Conference to showcase the practical, real-life enterprise use of data science, machine learning, AI, IoT, and open data in cities and mobility industries

OReilly, the premier source for insight-driven learning on technology and business, and Formulatedby today announced a new conference focused on how machine learning is transforming the future of urban communities and mobility industries around the world. The inaugural Smart Cities & Mobility Ecosystems (SCME) conference will take place in Phoenix, AZ from April 15-16, 2020 followed by a second event in Miami, FL from June 3-4, 2020.

Rapid technological advancements are challenging cities and the mobility industry with new business models, methodologies in development and manufacturing, unprecedented levels of automation, and the need for new infrastructure. From predictive analytics to policy, the Smart Cities & Mobility Ecosystems conference examines the role of governments, enterprises, and individuals in driving positive change as communities become increasingly connected.

"How we plan, build, and improve our cities has fundamentally changed, driven by powerful new technologies that can make life better for all the constituencies cities hope to serve," said Roger Magoulas, VP of Radar at OReilly and chair of the Smart Cities & Mobility Ecosystems conference. "This conference helps take the pulse of what we expect to change and what is possible for communities and mobility over the coming years."

The focused event brings together enterprise practitioners, technical experts, and executives to discuss how data, artificial intelligence (AI), machine learning, and cutting-edge technologies impact the future of our communities. Attendees can also workshop real-world applications of deep learning, sensor fusion, data processing and AI, automotive camera technology and computer vision algorithms, and reinforcement learning.

"The conversation around AI and ML has moved mainstream in applications like Smart Cities and Mobility Ecosystems," said Anna Anisin, founder and CEO at Formulatedby. "We're excited to collaborate with OReilly to connect our audience of ML practitioners and executives with the policymakers and stakeholders who will participate in taking this technology to the next level to improve lives at scale."

Key speakers at the Smart Cities & Mobility Ecosystems conference in Phoenix include:

Key speakers at the Smart Cities & Mobility Ecosystems conference in Miami include:

Registration for the upcoming Smart Cities and Mobility Ecosystems conference is now open for Phoenix and Miami. A limited number of media passes are also available for qualified journalists and analysts. Please contact info@formulated.by for media or analyst registration. Follow #SCME on Twitter for the latest news and updates.

About Formulatedby

Formulatedby is a marketing agency specializing in building data science, machine learning and AI communities. Female-owned and formulated in Miami, its best known for the Data Science Salon, a vertically focused conference series around AI and ML, and for working throughout the technology landscape in B2B enterprise marketing and experiential marketing. For more information, visit formulated.by.

About OReilly

For 40 years, OReilly has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise at OReilly conferences and through the companys SaaS-based training and learning solution, OReilly online learning. OReilly delivers highly topical and comprehensive technology and business learning solutions to millions of users across enterprise, consumer, and university channels. For more information, visit http://www.oreilly.com.

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Contacts

Allison Stokesfama PR for OReilly617-986-5010OReilly@famapr.com

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OReilly and Formulatedby Unveil the Smart Cities & Mobility Ecosystems Conference - Yahoo Finance

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Theres no shortage of resources and tools for developing machine learning algorithms. But when it comes to putting those algorithms into production for inference, outside of AWSs popular SageMaker, theres not a lot to choose from. Now a startup called Cortex Labs is looking to seize the opportunity with an open source tool designed to take the mystery and hassle out of productionalizing machine learning models.

Infrastructure is almost an afterthought in data science today, according to Cortex Labs co-founder and CEO Omer Spillinger. A ton of energy is going into choosing how to attack problems with data why, use machine learning of course! But when it comes to actually deploying those machine learning models into the real world, its relatively quiet.

We realized there are two really different worlds to machine learning engineering, Spillinger says. Theres the theoretical data science side, where people talk about neural networks and hidden layers and back propagation and PyTorch and Tensorflow. And then you have the actual system side of things, which is Kubernetes and Docker and Nvidia and running on GPUs and dealing with S3 and different AWS services.

