpha

In Let’s talk AI, Google Cloud introducing AI Hub Alpha.

AI hub where to place
AI hub where to place

AI Hub holds your content from many places: Kubeflow Pipelines, Cloud Video Intelligence, Cloud Translation, Cloud Vision, Kaggle, Cloud AutoML, Cloud Text-to-Speech, Cloud Speech-to-Text.

who can actually use AI today
Who can actually use AI today?

Very few users today can create a custom ML model, Google need to make AI accessible to millions more:

21 milions Developers, 1000’s Deep learning researchers, 1 milions Data scientists.

 

 

Principles of big data in the cloud: The foundation for AI

Cloud computing is changing the way we approach each phase of the data management lifecycle with positive implications for a forward-looking AI strategy. Learn how a serverless, integrated, end-to-end platform can serve today’s analytics needs while preparing for the AI requirements of tomorrow.
by 2021 75% of enterprise aplications will use AI
by 2021 75% of enterprise aplications will use AI

AI made easy: Pre-trained models and Cloud AutoML

Today we’re seeing revolutionary changes in hardware and software that are making machine learning accessible to any developer or data scientist. Whether you’re new to ML or you’re already an expert, GCP has a variety of tools to help you. We’ll start with the basics: how to use a pre-trained ML model with a single API call. Then you’ll learn how to customize a pre-trained model with Cloud AutoML.

The intelligent Internet of Things: Google Cloud’s IoT vision

Though devices today can be connected to a network, building and managing such networks securely while extracting data for analysis can be complicated and time-consuming. Learn how Google Cloud IoT platform accelerates digital transformation by helping you manage your globally dispersed devices and enabling you to uncover actionable insights in real time from your global device network.

The zen guide to preparing your data for ML

Before you know what to do with your data, you have to know your data. Properly preparing your data for machine learning requires the ability to visually explore, clean, and prepare both structured and unstructured data. This process of “data wrangling” ensures data professionals move in the right direction from the start: the raw data.
3 type of google AI
3 type of google AI

TPUs for developers: Getting started on Google Cloud TPUs

The hottest topic in computer science today is machine learning and deep neural networks. Many problems deemed “impossible” only five years ago have now been solved by deep learning: playing GO, recognizing what is in an image, translating languages. Software engineers are eager to adopt these new technologies as soon as they come out of research labs and the goal of this session is to equip you to do so. This session will focus on the newest developments convolutional neural network architectures and give you tips, engineering best practices and pointers to apply these techniques in your projects.

IoT at the Edge: Bringing intelligence to the edge using Cloud IoT

Cloud IoT Edge extends Google Cloud’s powerful data processing and machine learning to billions of edge devices from robotic arms to wind turbines. Performing inference locally on edge devices reduces the latency and cost of sending device data to the cloud to make a prediction. Join us to learn how Google Cloud IoT makes it easy to deploy your machine learning model from the cloud to your devices.

Data for AI with real-time data processing

Making AI real means making AI real-time. Artificial intelligence can enable businesses to make data-driven decisions in sub-second windows — but only if their analytics platform can keep up. Learn how Google Cloud’s stream analytics tools can ready your data pipeline for the speed and formats that AI demands.

Moving AI off your product roadmap and into your workstreams

Discover the breakthrough solutions that have been built by Google Cloud AI to help you inject AI into your workstreams, real world problems in various industries leveraging Google’s unique expertise in machine learning, security, and more.

Predictive Maintenance using Cloud IoT Core and Machine Learning

Due to the high-risk nature of manufacturing operations, the only solution for never-fail situations has been to over-compensate for uptime with redundant equipment and too many parts on hand. It’s an unsustainable model. Many companies rely on systems that track performance of various components using sensors, but have no proactive way to trigger maintenance to avoid downtime. In this session, you will learn about a solution that leverages Google Cloud IoT Core to read sensory data and predict the Remaining Useful Life (RUL) of components. See an example of an end-to-end solution that securely reads sensor data in real time, processes the data, and executes a machine learning model to make predictions.

Google BigQuery: The data warehouse for an AI-first paradigm

Data warehouses store the most valuable data for most organizations, and yet they are not often optimized for AI solutions. BigQuery, Google’s cloud-scale data warehouse, gives users the ability to consolidate their data silos and build one source of truth for both AI and traditional analytics. Complete with built-in capabilities for machine learning, BigQuery scales without compromise and accelerates time-to-insight for advanced analytics and AI. Learn how your data warehouse can help you jump-start your AI solutions.

Building Conversational Experiences More Quickly and Easily with Dialogflow

Conversational experiences are becoming the default way for users to interact with machines, but their expectations for natural and rich interactions are high. In this session, you’ll learn how customers across diverse industries used Dialogflow Enterprise Edition to more quickly and easily build such experiences without the need for background in natural language understanding and AI.

Building real-time IoT application using Cloud Firestore

Developers building stateful IoT devices today are confronted with the hard challenge of building the infrastructure to set, manage, and sync device state and then visualize this data in real-time. Connectivity to these devices is intermittent, device security can be hard to get right, and consistency across a global deployment can be challenging to achieve and reconcile. What if there was a way to simplify all of this? In this session, learn how Cloud Firestore will help dramatically simplify these challenges by providing support for offline sync, strong consistency, and local device storage and then enabling all of this to be visualized in real time.

Posted on