Two projects sponsored by Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) have generated solid use cases for machine learning in clinical care.

Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) collaborated in August 2019 to advance innovation in areas including cancer diagnostics, precision medicine, electronic health records, and medical imaging. 

Through the collaboration, researchers from the University of Pittsburgh Medical Center (UPMC), University of Pittsburgh, and Carnegie Mellon University (CMU) received support from Amazon Search Awards on top of existing support from PHDA to use machine learning to dive into various projects.

One of those projects examined machine learning techniques to help experts study breast cancer risk and understand what drives tumor growth. 

Led by Shandong Wu, an associate professor at the University of Pittsburgh department of radiology, a research team analyzed 452 normal mammograms from 226 patients in order to predict the short-term risk of developing breast cancer. 

Wu and his team, who included experts in computer vision, deep learning, bioinformatics, and breast cancer imaging, used two machine learning models and found that both models consistently outperformed in the area of breast density.

Specifically, the team’s model demonstrated between 33 percent and 35 percent improvement over the existing models, researchers highlighted. 

“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” Wu said in the announcement. 

“Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective.” 

Another project led by Eva Szigethy, clinical researcher at UPMC and Louis-Phillippe Morency, associate professor of computer science at CMU, used machine learning to measure changes in an individual’s behavior to diagnosis depression.

Their machine learning models are trained on tens of thousands of language, acoustic, and visual modalities to identify biomarkers for depression. The biomarkers will be compared to results from traditional clinical assessments to determine the accuracy of the machine learning models with identifying depression.

“New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole,” Morency stated. 

AWS and PHDA noted that the projects on breast cancer and depression are just the start when it comes to research collaboration to improve patient care. 

Teams of researchers, healthcare professionals, and machine learning experts will continue to work to understand the risk of aneurysms, predict how cancer cells progress, and aim to improve the electronic health records system. 

“Amazon is excited and encouraged by the progress these researchers are making and how machine learning is central to their work,” said An Luo, senior technical program manager for academic programs at Amazon AI. 

“We look forward to continuing to share how this unique collaboration between the PHDA and AWS is enabling new discoveries to help patients on a global scale.”

For example, David Vorp, PhD, associate dean for research at UPMA, and his research team employed AWS cloud resources to boost the diagnosis and therapy of abdominal aortic aneurysms.

And a CMU research team led by Russell Schwartz, PhD, and Jian Ma, PhD, used machine learning to develop algorithms and software tools to better understand cell origin and evolution. 

“With the latest advances in machine learning, we are developing an algorithm that will provide clinicians with an objective, predictive tool to guide surgical interventions before symptoms appear, improving patient outcomes,” Vorp said in the August announcement.

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AWS has announced Contact Center Intelligence (CCI) solutions—a combination of services powered by AWS’s machine learning technology to help enterprises add ML-based intelligence to their contact centers.

AWS CCI solutions let organizations leverage machine learning functionality such as text-to-speech, translation, enterprise search, chatbots, business intelligence, and language comprehension in their current contact center environments. Customers can now implement contact center intelligence machine learning solutions to aid self-service, live-call analytics and agent assist, and post-call analytics.

The new solutions were announced in an Amazon News blog post by AWS developer advocate Alejandra Quetzalli.

Currently, AWS CCI solutions are available through partners such as Genesys, Vonage, and UiPath for integration into existing enterprise contact center systems.

CCI offers contact center solutions through AWS pre-trained machine learning services to aid at a variety of points in the contact center workflow. The solutions are focused on three stages of the contact center workflow: Self-Service, Live Call Analytics and Agent Assist, and Post-Call Analytics.

Self-Service: Amazon Lex and/or Amazon Kendra support self-service by integrating chatbot and ML-driven IVRs (interactive voice response) responses for contact centers’ most common customer questions.
Live Call Analytics & Agent Assist: Enables the creation of real-time machine learning capabilities through Amazon Transcribe and Amazon Comprehend to drive staff productivity and engagement.
Post-Call Analytics: Uses AWS speech and text services, Amazon Transcribe, Translate, Comprehend, and Kendra, to automatically translate and analyze customer conversations for feedback loops, improving customer service.

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Gartner analysts have exhaled a “Magic Quadrant” report on Cloud AI developer services, concluding that while AWS is fractionally ahead, rivals Microsoft and Google are close behind, and that IBM is the only other company deserving a place in the “Leaders” section of the chart.

