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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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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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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

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

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

Today, when we look around, the technological advances in recent years have been immense. We can see driverless cars, hands-free devices that can turn on the lights, and robots working in factories, which prove that intelligent machines are possible.

In the last four years in the Indian startup ecosystem, the terms that were used (overused rather) more than funding, valuation, and exit were artificial intelligence (AI) and machine learning (ML). We also saw investors readily putting in their money in startups that remotely used or claimed to use these emerging technologies.

From deeptech, ecommerce, fintech, and conversational chatbots to mobility, foodtech, and healthcare, AI and ML have transformed most industry sectors today.

The industry has swiftly moved from asking programmers to feed tonnes of code to the machine to acquiring terabytes of data and crunching it to build relevant logic.

Sameer Dhanrajani, chief strategic officer of analytics startup Fractal Analytics, says,

A subset of artificial intelligence, machine learning allows systems to make predictions and crucial business decisions, driven by data and pattern-based experiences. Without humans having to intervene, the algorithms that are fed to the systems are helping them develop and improve their own models and understanding of a certain use-case.

According to a study carried out by Analytics India and AnalytixLabs, the Indian data analytics market is expected to double its size by 2020, with about 24 percent being attributed to Big Data. It said that almost 60 percent of the analytics revenue across India comes from exports of analytics to the USA. Domestic revenue accounts for only four percent of the total analytics revenue across the country.

The BFSI industry accounts for almost 37 percent of the total analytics market while generating almost $756 million. While marketing and advertising comes second at 26 percent, ecommerce contributes to about 15 percent.

At present, the average paycheck sizes of AI and ML engineers in India start from Rs 10 lakh per annum and the maximum cap often crosses Rs 50 lakh per annum.

According to a report by Great Learning, an edtech startup for professional education, India is expected to see 1.5 lakh new openings in Data Science in 2020, an increase of about 62 percent as compared to that of 2019. Currently, 70 percent of job postings in this sector are for Data Scientists with less than five years of work experience.

Shantanu Bhattacharya, a data scientist at Locus, had told YourStory earlier about the phenomenon, and opined that it is wrong to look at machine learning as a tool or a career path, and that it is only a convenient means to develop training models to solve problems in general.

The fluid nature of data science allows people from multiple fields of expertise to come and crack it. Shantanu believes if JRR Tolkien, being the brilliant linguist that he was, pursued data science to develop NLP models, he would have been the greatest NLP expert ever, and that is the kind of liberty and scope data science offers.

Needless to say, AI and ML have the scope to exponentially amplify the profitability and efficiency of a business by automating many tasks. And naturally, the trend has spread its wings to the jobs market where the dire need for experts and engineers in these technologies is only going up, and does not seem to slow down.

Thanks to the hefty paychecks and faster career growth, the role of machine learning engineers has claimed the top spot in job portals.

Hari Krishnan Nair, the co-founder of Great Learning, says,

For a country like India, acquiring new skills is not something of a luxury but a necessary requirement, and the trends of upskilling and reskilling are also currently on the rise to complement with the same. But data science, machine learning, and artificial intelligence are those fields where mere book-reading and formulaic interpretation and execution just does not cut it.

If one aspires to have a competitive career in futuristic technologies, machine learning and data science have a larger spectrum of required understanding of probability, statistics, and mathematics on a fundamental level.

To break the myths around programmers and software developers entering this market, machine learning involves understanding of basic programming languages (Python, SQL, R), linear algebra and calculus, as well as inferential and descriptive statistics.

Siddharth Das, Founder of Univ.ai, an early stage edtech startup that focuses on teaching these tools, says,

For a business world that thrives on data and its leverage, the science around it is where the employment economy is moving towards. While the youth of the country is anxious how rapid their upskilling rate is ought to be, it is no easy mountain to climb to rightfully master the art of data science, which it is often referred to as.

