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Wayne Blodwell, founder and chief exec of The Programmatic Advisory & The Programmatic University, battles through the buzzwords to explain why custom machine learning can help you unlock differentiation and regain a competitive edge.

Back in the day, simply having programmatic on plan was enough to give you a competitive advantage and no one asked any questions. But as programmatic has grown, and matured (84.5% of US digital display spend is due to be bought programmatically in 2020, the UK is on track for 92.5%), whats next to gain advantage in an increasingly competitive landscape?

Machine Learning

[noun]

The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

(Oxford Dictionary, 2020)

Youve probably head of machine learning as it exists in many Demand Side Platforms in the form of automated bidding. Automated bidding functionality does not require a manual CPM bid input nor any further bid adjustments instead, bids are automated and adjusted based on machine learning. Automated bids work from goal inputs, eg achieve a CPA of x or simply maximise conversions, and these inputs steer the machine learning to prioritise certain needs within the campaign. This tool is immensely helpful in taking the guesswork out of bids and the need for continual bid intervention.

These are what would be considered off-the-shelf algorithms, as all buyers within the DSP have access to the same tool. There is a heavy reliance on this automation for buying, with many even forgoing traditional optimisations for fear of disrupting the learnings and holding it back but how do we know this approach is truly maximising our results?

Well, we dont. What we do know is that this machine learning will be reasonably generic to suit the broad range of buyers that are activating in the platforms. And more often than not, the functionality is limited to a single success metric, provided with little context, which can isolate campaign KPIs away from their true overarching business objectives.

Custom machine learning

Instead of using out of the box solutions, possibly the same as your direct competitors, custom machine learning is the next logical step to unlock differentiation and regain an edge. Custom machine learning is simply machine learning that is tailored towards specific needs and events.

Off-the-self algorithms are owned by the DSPs; however, custom machine learning is owned by the buyer. The opportunity for application is growing, with leading DSPs opening their APIs and consoles to allow for custom logic to be built on top of existing infrastructure. Third party machine learning partners are also available, such as Scibids, MIQ & 59A, which will develop custom logic and add a layer onto the DSPs to act as a virtual trader, building out granular strategies and approaches.

With this ownership and customisation, buyers can factor in custom metrics such as viewability measurement and feed in their first party data to align their buying and success metrics with specific business goals.

This level of automation not only provides a competitive edge in terms of correctly valuing inventory and prioritisation, but the transparency of the process allows trust to rightfully be placed with automation.

Custom considerations

For custom machine learning to be effective, there are a handful of fundamental requirements which will help determine whether this approach is relevant for your campaigns. Its important to have conversations surrounding minimum event thresholds and campaign size with providers, to understand how much value you stand to gain from this path.

Furthermore, a custom approach will not fix a poor campaign. Custom machine learning is intended to take a well-structured and well-managed campaign and maximise its potential. Data needs to be inline for it to be adequately ingested and for real insight and benefit to be gained. Custom machine learning cannot simply be left to fend for itself; it may lighten the regular day to day load of a trader, but it needs to be maintained and closely monitored for maximum impact.

While custom machine learning brings numerous benefits to the table transparency, flexibility, goal alignment its not without upkeep and workflow disruption. Levels of operational commitment may differ depending on the vendors selected to facilitate this customisation and their functionality, but generally buyers must be willing to adapt to maximise the potential that custom machine learning holds.

Find out more on machine learning in a session The Programmatic University are hosting alongside Scibids on The Future Of Campaign Optimisation on 17 September. Sign up here.

Original post:
What is 'custom machine learning' and why is it important for programmatic optimisation? - The Drum

There is a need for more comprehensive prediction models for advanced age-related macular degeneration (AMD) that consider a wider range of risk factors. Researchers tested a prediction model and applied a machine learning algorithm that autonomously identified the most significant clinical, genetic, and lifestyle risk factors for AMD.

The training set, obtained from the Rotterdam Study I (RS-I), included 3,838 patients aged 55 years or older. Median follow-up was 10.8 years, and there were 108 incident cases of advanced AMD. The test set, obtained from the ALIENOR study, included 362 participants aged 73 years or older. Median follow-up was 6.5 years, and there were 33 incident cases of advanced AMD.