Both sides of the data science coin are important to building useful systems, Spillinger says, but its the development side that gets most of the glory. AWS has captured a good chunk of the market with SageMaker, which the company launched in 2017 and which has been adopted by tens of thousands of customers. But aside from just a handful of vendors working in the area, such as Algorithmia, the general data-building public has been forced to go it alone when it comes to inference.

A few years removed from UC Berkeleys computer science program and eager to move on from their tech jobs, Spillinger and his co-founders were itching to build something good. So when it came to deciding what to do, Spillinger and his co-founders decided to stick with what they knew, which was working with systems.

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We thought that we could try and tackle everything, he says. We realized were probably never going to be that good at the data science side, but we know a good amount about the infrastructure side, so we can help people who actually know how to build models get them into their stack much faster.

Cortex Labs software begins where the development cycle leaves off. Once a model has been created and trained on the latest data, then Cortex Labs steps in to handle the deployment into customers AWS accounts using its Kubernetes engine (AWS is the only supported cloud at this time; on-prem inference clusters are not supported).

Our starting point is a trained model, Spillinger says. You point us at a model, and we basically convert it into a Web API. We handle all the productionalization challenges around it.

That could be shifting inference workloads from CPUs to GPUs in the AWS cloud, or vice versa. It could be we automatically spinning up more AWS servers under the hood when calls to the ML inference service are high, and spinning down the servers when that demand starts to drop. On top of its build-in AWS cost-optimization capabilities, the Cortex Labs software logs and monitors all activities, which is a requirement in todays security- and regulatory-conscious climate.

Cortex Labs is a tool for scaling real-time inference, Spillinger says. Its all about scaling the infrastructure under the hood.

Cortex Labs delivers a command line interface (CLI) for managing deployments of machine learning models on AWS

We dont help at all with the data science, Spillinger says. We expect our audience to be a lot better than us at understanding the algorithms and understanding how to build interesting models and understanding how they affect and impact their products. But we dont expect them to understand Kubernetes or Docker or Nvidia drivers or any of that. Thats what we view as our job.

The software works with a range of frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost. The company is open to supporting more. Theres going to be lots of frameworks that data scientists will use, so we try to support as many of them as we can, Spillinger says.

Cortex Labs software knows how to take advantage of EC2 spot instances, and integrates with AWS services like Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Lambda, and Fargate. The Kubernetes management alone may be worth the price of admission.

You can think about it as a Kubernetes thats been massaged for the data science use case, Spillinger says. Theres some similarities to Kubernetes in the usage. But its a much higher level of abstraction because were able to make a lot of assumptions about the use case.

Theres a lack of publicly available tools for productionalizing machine learning models, but thats not to say that they dont exist. The tech giants, in particular, have been building their own platforms for doing just this. Airbnb, for instance, has its BigHead offering, while Uber has talked about its system, called Michelangelo.

But the rest of the industry doesnt have these machine learning infrastructure teams, so we decided wed basically try to be that team for everybody else, Spillinger says.

Cortex Labs software is distributed under an open source license and is available for download from its GitHub Web page. Making the software open source is critical, Spillinger says, because of the need for standards in this area. There are proprietary offerings in this arena, but they dont have a chance of becoming the standard, whereas Cortex Labs does.

We think that if its not open source, its going to be a lot more difficult for it to become a standard way of doing things, Spillinger says.

Cortex Labs isnt the only company talking about the need for standards in the machine learning lifecycle. Last month, Cloudera announced its intention to push for standards in machine learning operations, or MLOps. Anaconda, which develops a data science platform, also is backing

Eventually, the Oakland, California-based company plans to develop a managed service offering based on its software, Spillinger says. But for now, the company is eager to get the tool into the hands of as many data scientists and machine learning engineers as it can.

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Despite being one of the richest countries in the world, in Qatar, digital maps are lagging behind. While the country is adding new roads and constantly improving old ones in preparation for the 2022 FIFA World Cup, Qatar isn't a high priority for the companies that actually build out maps, like Google.

