Machine Learning Applications To Give Your Startup A Competitive Edge

Machine Learning Applications To Give Your Startup A Competitive Edge

Google wants a chunk of the $2.7 billion healthcare market, and its banking on machine learning powered startups to achieve that end. Intended as a broader initiative to promote machine learning among startups, Google has organized Launchpad Studio, an accelerator engine that provides promising new companies with access to equity free support, mentors, community support and prototyping resources. Launchpad Studio’s initial mission is to support startups in the healthcare and biotech space in order to bridge the gap between health applications and frontier technology. So far, Google has revealed four companies that are participating in the project. One company, Augmedix, is speeding up doctors’ schedules by using Google Glass scribing and natural language processing to automate scanning of patient data.

machine learning

Whatever industry you’re in, machine learning can give you a technological advantage over your competition. The machine learning market is expanding at an explosive annual compound growth rate of 44.1 percent and will be worth $8.81 billion by 2022, markets and markets projects. Here are three ways your startup can get in on the ground floor of the machine learning revolution in order to gain a jump on your competition.

Use An AI-compatible Infrastructure

Machine learning applications require a significant amount of computing power to process artificial intelligence (AI) applications and big data rapidly, so to use them effectively, it’s essential to use an AI-compatible infrastructure. There are three main ways to do this. The most expensive and least practical is to build your own on-premise data center with sufficient server space. For most startups, a more viable option is to tap into remote cloud computing infrastructure, platforms and apps. For instance, Google’s Cloud AI platform now offers machine learning as a service, including pre-trained machine learning models as well as the capability to build your own customized models.

However, transferring data between your local network devices and the cloud can slow down machine learning applications. So, an even faster solution beginning to emerge is on-device AI that can run machine learning applications directly on mobile devices. For example, Qualcomm’s new artificial intelligence platform is designed to handle 5G network speeds and is powerful enough to run machine learning applications such as biometric facial recognition, smart camera recognition and streaming virtual reality directly on mobile devices. This means you don’t have to invest thousands of dollars in building a cloud computing infrastructure. You can now access these apps from your very own smartphone, which provides startups with an affordable and viable solution.

Use Machine Learning To Drive Data Based Decisions

One of the most strategic deployments of machine learning is using insights derived from big data to drive your business decisions. Basing your business decisions on objective data instead of subjective intuition can lend you far more accurate insights into your company, customers and competition that translate into superior performance. Over half of companies in early and mature stages of machine learning adoption are already seeing a return on investment, and one in four are already gaining a competitive edge as a result of implementing machine learning, an MIT survey found.

You can use machine learning to process data from any area of your company as well as your market and your customer database. Leading machine learning platforms include Amazon Machine Learning, Google Cloud, IBM Watson Machine Learning and Microsoft Azure Machine Learning.

Integrate Machine Learning Into Your Operational Procedures

One powerful way to apply machine learning’s power to improve decision making is to use it to optimize your operational performance. For example, Salesforce’s Einstein platform lets you use machine learning to predict your sales performance, determine which products customers most want, identify which prospects are most ready to buy and automatically manage your sales force to match your best available representatives to your hottest prospects.

Similarly, machine learning can help you optimize your customer service. For instance, Zendesk Explore uses machine learning to help you understand how your customers use your support channels, identify which customers need extra attention and optimize management of your support team.

Adopting an AI-compatible infrastructure, using machine learning to drive your decisions and integrating machine learning into your operations will enable you to maximize the value you gain from deploying machine learning applications. Implementing these strategies will help you improve your operational efficiency, gain an edge on your competition and grow your profits.