On April 10, pharmaceutical giant Merck announced the launch of the Alexa Diabetes Challenge, a competition aimed at encouraging the development of software applications that use Amazon’s Alexa technology to help patients with a recent diagnosis of Type 2 diabetes. The competition builds upon Merck’s exploration of the capabilities of Amazon Lex, the machine learning technology that undergirds Amazon Alexa by facilitating the development of “conversational interfaces” for applications, to enhance and enrich capabilities to manage and ameliorate chronic diseases. Kimberly Park, Vice President, Customer Strategy & Innovation, Global Human Health, Merck, remarked on the significance of Merck’s usage of Amazon Web Services to address chronic diseases as follows:
Merck has a deep heritage of tackling chronic diseases through our medicines, and we have been expanding into other ways to help, beyond the pill. We are excited to leverage the AWS Cloud to find innovative ways to leverage digital solutions, such as voice-activated technology, to help support better outcomes that could make a difference in the lives of those suffering from chronic conditions like diabetes.
Here, Park comments on Merck’s expansion into modalities of treatment that range “beyond the pill” in what amounts to a disruptive expansion of the company’s traditional business model. Powered by Luminary Labs and sponsored by Merck, the Alexa Diabetes Challenge will provide incentives for selected applicants to use Amazon Alexa as well as Amazon Web Services. In the first round of the competition, five entrants will be awarded $25,000 in addition to $100,000 in AWS credits. The entrants will subsequently receive access to mentoring resources about their proposed solutions and have the opportunity to further develop and refine their apps before vying for the grand prize of $125,000. The Alexa Diabetes Challenge illustrates increased interest in exploration of the intersection of machine learning, cloud computing and healthcare on the part of both technology companies as well as healthcare organizations and pharmaceutical firms. Moreover, in the case of AWS, the collaboration with Merck underscores Amazon CEO Bezos’s interest in embracing the contemporary trend of machine learning and artificial intelligence as elaborated in a recent letter to AWS shareholders. Learn more about the Alexa Diabetes Challenge here.
Alibaba Cloud has announced the release of PAI 2.0, an updated version of a platform designed to facilitate the deployment of “large-scale data mining and modeling,” with a specific focus on artificial intelligence and machine learning. Alibaba Cloud’s PAI 2.0 represents China’s first publicly available machine learning platform that encompasses use cases for its “ET Industrial Brain” related to manufacturing, optimized device and sensor configuration, energy utilization management and analytics related to the industrial internet of things, more generally. Separately, Alibaba has announced details of an “ET Medical Brain” that specializes in use cases related to drug discovery, patient management, hospital and clinical facility management as well as the deployment of virtual medical assistants to help patients interact with clinical protocols and tests. PAI 2.0 features over 100 pre-configured machine learning algorithms that can be adapted for different use cases and scenarios. The announcement of PAI 2.0 underscores the intersection between cloud platforms and machine learning technologies as cloud infrastructures increasingly seek to differentiate their platforms with value-driven analytic, coding and data management capabilities. PAI 2.0 allows Alibaba Cloud to claim parity with the likes of AWS, Azure, Google Cloud and IBM SoftLayer with respect to advanced machine learning functionality although the sophistication and ease of use of its algorithms and deep learning technologies remains in the process of discovery and realization by its customers.
In this video, Microsoft Corporate Vice President reflects on the intersection between machine learning and neuroscience at Strata and Hadoop World 2016.
On May 9, Kaseya announced the release of Kaseya Traverse 9.3, an IT management platform for service providers that supports the management of hybrid cloud infrastructures. Kaseya’s proprietary machine learning and predictive analytics technology enables customers to identify the root cause of performance issues within their infrastructure. The Kaseya platform proactively identifies causes of performance degradation by means of analytics on infrastructure and application performance. The machine learning-based qualities of Kaseya’s infrastructure allows the platform to recognize the specificity of each customer’s infrastructure and iteratively refine analytics on anomalous or aberrant behavior that can embrace the heterogeneity of IT infrastructures and their corresponding implementations. Mike Puglia, chief product officer of Kaseya, remarked on the value of Kaseya Traverse with respect to hybrid cloud deployments as follows:
In today’s complex hybrid cloud environments, MSPs, SMBs and large enterprises alike require a solution such as Traverse to help them reduce downtime for their IT services. The days of monitoring servers and routers in an isolated silo are gone. Businesses today require tools such as Traverse that offer real-time tracking and correlation of the business impact these devices have on overall IT services.