"While visiting Qatar, weve had experiences where our Uber driver cant figure out how to get where hes going, because the map is so off," Sam Madden, a professor at MIT's Department of Electrical Engineering and Computer Science, said in a prepared statement. "If navigation apps dont have the right information, for things such as lane merging, this could be frustrating or worse."

Madden's solution? Quit waiting around for Google and feed machine learning models a whole buffet of satellite images. It's faster, cheaper, and way easier to obtain satellite images than it is for a tech company to drive around grabbing street-view photos. The only problem: Roads can be occluded by buildings, trees, or even street signs.

So Madden, along with a team composed of computer scientists from MIT and the Qatar Computing Research Institute, came up with RoadTagger, a new piece of software that can use neural networks to automatically predict what roads look like behind obstructions. It's able to guess how many lanes a given road has and whether it's a highway or residential road.

RoadTagger uses a combination of two kinds of neural nets: a convolutional neural network (CNN), which is mostly used in image processing, and a graph neural network (GNN), which helps to model relationships and is useful with social networks. This system is what the researchers call "end-to-end," meaning it's only fed raw data and there's no human intervention.

First, raw satellite images of the roads in question are input to the convolutional neural network. Then, the graph neural network divides up the roadway into 20-meter sections called "tiles." The CNN pulls out relevant road features from each tile and then shares that data with the other nearby tiles. That way, information about the road is sent to each tile. If one of these is covered up by an obstruction, then, RoadTagger can look to the other tiles to predict what's included in the one that's obfuscated.

Parts of the roadway may only have two lanes in a given tile. While a human can easily tell that a four-lane road, shrouded by trees, may be blocked from view, a computer normally couldn't make such an assumption. RoadTagger creates a more human-like intuition in a machine learning model, the research team says.

"Humans can use information from adjacent tiles to guess the number of lanes in the occluded tiles, but networks cant do that," Madden said. "Our approach tries to mimic the natural behavior of humans ... to make better predictions."

The results are impressive. In testing out RoadTagger on occluded roads in 20 U.S. cities, the model correctly counted the number of lanes 77 percent of the time and inferred the correct road types 93 percent of the time. In the future, the team hopes to include other new features, like the ability to identify parking spots and bike lanes.

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How Machine Learning Will Lead to Better Maps - Popular Mechanics

A risk prediction model consisting of 8 independent diagnostic variables, including age, sex, waist circumference, stomach pain frequency, cigarette smoking, duration of heartburn and acidic taste, and current history of antireflux medication use, can provide potential insight into a patients risk for Barretts esophagus before endoscopy, according to a study in published Lancet Digital Health.

The study assessed data from 2 prior case-control studies: BEST2 (ISRCTN Registry identifier: 12730505) and BOOST (ISRCTN Registry identifier: 58235785). Questionnaire data were assessed from the BEST2 study, which included responses from 1299 patients, of whom 67.7% (n=880) had Barretts esophagus, which was defined as endoscopically visible columnar-lined oesophagus (Prague classification C1 or M3), with histopathological evidence of intestinal metaplasia on at least one biopsy sample. An algorithm was used to randomly divide (6:4) the cohort into a training data set (n=776) and a testing data set (n=523). A total of 398 patients from the BOOST study, including 198 with Barretts esophagus, were included in this analysis as an external validation cohort. Another 200 control individuals were also included from the BOOST study.

Researchers used a univariate approach called information gain, as well as a correlation-based feature selection. These 2 machine learning filter techniques were used to identify independent diagnostic features of Barretts esophagus. Multiple classification tools were assessed to create a multivariable risk prediction model. The BEST2 testing data set was used for internal validation of the model, whereas the BOOST external validation data set was used for external validation.

In the BEST2 study, the investigators identified a total of 40 diagnostic features of Barretts esophagus. Although 19 of these features added information gain, only 8 features demonstrated independent diagnostic value after correlation-based feature selection. The 8 diagnostic features associated with an increased risk for Barretts esophagus were age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and receiving antireflux medication.