Gartner’s team of five mystics reckon that this is a significant topic. “By 2023, 40 per cent of development teams will be using automated machine learning services to build models that add AI capabilities to their applications, up from less than 2 per cent in 2019,” they predicted. The analysts also said that 50 per cent of “data scientist activities” will be automated by 2025, alleviating the current shortage of skilled humans.

The companies studied were Aible, AWS, Google, H20ai, IBM, Microsoft, Previson.io, Salesforce, SAP and Tencent. Alibaba and Baidu were excluded because of a requirement that products span “at least two major regions”.

AWS was praised for its wide range of services, including SageMaker AutoPilot, announced late last year, which automatically generates machine-learning models. However, some shortcomings in SageMaker were addressed during the course of the research, said the analysts. It is a complex portfolio, though, and can be confusing. In addition: “When users move from development to production environments, the cost of execution may be higher than they anticipated.” Gartner suggested developers attempt to model production costs early on, and even that they plan to move compute-intensive workloads on-premises as this may be more cost-effective.

Google was ranked just ahead of Microsoft on “completeness of vision” but fractionally behind on “ability to execute”. Gartner’s analysts were impressed with its strong language services, as well as its “what-if” tool, which lets you inspect ML models to assist explainability, the art of determining why a AI system delivers the results it does. Another plus was that Google’s image recognition service can be deployed in a container on-premises. Snags? The report identified a lack of maturity in Google’s cloud platform: “The organization is still undergoing substantial change, the full impact of which will not be apparent for some time.”

Microsoft won plaudits for the deployment flexibility of its AI services, on Azure or on-premises, as well as its wide selection of supported languages and its high level of investment in AI. A weakness, said the analysts, was lack of NLG (Natural Language Generation) services, though these are on the roadmap. The report also noted: “Microsoft can be challenging to engage with, due to a confusing branding strategy that spans multiple business units and includes Azure cognitive services and Cortana services. This overlap often confuses customers and can frustrate them.” In addition, “it can be difficult to know which part of Microsoft to contact.”

IBM is placed a little behind the other three, but still identified as having a “robust set of AI ML services”. Further, “according to its users, developing conversational agents on IBM’s Watson Assistant platform is a relatively painless experience.” That said, like Microsoft, IBM can be difficult to work with, having “different products, from different divisions, being handled by various development teams and having various pricing schemes,” said the analysts.

All four contenders can maybe take some comfort from Gartner’s report, which places the three leaders close together and IBM, with its smaller cloud product overall, not that far behind. Other considerations, such as existing business relationships, or points of detail in the AI services you want to use, could shift any one of them into the top spot for a specific project.

One of the points the researchers highlighted is that it can be cheaper to run compute-intensive workloads on-premises. Using standard tools gives the most flexibility, and in this respect Google’s recent announcement of Kubeflow 1.0, which lets devs run ML workflows on Kubernetes (K8s), is of interest. A developer can use Kubeflow on any K8s cluster including OpenShift. Google said it will support running ML workloads on-premises using Anthos in an upcoming release.

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dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, announced that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status.

dotData achieved AWS ML Competency status only eight months after joining the AWS Partner Network (APN). The certification recognizes dotData as an APN Partner that accelerates the full-cycle ML and data science process and provides validation that dotData has deep expertise in artificial intelligence (AI) and ML on AWS and can deliver their organization’s solutions seamlessly on AWS. dotData achieved AWS ML Competency status through a rigorous qualification process that included a strict validation of their capabilities to demonstrate technical proficiency and proven customer success, as well as a technical audit of its dotData Enterprise solution.

dotData provides solutions designed to improve the productivity of data science projects, which traditionally require extensive manual effort from valuable and skilled resources, by automating the full life-cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies.

dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.

“By leveraging the ease of use of dotData Enterprise and the power of AWS, we were able to develop and deploy our first machine learning models in weeks, without the need for data scientists,” said Phillip Byrnes, Director of Business Intelligence, of US Electrical Services Inc. an independent electrical supply distributor headquartered in Middletown CT. “dotData Enterprise is like having a mathematician in a box, it helps us optimize our business by leveraging our existing Business Intelligence teams and resources. The product is that easy to use.”

“From its foundation, dotData’s vision has been to make AI and ML accessible to as many people in the enterprise as possible. We are proud of the successful experience of US Electrical Services’ BI team, which is a testament to our commitment to truly democratize AI in enterprise,” said Ryohei Fujimaki, founder and CEO of dotData. “Achieving AWS ML Competency status in just eight months recognizes our ability to deliver an outstanding product that dramatically accelerates the AI and ML initiatives of AWS users and maximizes their business impacts.”