Here, Puglia comments on how Kaseya delivers a holistic approach to cloud monitoring that dispenses with siloed methods of understanding the performance of servers and routers as illustrated by the graphic below:
The screenshot above gives customers visibility into network traffic within a hybrid cloud infrastructure across multiple environments and infrastructure components. Kaseya Traverse 9.3 supports more than 40 new devices and continues to enhance its monitoring and analytic capabilities for Managed Service Providers and small to midsize businesses. The platform currently supports the Amazon Web Services public cloud in addition to other vendors such as Nimble Storage and Dell Compellent. As the space dedicated to cloud-based monitoring solutions continues to evolve, Kaseya Traverse 9.3 will need to continue sharpening its product differentiation in order to effectively complete against a proliferation of vendors that offer IT management solutions for hybrid cloud infrastructures. In the here and now, however, the platform’s impressive machine learning capabilities enable it to scalably embrace the radical heterogeneity of contemporary IT infrastructures in ways that swiftly support root cause analytics and the proactive resolution of IT performance issues.
Google recently announced the alpha release of Cloud Machine Learning, a managed, cloud-based framework for building machine learning models by using the TensorFlow framework that undergirds products such as Google Photos and Google Speech. Google’s Cloud Machine Learning platform features a Cloud Vision API that can categorize images into over a thousand categories such as “tree,” “book” and “car” and additionally identify “individual objects and faces within images” as well as read print within images. The platform also features a Cloud Speech API that can transcribe speech into text by using neural network models. Moreover, Google Cloud Machine Learning contains a Google Cloud Translate API that can translate source language into a supported target language, such as French to Japanese, for example. Google Cloud Machine Learning integrates with Google Cloud Dataflow in addition to data stores from Google Cloud Storage and Google BigQuery. By offering pre-trained machine learning models in conjunction with the capability to build customized models for specific scenarios and use cases, the platform delivers predictive modeling capabilities that can scale to support terabytes of data and rapidly proliferating data sources. Google Cloud Machine Learning competes with Amazon Machine Learning and Hewlett Packard Enterprise Haven On Demand, the latter of which is now commercially available on the Microsoft Azure platform. The alpha release of Google Cloud Machine Learning further illustrates Google’s investment in its Google Cloud Platform and the depth of its commitment to building an increasingly competitive position in the contemporary cloud computing market landscape.
On October 21, Aviso announced the release of the Aviso Virtual Sales War Room, a platform that delivers granular sales analytics to help sales leaders achieve their goals. The Aviso Virtual Sales Warm Room leverages a combination of machine learning and advanced analytics to develop scenarios for executing sales campaigns that are updated in real-time in conjunction with contemporary updates to sales prospects. The platform features the ability for sales professionals to update the status of sales deals as they progress, scenario modeling functionality to assess the impact of updates to the status of deals, real-time notification capabilities to allow sales teams to effectively collaborate throughout the sales process and data about deals that are in the process of concurrent negotiations. Aviso’s technology brings the power of predictive analytics to forecast which deals will close, when and under what terms, and subsequently refines those algorithms using machine learning technology that iteratively understands the rhythms of each sales team and their style of closing deals. The platform’s proprietary analytics based on CRM data, emails and calendar appointments, market data and social media delivers a degree of analytic sophistication to decisions about sales operations that help sales leaders reach their targets and make the best decisions for their portfolio of prospects. As such, the Aviso Virtual Sales War Room promises to disrupt the operational process of sales execution by harnessing the power of advanced analytics and machine learning to improve sales performance.
Amazon Web Services recently announced the availability of machine learning technology that allows developers to create predictive analytics by using the same algorithms that Amazon uses to manage its supply chain inventory and operations. Amazon’s Machine Learning platform empowers developers and data scientists with the ability to identify patterns and predictive analytics for data stored in Amazon S3, Amazon RedShift and Amazon Relational Database Service. By using Amazon Machine Learning, developers can obtain analytic insights without writing custom-built predictive analytics-based applications that require complicated scripting, debugging, code deployment and application management. In addition to enjoying the benefits of preconfigured machine learning libraries and wizards that accelerate access to pattern recognition and predictive analytics, Amazon Machine Learning enables customers to enjoy the benefits of a scalable platform capable of generating billions of predictions per day in real-time. Popular use cases for Amazon’s Machine Learning technology include the detection of fraud, the ability to personalize web-related content and deliver targeted marketing campaigns that iteratively become more effective in conjunction with the evolving sophistication of the predictive model. Amazon’s Machine Learning platform competes directly with Microsoft Azure’s Machine Learning platform that was released in public preview in July 2014. By rendering available the same technologies used by Amazon’s data scientists, Amazon adds yet another incentive for customers to leverage its ever expanding portfolio of cloud and big data products and services. The increasing availability of machine learning technologies underscores the democratization of analytics enabled by the contemporary cloud and big data revolution, even though many of the solutions available on the market remain proprietary and attached to usage of a larger IaaS platform.