Most professionals say it is a consistent routine of learning for almost six to eight months, to be an expert in this field. During this time, when the industry is almost on the verge of fully migrating to NLP and Neural Networks, which are a significant part of future deep-tech, now is more than a better time to start learning machine learning.

With rapidly changing technological paradigms, predicting how the world is going to run is something close to impossible. And being prepared for anything is the best one can manage with, at the moment.

(Edited by Megha Reddy)

See the original post here:
Technologies of the future, but where are AI and ML headed to? - YourStory

MIDDLETOWN Lord Fairfax Community College hosted technologist Gary McGraw on Wednesday night. He spoke of the cutting edge work being done at the Berryville Institute of Machine Learning, which he co-founded a year ago.

The talk was part of the colleges Tech Bytes series of presentations by industry professionals connected to technology.

The Berryville Institute of Machine Learning is working to educate tech engineers and others about the risks they need to think about while building, adopting and designing machine learning systems. These systems involve computer programs called neural networks that learn to perform a task such as facial recognition by being trained on lots of data, such as by the use of pictures, McGraw said.

Its important that we dont take security for granted or overlook security in the rush to adopt AI everywhere, McGraw said.

One easily relatable adaptation of this technology is in smartphones, which are using AI to analyze conversations, photos and web searches, all to process peoples data, he said.

There should be privacy by default. There is not. They are collecting your data you are the product, he said.

The institute anticipates within a week or two releasing a report titled An Architectural Risk Analysis of Machine Learning Systems in which 78 risks in machine learning systems are identified.

McGraw told the audience that, while not interchangeable terms, artificial intelligence and machine learning have been sold as magic technology that will miraculously solve problems. He said that is wrong. The raw data used in machine learning can be manipulated and it can open up systems to risks, such as system attacks that could compromise information, even confidential information.

McGraw cited a few of those risks.

One risk is someone fooling a machine learning system by presenting malicious input of data that can cause a system to make a false prediction or categorization. Another risk is if an attacker can intentionally manipulate the data being used by a machine learning system, the entire system can be compromised.

One of the most often discussed risks is data confidentiality. McGraw said data protection is already difficult enough without machine learning. In machine learning, there is a unique challenge in protecting data because it is possible that through subtle means information contained in the machine learning model could be extracted.

LFCC Student Myra Diaz, who is studying computer science at the college, attended the program.

I like it. I am curious and so interested to see how can we get a computer to be judgmental in a positive way, such as judging what it is seeing, Diaz said.

Remaining speakers for this years Tech Bytes programs are:

6 p.m. Feb. 19: Kay Connelly, Informatics.

1 p.m. March 11:Retired Secretary of the Navy Richard Danzig

6 p.m. April 8: Heather Wilson, Analytics, L Brands

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Expert: Don't overlook security in rush to adopt AI - The Winchester Star

A few years ago, I wondered how edge data would ever be useful given the enormous cost of transmitting all the data to either the centralized data center or some variant of cloud infrastructure. (It is said that 5G will solve that problem).

Consider, for example, applications of vast sensor networks that stream a great deal of data at small intervals. Vehicles on the move are a good example.

There is telemetry from cameras, radar, sonar, GPS and LIDAR, the latter about 70MB/sec. This could quickly amount to four terabytes per day (per vehicle). How much of this data needs to be retained? Answers I heard a few years ago were along two lines:

My counterarguments at the time were:

Introducing TensorFlow federated, via The TensorFlow Blog:

This centralized approach can be problematic if the data is sensitive or expensive to centralize. Wouldn't it be better if we could run the data analysis and machine learning right on the devices where that data is generated, and still be able to aggregate together what's been learned?

Since I looked at this a few years ago, the distinction between an edge device and a sensor has more or less disappeared. Sensors can transmit via wifi (though there is an issue of battery life, and if they're remote, that's a problem); the definition of the edge has widened quite a bit.