AWS ML Competency Partners provide solutions that help organizations solve their data challenges and enable ML and data science workflows. The program is designed to highlight APN Partners who have demonstrated technical proficiency in specialized solution areas and helps customers find the most qualified organizations with deep expertise and proven customer success.

dotData democratizes data science by enabling existing resources to perform data science tasks, making enterprise data science scalable and sustainable. dotData automates up to 100 percent of the data science workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input. dotData is also designed to operationalize data science by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflow. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, the data science task becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.

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Dubai, UAE: Capillary Technologies, one of Asia’s leading consumer engagement and commerce product companies, announced today that it has achieved the Amazon Web Services (AWS) Retail Competency status.

AWS Retail Competency Partners have demonstrated success in offering end-to-end solutions across Customer Engagement, Supply Chain and Distribution, Physical, Digital, and Virtual Store, Advanced Retail Data Science, Core Retail Business Applications, and Consulting Practices for Retail on AWS.

AWS Retail Competency Partners undergo rigorous validation by AWS to ensure alignment to AWS best practices for building secure, high-performing, resilient, and efficient cloud infrastructure for industry applications to give customers an increased confidence when making decisions.

Achieving the AWS Retail Competency differentiates Capillary Technologies as an AWS Partner Network (APN) member that delivers highly specialized technical proficiency for Retail. To receive the AWS Competency designation, APN Partners must possess deep AWS expertise and deliver solutions seamlessly on AWS

Capillary Technologies, has completed 10 years with AWS and has grown 50% year over year since then. The company was launched on AWS and over the years has invested in R&D by building AI-powered Omnichannel Customer Engagement and enterprise-ready eCommerce solutions for connected experience.

Talking about the AWS Retail Competency, Capillary Technologies’ Chief Technology Officer, Pravanjan Choudhury, said, “Capillary has driven growth through two primary strategies; continuous innovation and geographical expansion. AWS has been a pillar of support in achieving these results. Their constant updates and presence across the globe have helped our customers to implement our solutions globally easier and faster. Today, Capillary Technologies can stand out as an innovative company and offer innovative solutions to our clients such as integration of behavioral events on CRM, Offline clickstreams with data from offline retail stores, and more, and we are happy to share this success with AWS.”

“Many retailers are reinventing their operations and brand experience with new innovations in the cloud,” said Tom Litchford, Head of Retail Business Development, Amazon Web Services, Inc. “We are delighted to welcome Capillary Technologies to the AWS Retail Competency Program. Their solutions for retail operations, powered and vetted by AWS, can help our customers to accelerate their transformation, modernization, and customer engagement efforts.”

In their journey to become a SaaS leader, Capillary Technologies believes the vast capabilities of AWS have pushed them to explore deeper and come up with innovative products. Some of the key benefits that they have been able to power through AWS in the last few years are:

• Simplifies expansion to global markets: AWS’s global presence has allowed Capillary to offer our solutions in 30 countries in a short span of 10 years.
• Scaling while controlling costs: During peak shopping seasons or campaign periods, the traffic can be up to 50 times higher than usual fo retailers. Capillary has leveraged AWS solutions to stay resilient to this demand and be scale-ready for some of the largest retailers. The company has formed an AI/ML innovation lab dedicated to adding more AI capabilities to its products to enhance ROIs for their customers. Capillary has maintained an availability rate up to 99.97 percent despite a soaring customer base.
• Boosting Innovation: Capillary has also used Amazon Rekognition to drive an effective customer engagement solution powered by smart IoT sensors deployed in the stores. The technology captures information about visitors such as how much time they spend in a store or if they are a returning customer with products – VisitorMetrix and VisitorSense. Capillary is also experimenting with Amazon SageMaker to simplify the development and deployment of ML algorithms across its product portfolio. Leveraging the power of AWS has also reduced time to market for new products.

AWS is enabling scalable, flexible, and cost-effective solutions from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the AWS Competency Program to help customers identify Consulting and Technology APN Partners with deep industry experience and expertise.

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Amazon Web Services has expanded the capabilities of its Amazon SageMaker machine learning toolkit to address a number of challenges that enterprises confront when trying to operationalize machine learning, from model organization, training, and optimization to monitoring the performance of models in production.