The following factors were retained by the prediction model:

In the RS-I group, the cross-validated area under the receiver operating characteristic curve (AUC) estimation was: at five years, 0.92; at 10 years, 0.92; and at 15 years, 0.91. In the ALIENOR cohort, at five years, the AUC was 0.92. The researchers noted that when it came to calibration, the prediction model underestimated the cumulative incidence of advanced AMD in high-risk groups; this was particularly evident in the ALIENOR cohort.

They concluded that their prediction model achieved high discrimination abilities and was a step toward precision medicine for patients with AMD.

Read more here:
The Use of Machine Learning to Forecast Progression to Advanced AMD - DocWire News

A new report added by Big Market Research claims that the globalMachine Learning in Automobile marketgrowth is set to reach newer heights during the forecast period,20202025.

The report is an exhaustive analysis of this market across the world. It offers an overview of the market including its definition, applications, key drivers, key market players, key segments, and manufacturing technology. In addition, the study presents statistical data on the status of the market and hence is a valuable source of guidance for companies and individuals interested in the industry. Additionally, detailed insights on the company profile, product specifications, capacity, production value, and market shares for key vendors are presented in the report.

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By Key Players:Allerin, Intellias Ltd, NVIDIA Corporation, Xevo, Kopernikus Automotive, Blippar, Alphabet Inc, Intel, IBM, Microsoft

A proper understanding of the Machine Learning in Automobile Market dynamics and their inter-relations helps in gauging the performance of the industry. The growth and revenue patterns can be revised and new strategic decisions taken by companies to avoid obstacles and roadblocks. It could also help in changing the patterns using which the market will generate revenues. The analysis includes an assessment of the production chain, supply chain, end user preferences, associated industries, proper availability of resources, and other indexes to help boost revenues.

Regions & Top Countries Data Covered in this Report are:Asia-Pacific (China, Southeast Asia, India, Japan, Korea, Western Asia), Europe (Germany, UK, France, Italy, Russia, Spain, Netherlands, Turkey, Switzerland), North America (United States, Canada, Mexico), Middle East & Africa (GCC, North Africa, South Africa) , South America (Brazil, Argentina, Columbia, Chile, Peru).

The Machine Learning in Automobile Market is gaining pace and businesses have started understanding the benefits of analytics in the present day highly dynamic business environment. The market has witnessed several important developments over the past few years, with mounting volumes of business data and the shift from traditional data analysis platforms to self-service business analytics being some of the most prominent ones.

With the help of in-depth research offered in the report, readers can effortlessly get detailed analysis of the key dynamics of the Machine Learning in Automobile market. The report also offers competitive landscape by providing detailed information on trends in competition, prominent players, and nature of competition. Additionally, it offers detailed analysis of the key segments of the market that helps in understanding the global trends in the Machine Learning in Automobile Market. An overview of each market segment such as type, application, and region are presented in the report. Additionally, the report presents drivers, limitations, and opportunities for the Machine Learning in Automobile industry, followed by industry news and policies.

Machine Learning in Automobile Market By Type:

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

Machine Learning in Automobile Market By Application:

AI Cloud ServicesAutomotive InsuranceCar ManufacturingDriver MonitoringOthers

Reasons for Buying This Report:

The report provides insights on the following pointers:

North America, Europe, Asia Pacific, Middle East & Africa, Latin America market size (sales, revenue and growth rate) of Machine Learning in Automobile industry.

Global major manufacturers operating situation (sales, revenue, growth rate and gross margin) of Machine Learning in Automobile industry.

Global major countries (United States, Canada, Germany, France, UK, Italy, Russia, Spain, Netherlands, Switzerland, Belgium, China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Vietnam, Turkey, Saudi Arabia, United Arab Emirates, South Africa, Israel, Egypt, Nigeria, Brazil, Mexico, Argentina, Colombia, Chile, Peru) market size (sales, revenue and growth rate) of Machine Learning in Automobile industry.

Different types and applications of Machine Learning in Automobile industry, market share of each type and application by revenue.

Global market size (sales, revenue) forecast by regions and countries from 2020 to 2026 of Machine Learning in Automobile industry.

Upstream raw materials and manufacturing equipment, downstream major consumers, industry chain analysis of Machine Learning in Automobile industry.

Key drivers influencing market growth, opportunities, the challenges and the risks analysis of Machine Learning in Automobile industry.