Decentralized data collection and processing have become more powerful and able to do an impressive amount of computing. The case is point in Intel's Introducing the Intel Neural Compute Stick 2 computer vision and deep learning accelerator powered by the Intel Movidius Myriad X VPU, that can stick into a Pi for less than $70.00.

But for truly distributed processing, the Apple A13 chipset in the iPhone 11 has a few features that boggle the mind: From Inside Apple's A13 Bionic system-on-chip Neural Engine, a custom block of silicon separate from the CPU and GPU, focused on accelerating Machine Learning computations. The CPU has a set of "machine learning accelerators" that perform matrix multiplication operations up to six times faster than the CPU alone. It's not clear how exactly this hardware is accessed, but for tasks like machine learning (ML) that use lots of matrix operations, the CPU is a powerhouse. Note that this matrix multiplication hardware is part of the CPU cores and separate from the Neural Engine hardware.

This should beg the question, "Why would a smartphone have neural net and machine learning capabilities, and does that have anything to do with the data transmission problem for the edge?" A few years ago, I thought the idea wasn't feasible, but the capability of distributed devices has accelerated. How far-fetched is this?

Let's roll the clock back thirty years. The finance department of a large diversified organization would prepare in the fall a package of spreadsheets for every part of the organization that had budget authority. The sheets would start with low-level detail, official assumptions, etc. until they all rolled up to a small number of summary sheets that were submitted headquarters. This was a terrible, cumbersome way of doing things, but it does, in a way, presage the concept of federated learning.

Another idea that vanished is Push Technology that shared the same network load as centralizing sensor data, just in the opposite direction. About twenty-five years, when everyone had a networked PC on their desk, the PointCast Network used push technology. Still, it did not perform as well as expected, often believed to be because its traffic burdened corporate networks with excessive bandwidth use, and was banned in many places. If Federated Learning works, those problems have to be addressed

Though this estimate changes every day, there are 3 billion smartphones in the world and 7 billion connected devices.You can almost hear the buzz in the air of all of that data that is always flying around. The canonical image of ML is that all of that data needs to find a home somewhere so that algorithms can crunch through it to yield insights. There are a few problems with this, especially if the data is coming from personal devices, such as smartphones, Fitbit's, even smart homes.

Moving highly personal data across the network raises privacy issues. It is also costly to centralize this data at scale. Storage in the cloud is asymptotically approaching zero in cost, but the transmission costs are not. That includes both local WiFi from the devices (or even cellular) and the long-distance transmission from the local collectors to the central repository. This s all very expensive at this scale.

Suppose, large-scale AI training could be done on each device, bringing the algorithm to the data, rather than vice-versa? It would be possible for each device to contribute to a broader application while not having to send their data over the network. This idea has become respectable enough that it has a name - Federated Learning.

Jumping ahead, there is no controversy that training a network without compromising device performance and user experience, or compressing a model and resorting to a lower accuracy are not alternatives. In Federated Learning: The Future of Distributed Machine Learning:

To train a machine learning model, traditional machine learning adopts a centralized approach that requires the training data to be aggregated on a single machine or in a datacenter. This is practically what giant AI companies such as Google, Facebook, and Amazon have been doing over the years. This centralized training approach, however, is privacy-intrusive, especially for mobile phone usersTo train or obtain a better machine learning model under such a centralized training approach, mobile phone users have to trade their privacy by sending their personal data stored inside phones to the clouds owned by the AI companies.

The federated learning approach decentralizes training across mobile phones dispersed across geography. The presumption is that they collaboratively develop machine learning while keeping their personal data on their phones. For example, building a general-purpose recommendation engine for music listeners. While the personal data and personal information are retained on the phone, I am not at all comfortable that data contained in the result sent to the collector cannot be reverse-engineered - and I havent heard a convincing argument to the contrary.