Launched at the Amazon’s re:invent conference in 2017, SageMaker aims to make machine learning adoption simpler for customers by bringing together a hosted environment for Jupyter notebooks with built-in model management, automated spin up of training environments in Amazon S3, and HTTPS endpoints for hosting capabilities using EC2 instances.

As CEO Andy Jassy presents it, AWS—like rivals Google Cloud and Microsoft Azure—wants to become the leading, full-service environment for data scientists, data engineers, and non-specialist developers to run all of their machine learning workloads.

For AWS this means a triple-layered stack of services, starting with the basic building blocks used by experienced technical practitioners who want to be able to tweak every part of their modeling process, whether with TensorFlow, PyTorch, MXNet, or another machine learning framework. SageMaker promises to simplify key elements of the process, topped off with cognitive off-the-shelf services like Translate, Transcribe, image recognition, and voice recognition capabilities.

Introducing SageMaker Studio

Now Amazon is expanding this sandbox with what it calls SageMaker Studio, finally giving customers a fully integrated development environment (IDE) to store and collect all of the source code, notebooks, documentation, data sets, and project folders needed to run and manage machine learning models at enterprise scale, including collaboration capabilities.

Many of these capabilities can already be found within Microsoft’s Azure Machine Learning platform and Google Cloud’s AI Hub, while data science “workbench” offerings are also provided by the likes of Domino Data Lab and Dataiku.

SageMaker Experiments and Model Monitor

Among the new capabilities AWS has announced, let’s start with notebooks. AWS wants to simplify the provisioning of compute when spinning up a Jupyter notebook with one click, as well as automating the tricky process of transferring contents between notebooks.

Next on the list of announcements was SageMaker Experiments, a new feature which allows developers to view and manage all of the different iterations of their models. It does this by collecting key metrics like input parameters, configuration, and output results so that users can compare and contrast the performance of multiple models, both new models and older experiments.

Amazon has also added a native debugging tool, allowing users to debug and profile models during training, a process that has traditionally proved opaque. The debugger will flag when models are deviating from accuracy and performance indicators complete and offer remediation advice.

Lastly Amazon also announced SageMaker Model Monitor, which helps customers better detect “concept drift,” where the data being used by a model in production starts to deviate from that used to train the model. With SageMaker Model Monitor, AWS customers will be alerted when deviations in the data may be occurring based on a baseline level they configure by feeding a sample of their data to SageMaker. Model Monitor will then inspect data and prediction quality on a set schedule, even providing per-feature metrics to Amazon CloudWatch.

As Nick McQuire, vice president of enterprise research at CCS Insight said, “Customers are now doubling down on tackling data drift, black box AI, and requiring more tools to help them track model behavior in production. AWS has had to finally bring these areas into focus but in my view, they are a bit late to the party. Model explainability, bias detection, and performance monitoring have been glaring omissions in its strategy this year against Microsoft and Google in particular.”

SageMaker Autopilot for automated machine learning

Amazon also announced some changes to its automated machine learning, or AutoML, offering (not to be confused with Google Cloud’s own AutoML product), which automates the selection, training, and optimization of machine learning models within Sagemaker for classification and linear regression models.

Jassy said that customers have asked for greater visibility into these models, and has responded with SageMaker Autopilot.

The rough end-to-end workflow with SageMaker Autopilot is that customers provide the CSV file or a link to the S3 location of data they want to build the model on, and SageMaker will then train up to 50 different models on that data and give customers access to each of these as notebooks and present them in the form of a leaderboard within SageMaker Studio. The entire process, from data cleaning and pre-processing to algorithm choice to instance and cluster size selection, is handled automatically.

“So when you open the notebook the recipe of that model is there, from the algorithm to the parameters, so you can evolve it if you want,” Jassy said during his re:Invent keynote today.

In theory this allows companies to level up their models as they go with AWS, starting with classification and regression algorithms, but giving them the ability to track, measure, and customize these as they accumulate more data and grow the data science and engineering skills in their business.

SageMaker Studio is available immediately from the AWS US East (Ohio) region, while SageMaker Experiments and SageMaker Model Monitor are available immediately for all SageMaker customers.

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Today, Amazon  announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.

While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.

By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.

“This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.

For starters, Wood says developers can take advantage of Aurora, the company’s MySQL (and Postgres)-compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.

The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.

The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.

“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Asay wrote in a blog post announcing these new capabilities.

Asay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.

As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.