The upper estimate of the predictive value of the model, which included these 8 features, had an area under the curve (AUC) of 0.87 (95% CI, 0.84-0.90; sensitivity set, 90%; specificity, 68%). In addition, the testing data set demonstrated an AUC of 0.86 (95% CI, 0.83-0.89; sensitivity set, 90%; specificity, 65%), and the external validation data set featured an AUC of 0.81 (95% CI, 0.74-0.84; sensitivity set, 90%; specificity, 58%).

The study was limited by the fact that it collected data solely from at-risk patients, which enriched the overall cohorts for patients with Barrets esophagus.

The researchers concluded that the risk prediction panels generated from this study would be easy to implement into medical practice, allowing patients to enter their symptoms into a smartphone app and receive an immediate risk factor analysis. After receiving results, the authors suggest, these data could then be uploaded to a central database (eg, in the cloud) that would be updated after that person sees their medical professional.

Reference

Rosenfeld A, Graham DG, Jevons S, et al; BEST2 study group. Development and validation of a risk prediction model to diagnose Barretts oesophagus (MARK-BE): a case-control machine learning approach [published online December 5, 2019]. Lancet Digit Health. doi:10.1016/S2589-7500(19)30216-X.

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Machine Learning Could Aid Diagnosis of Barrett's Esophagus, Avoid Invasive Testing - Medical Bag

RealityEngines.AI, a San Francisco-based AI and machine learning research startup, is coming out of stealth and launching the worlds first completely autonomous cloud AI service to address common enterprise use-cases. The cloud AI service automatically creates, deploys and maintains deep learning systems in production. The engine handles setting up data pipelines, scheduled retraining of models from new data, provisioning high availability online model serving from raw data using a feature store service, and providing explanations for the models predictions. The service is the first of its kind and helps organizations with little to no machine learning expertise plug and play state-of-the-art AI into their existing applications and business processes effortlessly.

RealityEngines.AI tackles common enterprise use-cases including user churn predictions, fraud detection, sales lead forecasting, security threat detection, and cloud spend optimization. Customers simply have to pick a use-case that is applicable to them and then point their data to RealityEngines. The service will then process the data, train a model, deploy it in production and maintain the system for them. Behind the scenes, RealityEngines.AI searches several thousand neural net architectures to find the best neural net model based on the use-case and dataset. The underlying neural net model trained by RealityEngines.AI surpasses custom models that are hand-tuned by experts and take months to put into production.

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Another common barrier to organizations deploying deep learning systems in production is the lack of large volumes of good data. RealityEngines.AI has built on existing research and invented a new technique to effectively handle smaller, incomplete and noisy datasets. The company has developed a technique based on generative models (GANS). This technique creates synthetic data that augments the original dataset. and then trains a deep learning model on the combined dataset. These models yield up to 15% improvement in accuracy compared to models that are trained without using this augmentation technique.

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RealityEngines.AI has created a fun visual demo to showcase their technology. The demo generates versions of celebrities expressing different emotions such as happiness, sadness, anger, disgust, and surprise, transforming their age and gender and mouthing famous quotes. The technology works on virtually any photo with a face and can be tested by uploading a selfie. Behind the scenes, a generative model will create multiple versions of the selfie in near real-time. The same technology is re-purposed to create different samples of the original data and a synthetic dataset. This dataset can be used to train robust models even when there is insufficient raw data.

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The latest report published by ReportsnReports has titled Global Machine Learning in Retail Market by Application, By Region and Key Participants Market Status and Outlook, Competition landscape, share, growth rate, future trends, Opportunities and challenges.

This report presents the worldwide Machine Learning in Retail Market size (value, production and consumption), splits the breakdown (data status 2015-2019 and forecast to 2026), by manufacturers, region, type and application. This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porters Five Forces Analysis.

This report focuses on the global Machine Learning in Retail status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Machine Learning in Retail development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.

