We'll leave the actual model creation and optimization processes to the experts: BigQuery ML and AutoML Tables. Even better, we'll train two different models and select the one that performs better with our dataset. Before we dive into the pipeline, let's take a quick look at the tools we'll rely on for model development: BigQuery ML (BQML) lets you create and execute machine learning models in BigQuery using standard SQL queries while leveraging BigQuery's petabyte scale. BigQuery ML democratizes machine learning by letting SQL practitioners build models using existing SQL tools and skills. AutoML Tables is even more hands-off.

Google has devised a machine learning (ML) model that predicts disk failures with 98 per cent accuracy. The idea is to reduce data recovery work when disks actually fail. According to a Google blog by technical program manager Nitin Agarwal and AI engineer Rostam Dinyari, Google has millions of hard disk drives (HDDs) under management, some of which fail. "Any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services." When a disk in Google's data centres encounters non-fatal problems, short of an actual crash, then data is (drained) read from the drive. The drive is then disconnected from production use, they apply diagnostics and it is fixed and returned to production.

Google has one of the largest machine learning stacks in the industry, currently centering on its Google Cloud AI and Machine Learning Platform. Google spun out TensorFlow as open source years ago, but TensorFlow is still the most mature and widely cited deep learning framework. Similarly, Google spun out Kubernetes as open source years ago, but it is still the dominant container management system. Google is one of the top sources of tools and infrastructure for developers, data scientists, and machine learning experts, but historically Google AI hasn't been all that attractive to business analysts who lack serious data science or programming backgrounds. The Google Cloud AI and Machine Learning Platform includes AI building blocks, the AI platform and accelerators, and AI solutions.

Leveraging machine learning to process data and workloads has proved to be significantly beneficial for diverse enterprise industries in recent years. Whether it be healthcare, BFSI or retail, machine learning systems turned out to be extremely promising to process millions of data and build complex models. Having said that, the traditional machine learning process involves humans to look after the operations, to code, and to build the models. But, with the crisis in hand, businesses are looking to reduce their workforce, some are even not equipped with resources to spend on employing an experienced data science team. And that's when AutoML can come to rescue for many.

Artificial intelligence is set to transform global productivity, working patterns, and lifestyles and create enormous wealth. Research firm Gartner expects the global AI economy to increase from about $1.2 trillion last year to about $3.9 Trillion by 2022, while McKinsey sees it delivering global economic activity of around $13 trillion by 2030. AI techniques, especially Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another -- image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation -- just to name a few. They are being used in -- healthcare, I.T. services, finance, manufacturing, autonomous driving, video game playing, scientific discovery, and even the criminal justice system. However, they are like anything but classical Machine Learning (ML) algorithms/techniques.

As expected, Google used the second day of its annual Cloud Next conference to shine a spotlight on its AI tools. The company made a dizzying number of announcements today, but at the core of all of these new tools and services is the company's plan to democratize AI and machine learning with pre-built models and easier to use services, while also giving more advanced developers the tools to build their own custom models. The highlight of today's announcements is the beta launch of the company's AI Platform. The idea here is to offer developers and data scientists an end-to-end service for building, testing and deploying their own models. To do this, the service brings together a variety of existing and new products that allow you to build a full data pipeline to pull in data, label it (with the help of a new built-in labeling service) and then either use existing classification, object recognition or entity extraction models, or use existing tools like AutoML or the Cloud Machine Learning engine to train and deploy custom models.