Almost everyone agrees that artificial intelligence (AI) is the next big thing for businesses. But, is there any AI platform ready for you to deploy? While IBM’s Watson is the most widely known AI platform, there are many more popular artificial intelligence tools for you to hire.
Amazon Web Services (AWS) offers a good range of AI services including tools, platforms, frameworks and infrastructure. Amazon Machine Learning is a cloud-based service that makes machine learning technology available to users of all skill levels. Using powerful algorithms, it helps users create machine learning models by finding patterns in existing data, and using these patterns to make predictions from new data as it becomes available. Offering a user-friendly interface utilising visualisation tools, wizards and more, the highly scalable platform enables creation, evaluation and deployment of meaningful machine learning (ML) models. AWS also provides AI services like image recognition, text-to-speech, voice and text chatbots, and more.
This service is widely used in applications like demand forecast, user activity prediction, item recommendation, fraud detection and suspicious transaction flagging, review filtering and so on. Amazon Machine Learning’s secret of ease lies in its automated processing in every step.
Experts suggest that this platform is well-suited for users who are looking for quick and powerful yet low-cost solutions. It is also a recommendable platform for relatively newer users. It can be used for retail, food chain, hospitality, sports and entertainment, and finance among other sectors.
Let’s look at the example of Upserve—a US-based company that delivers cloud-based intelligence and management system for restaurants. Amazon Machine Learning, ingesting data like reservations, real-time payment processing, menu preference histories, customer count and so on, developed over a hundred machine learning models, which helped Upserve’s client restaurants to improve profitability and drastically cut down on wastage based on major predictive parameters.
Google Cloud Machine Learning Engine
Google Cloud ML platform has made a mark very quickly despite being a much newer release than Amazon or Azure ML platforms. This platform attends to developers’ desire of forging their own machine learning model—no matter how complex or how large. This ability comes from the TensorFlow system—Google’s open source deep learning framework—lying at the heart of Google’s machine learning platform.
The massively scalable machine learning engine backed by AI APIs for speech recognition, advanced search, natural language processing, video, image and text analysis, and much more, gives users a machine learning model with benefits like accurate real-time analytics, efficient anomaly detection and correction, prediction, easy search and recommendations, meaningful insight into complex unstructured data, automated processes and so on.
Customers of any stature ranging from search engines, online retail, job search sites, sales or marketing up to bioengineering or even aerospace and defence firms will find Google Cloud ML platform suitable for their requirement. The biggest confidence in using Google’s offerings comes from the fact that the global giant itself uses the same technologies in its own systems as offered to its customers.
In a real-life scenario, Google’s ML platform has benefited the likes of Airbus Defense and Space in detection and correction of satellite images. Even at high quality level, satellite images capture phenomena like cloud formation, which hinder the real motive of the picture like accurate atmosphere or seasonal analysis—something which is used in areas like precision farming, yield prediction or crop health analysis, where data is required to be absolutely precise. At Airbus, the process had been manual, time-consuming and error-prone for over a decade. Google’s solution automated it and delivered to Airbus customers results with absolute accuracy and detailed insight. Thus, Google’s platform is capable of delivering solutions for any scale of complexity.
USA-based IPSoft created an intriguing AI innovation, called Amelia. The interesting aspect of Amelia is its interaction capabilities coupled with conversation and advanced learning talents that push it closer to the true sense of AI. Geared with advanced learning and self-learning tools, machine learning and deep learning capabilities, understanding of entire sentences and conversations with knowledge of 20 languages, and facial expression and emotion detection, the system can extract knowledge from large documents and historical records, interact directly with its employer’s customers, educate human colleagues, oversee and streamline workflow in a management role, and do much more. The applications of Amelia put a major focus towards digital workforce management.
Chetan Dube, founder and CEO, IPSoft, says, “Amelia is neither a platform, nor a solution. It is a digital employee who has been designed with intelligence, reasoning and decision-making skills that help her more closely resemble the way humans think, act, and work. Amelia is a scalable digital workforce that can address as much work volume as needed. She has a direct impact on the key ROI metrics that shareholders and management teams prioritise above all others. Amelia’s ability to learn more quickly, manage more complex dialogues, respond to analytical triggers in real time, and better understand those with whom she interacts with truly sets her apart as a valuable asset for any modern enterprise.”