• Cloud Transformation
  • Data and AI

Article March 6th, 2019

by Nortal

Machine Learning 101: How it works and why It matters

With 90% of the world’s data created in the past couple of years, at a clip of 2.5 quintillion bytes a day, many businesses and industries still struggle to harness the information they need to improve outcomes. Fortunately, data scientists and analytics specialists in the emerging field of machine learning (ML) are creating new tools to mine data for transformational business results.

Considered a branch of artificial intelligence (AI), ML meshes systems and algorithms that learn from various data and make predictions without being specifically programmed. ML can discover patterns that are humanly impossible to see and create valuable predictive models that help tackle practical problems.

Cloud computing is fueling the adoption of ML with its infinite data storage and computing power. The cloud enables ML to analyze very large data sets more quickly and affordably. Today’s top public cloud service providers (CSPs), such as Google Cloud Platform (GCP), incorporate ML services into their offerings with analysis for seek/search, video, speech, language, and chatbot functionality.

Changing the game for major industries

ML’s reach has stretched into areas as diverse as transportation (self-driving vehicles) and financial services (automated monthly closings) in recent years. Technology thought leaders such as Bill Gates and Elon Musk point to AI and ML as two of the most important technologies for the future.

At Airbnb, Mike Curtis, vice president of engineering, says the effect ML has had on the company’s unique challenge — creating great matches between guests and hosts — is profound.

His company introduced ML in 2014 to personalize search ranks. Previously, matches were determined by a set of hard-coded rules based on very basic signals, such as the number of bedrooms and price. Airbnb now factors in signals about the guests themselves, as well as guests similar to them, to personalize searches and create better outcomes.

Over at Uber, the engineering team has developed Michelangelo, an internal ML-as-a-service platform that scales to meet the needs of its rapidly growing ride-sharing service. The company uses ML to determine arrival times, pickup locations, and UberEATS’ meal deliveries. Several UberEATS models run on Michelangelo, covering meal-delivery time predictions, search rankings, search autocomplete, and restaurant rankings. The delivery time models predict how much time a meal will take to prepare and deliver before the order is issued and then again at each stage of the delivery process.

How GCP is democratizing ML technologies

Today, Google Cloud Platform offers developers fast, large-scale, and easy-to-use AI services that incorporate modern ML tools. GCP possesses the world’s biggest trove of data thanks to Google Chrome and more than 2 trillion searches a year. GCP offers ML tools that are feature-rich and simple to use. It supports all major ML frameworks, such as TensorFlow, Caffe2, Apache, and MXNet, and includes pre-trained models and a service to generate tailored models.

To help developers get off to a fast start, GCP offers AutoML Vision, a flexible and secure ML service that enables developers to train custom vision models, or Google Cloud Machine Learning Engine, a large-scale managed ML learning service with a pay-as-you-go model. For more specific uses, GCP offers Google Cloud Job Discovery for job seeker/search analysis; Dialogflow Enterprise Edition, Google Cloud Text-to-Speech, Google Cloud Natural Language and Google Cloud Translation API for speech and language analysis; and, Google Cloud Video Intelligence API and Google Cloud Vision API for video and image analysis.

At Nortal, we are GCP Premier Partners. We use our deep expertise to help our customers take advantage of these ML tools from Google to transform the way they do business. Our specialty is building secure cloud infrastructures that maximize investment through a cohesive, high-confidence deployment pipeline using continuous delivery principles.

To learn more about leveraging ML tools from GCP, contact us!

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