“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.

As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.

Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.

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With the demand for workers with advanced tech skills skyrocketing, many companies are putting more resources into recruiting, hiring and nurturing the right talent to remain in the global competition.

That means employees willing to put time into developing tech skills may have the upper-hand in landing some of the most in-demand roles.

But as technology continues to evolve at a rapid pace, it can be difficult to know exactly what skill sets are necessary to thrive in different fields. One way to find out is to understand people are spending their time learning, which is exactly what Udemy’s 2020 Workplace Learning Trends Report aims to do.

After analyzing data from more than 40 million users, the online learning platform found that the most popular tech skill people are learning is Python, a programming language. Overall, the report notes there’s a huge interest in learning about artificial intelligence (AI) and data science.

Shelley Osborne, vice president of learning at Udemy, told CNBC Make It that while tech courses tend to be more robust, some of the briefer introductory courses can be helpful to those who don’t even work in the tech field. “We sometimes see these topics trending with executive-level leaders who want to better understand their business’ approach using data science,” she said.

As it becomes easier for companies to parse out data, the need for workers to interpret those data sets is becoming more crucial. Jobs involving data science skills have been named as some of the most promising jobs in the U.S., according to LinkedIn.

“Organizations are becoming more data-driven, and that’s partly because they’re harnessing the power of AI, and there’s a need to analyze and process data across all kinds of roles,” Jennifer Juo, who leads the content marketing team at Udemy, told CNBC Make It.

Hiring managers are also having an especially hard time trying to fill roles that require skills in software development (e.g., Python, JavaScript), AI and cloud computing (e.g., Amazon Web Services, Google Cloud) and business intelligence (e.g., Microsoft Business Intelligence).

According to a 2019 report from iCIMS, a recruitment software provider, it took companies an average of 55 days to fill a tech role in 2016. In 2019, that number jumped to 66 days. These unfilled roles can cost about $680 in lost revenue per day per vacancy, according to iCIMS.

“We’re seeing a shift in skills development that requires us to think differently about how we approach talent,“said Osborne. She suggests companies encourage current employees to take classes and develop the essential skills that they lack.

Here are the top 10 most popular tech skills of 2020 — and where workers are leveling up the most:

1. Python

A programming language used in software development, infrastructure management and data analysis.

2. React (web)

A JavaScript library for building user interfaces.

3. Angular

A JavaScript-based open-source front-end web framework.

4. Machine learning

The scientific study of algorithms and statistical models.

5. Docker

An open-source platform used to create software packages called containers.

6. Django

A Python-based free and open-source web framework

7. CompTIA

A professional tech organization that has four IT certification series ranging from entry-level to expert.

8. Amazon AWS

A certification that validates cloud expertise.

9. Deep learning

A class of machine learning based on artificial neural networks.

10. React Native (mobile)

An open-source mobile application framework created by Facebook to develop apps for Android, iOS, Web and Universal Windows Platform.

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This report focuses on the Global Machine Learning and Location Data Applications Market landscape, future outlook, growth opportunities, and key and key contacts. The research objective is to present the development of the market in the US, Europe and Others. In addition, industry development trends and marketing channels are analyzed. The industry analysis have also been done to examine the impact of various factors and understand the overall attractiveness of the industry.

Machine Learning and Location Data Applications Market scenario, many emerging cities across the globe are struggling for traffic congestion, increased fuel consumption, and deteriorated air quality. Increasing fuel consumption has secured a long-term energy security of several countries, making them increasingly susceptible to global oil supply fluctuations. Bike-sharing services majorly minimize the long streaks of traffic, moreover, allowing countries to manage environmental challenges and energy dependency effectively.

The report also provides an analysis of the market competitive landscape and offers information on several companies including Microsoft Corporation, SAP SE,Sas Institute Inc., Amazon Web Services, Inc., Bigml, Inc., Google Inc., Fair Isaac Corporation, Baidu, Inc., Hewlett Packard Enterprise Development Lp, Intel Corporation

The report provides a comprehensive assessment of the market. We do this through in-depth qualitative insights, historical data and verifiable prospects for market size. The outlook presented in the report was derived using proven methodology and assumptions. Through this, the research report serves as a repository for analysis and information on all aspects of the market, including, but not limited to, local markets, technologies, types and applications.

The detailed qualitative and quantitative analysis of the market is also included in the report, with the information collected from market participants operating in the main areas of the value-added series of markets. A separate analysis of macro-and micro-economic aspects, rules and trends that affect the overall development of the market has also been included in the report.

