Introduction

What is GCP?

Google is very good with data, it’s kind of the essence of what they do. Google Cloud Platform is essentially the packaging and commoditization of this expertise in data that’s been built over the years

GCP Timeline

1998 Google was founded.

2008 Google made available a data offering called the App Engine which was focused on development and deployment of custom web applications.

2011 App Engine evolved into being ready for general availability, along with other key services like cloud storage.

2022 Google Cloud Platform (GCP) has well over 100 unique services continuing to grow continuing to develop and distribute distributed globally.

GCP Services: AI for Data Scientists

Vertex AI

Description: Our new unified machine learning platform will help you build, deploy and scale more effective AI models.

Accelerating data preparation

Scaling data

Training and experimentation

Model deployment

Vertex AI Workbench

Description: The single development environment for the entire data science workflow. 

Rapid prototyping and model development.

Developing and deploying AI solutions on Vertex AI with minimal transition.

GCP Services: AI for Developers

AutoML

Description: Train high-quality custom machine learning models with minimal effort and machine learning expertise.

Building custom machine learning models in minutes.

Training models specific to your business needs.

Cloud Inference API

Description: Uncover insights from large scale, typed time-series data.

Indexing and loading a dataset consisting of multiple stored data sources.

Executing Inference queries over loaded datasets.

Unloading or canceling the loading of a dataset.

Cloud Natural Language

Description: Derive insights from unstructured text using Google machine learning.

Applying natural language understanding to apps with the Natural Language API

Training your open ML models to classify, extract, and detect sentiment

Dialogflow

Description: Create conversational experiences across devices and platforms.

Creating natural interaction for complex multi-turn conversations

Building and deploying advanced agents quickly

Building enterprise-grade scalability

Media Translation (Beta)

Description: Add real-time audio translation to your content and applications.

Delivering real-time speech translation directly from your audio data

Scaling quickly with straightforward internationalization

Speech-to-Text

Description: Accurately convert speech into text using an API powered by Google’s AI technologies.

Creating automatic speech recognition

Transcribing in real time

Empowering Google Contact Center AI

Text-to-Speech

Description: Convert text into natural-sounding speech using an API powered by Google’s AI technologies.

Improving customer interactions 

Engaging users with voice user interface in devices and applications

Personalizing communication 

Timeseries Insights API (Preview)

Description: Large-scale time series forecasting and anomaly detection in real time.

Gathering insights in real time from time series datasets

Detecting anomalies while they are happening

Handling large scale datasets and running thousands of queries per second

Translation AI

Description: Make your content and apps multilingual with fast, dynamic machine translation.

Delivering seamless user experience with real-time translation

Engaging your audience with compelling localization of your content

Reaching global markets through internationalization of your products

Video AI

Description: Enable powerful content discovery and engaging video experiences.

Extracting rich metadata at the video, shot, or frame level

Creating your own custom entity labels with AutoML Video Intelligence

Vision AI

Description: Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more.

Using ML to understand images with industry-leading prediction accuracy

Training ML models to classify images by custom labels using AutoML Vision

GCP Services: AI Infrastructure

Deep Learning Containers

Description: Preconfigured and optimized containers for deep learning environments.

Prototyping your AI applications in a portable and consistent environment 

Deep Learning VM Image

Description: Preconfigured VMs for deep learning applications.

Accelerating your model training and deployment

GPUs

Description: High-performance GPUs on Google Cloud for machine learning, scientific computing, and 3D visualization.

Speeding up compute jobs like machine learning and HPC

Accelerating specific workloads on your VMs

TensorFlow Enterprise

Description: Reliability and performance for AI applications with enterprise-grade support and managed services.

Boosting enterprise development with long-term support on specific distributions

Scaling resources across CPUs, GPUs, and Cloud TPUs

Developing and deploying TensorFlow across managed services

TPUs

Description: Train and run machine learning models faster than ever before.

Running cutting-edge machine learning models with AI services on Google Cloud

Iterating quickly and frequently on machine learning solutions

Building your own ML-powered solutions for real-world use cases.