Download Free PDF Sample Brochure of report Machine Learning in Retail spread across 91 pages and supported with tables and figures is now available @ http://www.reportsnreports.com/contactme=2882353

Top Manufacturers Analysis:

- IBM- Microsoft- Amazon Web Services- Oracle- SAP- Intel- NVIDIA- Google- Sentient Technologies- Salesforce- ViSenze

Market segment by Regions/Countries, this report covers- North America- Europe- China- Japan- Southeast Asia- India- Central & South America

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Market segment by Type, the product can be split into- Cloud Based- On-Premises

Market segment by Application, split into- Online- Offline

Major Points from Table of Contents

List of Tables:

Table 1. Machine Learning in Retail Key Market SegmentsTable 2. Key Players Covered: Ranking by Machine Learning in Retail RevenueTable 3. Ranking of Global Top Machine Learning in Retail Manufacturers by Revenue (US$ Million) in 2019Table 4. Global Machine Learning in Retail Market Size Growth Rate by Type (US$ Million): 2020 VS 2026Table 5. Key Players of Cloud BasedTable 6. Key Players of On-PremisesTable 7. Global Machine Learning in Retail Market Size Growth by Application (US$ Million): 2020 VS 2026Table 8. Global Machine Learning in Retail Market Size by Regions (US$ Million): 2020 VS 2026Table 9. Global Machine Learning in Retail Market Size by Regions (2015-2020) (US$ Million)Table 10. Global Machine Learning in Retail Market Share by Regions (2015-2020)Table 11. Global Machine Learning in Retail Forecasted Market Size by Regions (2021-2026) (US$ Million)Table 12. Global Machine Learning in Retail Market Share by Regions (2021-2026)Table 13. Market Top TrendsTable 14. Key Drivers: Impact AnalysisTable 15. Key ChallengesTable 16. Machine Learning in Retail Market Growth StrategyTable 17. Main Points Interviewed from Key Machine Learning in Retail PlayersTable 18. Global Machine Learning in Retail Revenue by Players (2015-2020) (Million US$)Table 19. Global Machine Learning in Retail Market Share by Players (2015-2020)Table 20. Global Top Machine Learning in Retail Players by Company Type (Tier 1, Tier 2 and Tier 3) (based on the Revenue in Machine Learning in Retail as of 2019)Table 21. Global Machine Learning in Retail by Players Market Concentration Ratio (CR5 and HHI)Table 22. Key Players Headquarters and Area ServedTable 23. Key Players Machine Learning in Retail Product Solution and ServiceTable 24. Date of Enter into Machine Learning in Retail MarketTable 25. Mergers & Acquisitions, Expansion PlansTable 26. Global Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 27. Global Machine Learning in Retail Market Size Share by Type (2015-2020)Table 28. Global Machine Learning in Retail Revenue Market Share by Type (2021-2026)Table 29. Global Machine Learning in Retail Market Size Share by Application (2015-2020)Table 30. Global Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 31. Global Machine Learning in Retail Market Size Share by Application (2021-2026)Table 32. North America Key Players Machine Learning in Retail Revenue (2019-2020) (Million US$)Table 33. North America Key Players Machine Learning in Retail Market Share (2019-2020)Table 34. North America Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 35. North America Machine Learning in Retail Market Share by Type (2015-2020)Table 36. North America Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 37. North America Machine Learning in Retail Market Share by Application (2015-2020)Table 38. Europe Key Players Machine Learning in Retail Revenue (2019-2020) (Million US$)Table 39. Europe Key Players Machine Learning in Retail Market Share (2019-2020)Table 40. Europe Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 41. Europe Machine Learning in Retail Market Share by Type (2015-2020)Table 42. Europe Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 43. Europe Machine Learning in Retail Market Share by Application (2015-2020)Table 44. China Key Players Machine Learning in Retail Revenue (2019-2020) (Million US$)Table 45. China Key Players Machine Learning in Retail Market Share (2019-2020)Table 46. China Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 47. China Machine Learning in Retail Market Share by Type (2015-2020)Table 48. China Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 49. China Machine Learning in Retail Market Share by Application (2015-2020)Table 50. Japan Key Players Machine Learning in Retail Revenue (2019-2020) (Million