Following are the List of Chapter Covers in the Machine Learning and Location Data Applications Market:

1. Machine Learning and Location Data Applications Market Overview
2. Global Economic Impact on Industry
3. Global Market Competition by Manufacturers
4. Global Market Analysis by Application
5. Marketing Strategy Analysis, Distributors/Traders
6. Market Effect Factors Analysis
7. Global Machine Learning and Location Data Applications Market Forecast

About Us

We at, QYReports, a leading market research report published accommodate more than 4,000 celebrated clients worldwide putting them at advantage in today’s competitive world with our understanding of research. Our list of customers includes prestigious Chinese companies, multinational companies, SME’s and private equity firms whom we have helped grow and sustain with our fact-based research. Our business study covers a market size of over 30 industries offering unfailing insights into the analysis to reimagine your business. We specialize in forecasts needed for investing in a new project, to revolutionize your business, to become more customer centric and improve the quality of output.

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The Pittsburgh Health Data Alliance (PHDA) is partnering with Amazon Web Services (AWS) to improve medical imaging, cancer diagnostics, precision medicine, voice-enabled technologies, and other areas of healthcare with machine learning. 

The AWS Machine Learning Research sponsorship will enable PHDA scientists from the University of Pittsburgh and Carnegie Mellon University (CMU) to accelerate research and product commercialization efforts across eight projects. 

Projects could have the potential to create an individualized risk score for every cancer patient, which will help providers better predict a patient’s response to treatment. Other projects will aim to use a patient’s verbal and visual cues to diagnose and treat mental health symptoms, and to reduce medical errors by mining all data in patient medical records. 

Researchers from the University of Pittsburgh are using AWS resources to improve diagnosis and treatment of abdominal aortic aneurysms, the 13th leading cause of death in western countries. Currently, clinicians can only use the measurements of an aneurysm’s diameter and growth rate to predict the risk of a rupture. 

“With the latest advances in machine learning, we are developing an algorithm that will provide clinicians with an objective, predictive tool to guide surgical interventions before symptoms appear, improving patient outcomes,” said David Vorp, PhD, associate dean for research at Pitt’s Swanson School of Engineering and the John A. Swanson Professor of Bioengineering.

Additionally, a team from CMU will leverage AWS support to develop algorithms and software tools to better understand the origin and evolution of tumor cells. The project will use machine learning to generate insights into how tumors predict, as well as how likely they are to change and grow in the future. 

“Data-driven, genomic methods guided by an understanding of cancers as evolutionary systems have relevance to numerous aspects of clinical cancer care,” said Russell Schwartz, PhD, professor of biological sciences and computational biology at CMU. 

“These include determining which precancerous lesions are likely to become cancers, which cancers have a good or bad prognosis, and which of those with bad prognoses might respond long-term to specific therapies.”

AWS resources will also support several precision medicine projects. One of these projects will focus on identifying genetic drivers of cancer within individual tumors, while another will aim to create a personalized risk score for breast cancer recurrence. 

Formed in 2015, the PHDA brings together the leading health sciences research at the University of Pittsburgh, computer science and machine learning capabilities of CMU, and the clinical care, patient data and commercialization expertise of the University of Pittsburgh Medical Center (UPMC). 

The PHDA uses the big data generated in healthcare to transform the way providers treat and prevent diseases, and to engage patients in their own care. With new machine learning toolsand advances in computing power, like those offered by Amazon, PHDA will be able to rapidly translate research insights into treatments and services that could significantly improve patient health. 

“We believe that machine learning can significantly accelerate the progress of medical research and help translate those advances into treatments and improved experiences for patients,” said Swami Sivasubramanian, vice president of machine learning for AWS. 

“We are excited to bring our machine learning services and cloud computing resources to support the high-impact work being done at the PHDA.”

By partnering with AWS, PHDA will continue its efforts to advance healthcare delivery and disease treatment. 

“This collaboration with AWS complements the unique strengths of the PHDA’s founders and will provide unparalleled resources to our researchers,” said Tal Heppenstall, president of UPMC Enterprises, which funds the PHDA and focuses on commercializing its breakthroughs. 

“By leveraging AWS machine learning and artificial intelligence services, we can help Pittsburgh become the premier hub of technology innovation in health care, drawing innovators from companies big and small to join us in this critical effort to revolutionize the delivery of health care.”

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