“Global Machine Learning Software Market Trending Research Report 2019 - Microsoft, Google, TensorFlow, Kount, Warwick Analytics - Top News Herald” plus 2 more
- Global Machine Learning Software Market Trending Research Report 2019 - Microsoft, Google, TensorFlow, Kount, Warwick Analytics - Top News Herald
- Google updates Shopping Insights for product search data and trends - Search Engine Land
- Google launches a new real-time data product for journalists - TechCrunch
Posted: 14 Aug 2019 12:36 AM PDT
Global Machine Learning Software Market Research Report 2019 Overview :
Global Machine Learning Software Market Growth (Status and Outlook) 2019-2024 study clears up all the minute bits of knowledge about the Machine Learning Software market. It presents a professional and in-depth study on the ongoing state of the market. The report uncovers key aspects of the tremendous features and parts of the market. It estimates the result of the present, historical, and future projections for which the research study evaluates the performance of the market in the world. The limitations that restrict market advancement are highlighted. The market ranking and its size are furthermore viewed on the basis of geographical segment analysis.
The research report estimates the Machine Learning Software market size and future growth potential of this market across different segments such as the manufacturer type, region, product type, and application. The report also examines the market on the basis of the products' details which includes, product price, production volume, supply chain dynamics, revenue generated as well as the policies influencing production are all included in this market study. The market is projected to be the fastest-growing market during the forecast period from 2019 to 2024. It comes with several charts, graphs, and graphics to make it an interesting read.
DOWNLOAD FREE SAMPLE REPORT: https://www.mrinsights.biz/report-detail/177138/request-sample
The market is evaluated based on its regional penetration, explaining the performance of the market in each regional market covering regions such as
In addition, the market dynamics between every application and the overall industrial chain is studied with respect to each regional market in order to provide a better understanding of how the market works.
Focusing Machine Learning Software market top manufacturers in the market, with production, price, revenue, and market share for each manufacturer, covering top players: Microsoft, Google, TensorFlow, Kount, Warwick Analytics, Valohai, Torch, Apache SINGA, AWS, BigML, Figure Eight, Floyd Labs
Global Machine Learning Software Market Report Contains The Following Points:
The overall Machine Learning Software market study offers a thorough examination of the perceptible driving parts that are identified reliant on the end customer demands, variable market changes, restricting segments. Through analysis of key raw materials, key suppliers of raw materials, cost of raw materials & labor cost, and price trend of key raw materials are provided in this report.
There are 12 Chapters to deeply display the global Machine Learning Software market.
Chapter 1: Scope of the Report
Chapter 2: Executive Summary
Chapter 3: Global Machine Learning Software by Manufacturers
Chapter 4: Machine Learning Software by Regions
Chapter 5, 6, 7, 8 and 9: Americas,APAC,Europe,Middle East & Africa,Market Drivers, Challenges and Trends
Chapter 10 and 11: Global Machine Learning Software Market Forecast, Key Players Analysis
Chapter 12 : Research Findings and Conclusion.
Customization of the Report:This report can be customized to meet the client's requirements. Please connect with our sales team (email@example.com), who will ensure that you get a report that suits your needs.
Tom is a staff writer at Top News Herald. He covers technology news and handles all the technical stuff for Top News Herald. Tom originally hails from the UK and went to Foyle College.
Posted: 16 Oct 2018 12:00 AM PDT
Shopping Insights tells you what products and brands are popular, trending up or down, and how they vary by region in the U.S.
What's new? There are several new features in the Shopping Insights tool.
Why it matters to marketers. Marketers can drill into Google Insights data by region to understand what products are hot and where. The popularity comparison means "retailers can track the relative popularity of various brands within a category and adjust their strategies accordingly," Aman Govil, project lead for Shopping Insights, wrote.
Marketers and retailers can also look at weekly trends during a given time period — say last year's holiday season — to see what brands were trending and how that differed from other time periods. "By tracking this data weekly, marketers can optimize their tactics in season, and respond to changing consumer preferences within different categories," said Govil.
You can create up to 10 weekly and monthly email reports for the products and categories you want to follow.
Posted: 25 Mar 2019 12:00 AM PDT
One of those products was News Consumer Insights, which has been used by publishers like BuzzFeed, Business Insider and Conde Nast. It takes data already collected through Google Analytics and makes it more useful for publishers, particularly when it comes to understanding different audience segments and whether they're likely to become paying subscribers.
"It's turning raw data into business intelligence and actionable insights," said Amy Adams Harding, Google's head of analytics and revenue optimization and head of publisher development.
Now Google is building on the NCI product with a new tool called Real-time Content Insights.
As the name implies, RCI is focused on telling publishers what's happening on their site at this moment, and on helping them identify trending news stories that could attract more readers. The initial NCI data is more useful for the publisher's business or audience development teams, Harding said. RCI is designed "to help the editorial side of our partners understand the dynamics of content on their site — what's trending, what's falling off, what's getting traction."
Google is hardly the first company to offer real-time data to news publishers, but Harding said this "off-the-shelf, click-to-play" product could be particularly useful for smaller newsrooms that don't have a lot of resources and aren't particularly data-savvy.
"Local is a huge pillar of the Google News Initiative," Harding said. "What can we do to help develop tools where we can be support mechanisms for our partners as they try to stay sustainable during this transition … not only to digital, but one more transition over to this diversified revenue stream? That's something that many of our publishers are not resourced well enough to take on on their own."
RCI presents the data in the form of an image-heavy dashboard showing how many readers are looking at a story currently, and how many views the story's gotten in the past 30 minutes. You also can see how well the site is doing today, compared to a normal day's traffic, and break down your traffic by geography and referral sources.
The dashboard also shows the topics that are currently trending on Google and Twitter. Of course, not all of those topics will be right for your publication, but Harding said it can help editors and writers identify the gaps in their coverage, based on, "What are people curious about?" (on Google) and "What are people talking about?" (on Twitter).
At first glance, RCI doesn't seem to tie directly into the bigger goals of helping publishers building sustainable, diversified business models. However, Harding noted that it can be used in conjunction with the existing NCI product, which helps them identify their most valuable audiences.
"Where the publisher would see value is, 'Okay, we know that users coming from direct referral traffic are more valuable, [and] this article is driving is driving viewership from those types of readers,' " she said.
Harding added that Google is making the RCI source code available on GitHub, so that more-sophisticated publishers can customize it to create their own data visualizations.
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