US$)Table 51. Japan Key Players Machine Learning in Retail Market Share (2019-2020)Table 52. Japan Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 53. Japan Machine Learning in Retail Market Share by Type (2015-2020)Table 54. Japan Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 55. Japan Machine Learning in Retail Market Share by Application (2015-2020)Table 56. Southeast Asia Key Players Machine Learning in Retail Revenue (2019-2020) (Million US$)Table 57. Southeast Asia Key Players Machine Learning in Retail Market Share (2019-2020)Table 58. Southeast Asia Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 59. Southeast Asia Machine Learning in Retail Market Share by Type (2015-2020)Table 60. Southeast Asia Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 61. Southeast Asia Machine Learning in Retail Market Share by Application (2015-2020)Table 62. India Key Players Machine Learning in Retail Revenue (2019-2020) (Million US$)Table 63. India Key Players Machine Learning in Retail Market Share (2019-2020)Table 64. India Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 65. India Machine Learning in Retail Market Share by Type (2015-2020)Table 66. India Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 67. India Machine Learning in Retail Market Share by Application (2015-2020)Table 68. Central & South America Key Players Machine Learning in Retail Revenue (2019-2020) (Million US$)Table 69. Central & South America Key Players Machine Learning in Retail Market Share (2019-2020)Table 70. Central & South America Machine Learning in Retail Market Size by Type (2015-2020) (Million US$)Table 71. Central & South America Machine Learning in Retail Market Share by Type (2015-2020)Table 72. Central & South America Machine Learning in Retail Market Size by Application (2015-2020) (Million US$)Table 73. Central & South America Machine Learning in Retail Market Share by Application (2015-2020)Table 74. IBM Company DetailsTable 75. IBM Business OverviewTable 76. IBM ProductTable 77. IBM Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 78. IBM Recent DevelopmentTable 79. Microsoft Company DetailsTable 80. Microsoft Business OverviewTable 81. Microsoft ProductTable 82. Microsoft Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 83. Microsoft Recent DevelopmentTable 84. Amazon Web Services Company DetailsTable 85. Amazon Web Services Business OverviewTable 86. Amazon Web Services ProductTable 87. Amazon Web Services Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 88. Amazon Web Services Recent DevelopmentTable 89. Oracle Company DetailsTable 90. Oracle Business OverviewTable 91. Oracle ProductTable 92. Oracle Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 93. Oracle Recent DevelopmentTable 94. SAP Company DetailsTable 95. SAP Business OverviewTable 96. SAP ProductTable 97. SAP Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 98. SAP Recent DevelopmentTable 99. Intel Company DetailsTable 100. Intel Business OverviewTable 101. Intel ProductTable 102. Intel Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 103. Intel Recent DevelopmentTable 104. NVIDIA Company DetailsTable 105. NVIDIA Business OverviewTable 106. NVIDIA ProductTable 107. NVIDIA Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 108. NVIDIA Recent DevelopmentTable 109. Google Business OverviewTable 110. Google ProductTable 111. Google Company DetailsTable 112. Google Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 113. Google Recent DevelopmentTable 114. Sentient Technologies Company DetailsTable 115. Sentient Technologies Business OverviewTable 116. Sentient Technologies ProductTable 117. Sentient Technologies Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 118. Sentient Technologies Recent DevelopmentTable 119. Salesforce Company DetailsTable 120. Salesforce Business OverviewTable 121. Salesforce ProductTable 122. Salesforce Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 123. Salesforce Recent DevelopmentTable 124. ViSenze Company DetailsTable 125. ViSenze Business OverviewTable 126. ViSenze ProductTable 127. ViSenze Revenue in Machine Learning in Retail Business (2015-2020) (Million US$)Table 128. ViSenze Recent DevelopmentTable 129. Research Programs/Design for This ReportTable 130. Key Data Information from Secondary SourcesTable 131. Key Data Information from Primary Sources

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Machine Learning in Retail Market forecast to 2026 interpreted by a new report - WhaTech Technology and Markets News