New Project Investment Feasibility Analysis of Machine Learning in Automobile industry.

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Our analysis involves the study of the market taking into consideration the impact of the COVID-19 pandemic. Please get in touch with us to get your hands on an exhaustive coverage of the impact of the current situation on the market. Our expert team of analysts will provide as per report customized to your requirement.

Table of Content:

Industry Overview of Machine Learning in Automobile

Major Manufacturers Analysis of Machine Learning in Automobile

Global Price, Sales and Revenue Analysis of Machine Learning in Automobile by Regions, Manufacturers, Types and Applications

North America Sales and Revenue Analysis of Machine Learning in Automobile by Countries

Europe Sales and Revenue Analysis of Machine Learning in Automobile by Countries

Asia Pacific Sales and Revenue Analysis of Machine Learning in Automobile by Countries

Latin America Sales and Revenue Analysis of Machine Learning in Automobile by Countries

Middle East & Africa Sales and Revenue Analysis of Machine Learning in Automobile by Countries

Global Market Forecast of Machine Learning in Automobile by Regions, Countries, Manufacturers, Types and Applications

Industry Chain Analysis of Machine Learning in Automobile

New Project Investment Feasibility Analysis of Machine Learning in Automobile

Conclusion of the Global Machine Learning in Automobile Industry Market Professional Survey 2020

Appendix

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Big Market Research has a range of research reports from various publishers across the world. Our database of reports of various market categories and sub-categories would help to find the exact report you may be looking for.We are instrumental in providing quantitative and qualitative insights on your area of interest by bringing reports from various publishers at one place to save your time and money. A lot of organizations across the world are gaining profits and great benefits from information gained through reports sourced by us.

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Read more:
Global Machine Learning in Automobile Market: Development History, Current Analysis and Estimated Forecast to 2024 - The Market Correspondent

TEMPE, Ariz., Sept. 15, 2020 /PRNewswire/ --Dashboard AI,a technology innovator in the foodservice and hospitality industry, today announces its vision for a single system of record for the industry.The company leverages advances in machine learning, computer vision, and IoT toreveal insights that improve safety, efficiency, compliance, and training thatcreate a single system of record.

"The current fragmented software landscape creates an opportune time to implement a unified platform that is the system of record for critical data around business operations, safety, inventory, staffing, and productivity." said Brian Pierce, Dashboard AI cofounder and chairman."Our Dashboard AI's stewardship of this data creates aplatform of powerthat can be used in other business software and services categories that need access to this underlying data."

Harnessing the recent advancements in facial recognition, computer vision, sensors, and IoT, the company is building a suite of products that automate existing operational processes that are typically performed manually.Dashboard AI's machine learning algorithms are built using the company's proprietary data and training libraries designed specifically for foodservice operations.Its machine learning capabilities include first-of-its-kind inventory and usage monitoring algorithms to naturally identify food and beverage brands within the foodservice and hospitality environment, to learn, monitor, track, and measure brand usage. These applications use the company's technology to improve accuracy, efficacy, and efficiency.

"The foodservice sector has been riveted by the global pandemic and we need to leverage technology to rethink how we can optimize our operational processes and build a foundation for the future" said Brian Pierce."At Dashboard AI,we've been working on this opportunity for over a year before the pandemic hit and as a result of its impact on the industry, we've accelerated our efforts to develop and refine our solutions."

AboutDashboard AIDashboard AI is an "All-in-One" platform for foodservice.The company's mission is toincrease efficiency, safety, compliance, and training for the foodservice industry.Leveraging advances in machine learning, computer vision, and IoT, Dashboard AI creates a single system of record for the industry.The company's suite of solutions includes machine learning-powered inventory monitoring and ordering, safety and security, training and education, and labor productivity measurement all in an integrated, open, real-time platform.

Founded in 2019 by serial entrepreneurs in the foodservice and technology industry, Brian Pierce and Kelly Egan, Dashboard AI is backed by Resiliency Ventures as well as notable angel investors from the foodservice and technology industry.The company is expanding its current round of financing as a result of increased interest in its technology and solutions.

Media Contact:[emailprotected]

Dashboard AI60 Rio Salado ParkwayTempe, AZ 85281

SOURCE Dashboard AI, Inc.

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Dashboard AI Announces Its Technology Vision for the Foodservice and Hospitality Industry - PRNewswire