Here is how it works. A computing group, for example, is a collection of mobile devices that have opted to be part of a large scale AI program. The device is "pushed" a model and executes it locally and learns as the model processes the data. There are some alternatives to this. Homogeneous models imply that every device is working with the same schema of data. Alternatively, there are heterogeneous models where harmonization of the data happens in the cloud.

Here are some questions in my mind.

Here is the fuzzy part: federated learning sends the results of the learning as well as some operational detail such as model parameters and corresponding weights back to the cloud. How does it do that and preserve your privacy and not clog up your network? The answer is that the results are a fraction of the data, and since the data itself is not more than a few Gb, that seems plausible. The results sent to the cloud can be encrypted with, for example, homomorphic encryption (HE). An alternative is to send the data as a tensor, which is not encrypted because it is not understandable by anything but the algorithm. The update is then aggregated with other user updates to improve the shared model. Most importantly, all the training data remains on the user's devices.

In CDO Review, The Future of AI. May Be In Federated Learning:

Federated Learning allows for faster deployment and testing of smarter models, lower latency, and less power consumption, all while ensuring privacy. Also, in addition to providing an update to the shared model, the improved (local) model on your phone can be used immediately, powering experiences personalized by the way you use your phone.

There is a lot more to say about this. The privacy claims are a little hard to believe. When an algorithm is pushed to your phone, it is easy to imagine how this can backfire. Even the tensor representation can create a problem. Indirect reference to real data may be secure, but patterns across an extensive collection can surely emerge.

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Federated machine learning is coming - here's the questions we should be asking - Diginomica

In the last two decades, Artificial Intelligence has steadily made its way into versatile industry applications. This has helped businesses reap major rewards by reducing operational costs, triggering efficiency, boosting revenue and improving the overall customer experience. With a constantly evolving range of technologies, efforts are on to develop AI to a stage where it reduces human intervention to the minimum. This is where the relevance of Machine Learning and Cloud comes in. As businesses transform the way in which they communicate, work and grow, the importance of Cloud in deploying Machine Learning models becomes important. Because of the massive storage and processing of data, Machine Learning often involves the application of computational power to train models because of the lack of strong machines. Thus, when Cloud is paired with ML models, it forms the Intelligent Cloud that becomes a suitable destination for any companys Machine Learning projects. The Cloud will enable ML data to make more accurate predictions and analyze data more efficiently, enhancing business value by a huge extent. With so many developments, any study of Machine Learning is incomplete without learning about its association with the Cloud.To help working professionals become a part of any companys end-to-end packaged ML solution, IIT Madras in collaboration with upGrad, has launched the ML and Cloud program. As one of Indias largest online education platforms, it recognizes the huge potential of taking Machine Learning to the Cloud, and how the first step to enable this is to train ML professionals in the right direction. Lets take a look at how upGrads ML program in Cloud will help professionals skill up in the foreseeable future.'; var randomNumber = Math.random(); var isIndia = (window.geoinfo && window.geoinfo.CountryCode === 'IN') && (window.location.href.indexOf('outsideindia') === -1 ); console.log(isIndia && randomNumber A revolutionary advanced certification course in Machine Learning and CloudIn the current business set up, data and insights can be termed as the true currency for business operation. This is why every organization is immensely scaling up its ML capabilities. upGrads Advanced Certification in Machine Learning and Cloud is helping learners become Machine Learning experts by training them to deploy machine learning models using PySpark on Cloud. This prestigious certification provides students with the opportunity to learn from a set of experienced Machine Learning faculty and industry leaders. Another highlight of this 9-month program is that it has about 300+ hiring partners, ensuring that professionals who choose to upskill with this course ends up in the industry of their choice.

The Advanced Certification in Machine Learning and Cloud by upGrad seeks to build employability of professionals and also boost up their annual packages. The requirement for ML professionals has now percolated to multiple industry domains like e-commerce, retail, healthcare, banking, manufacturing, transport, NBFC, and finance, among others. The course offers an equal opportunity to every learner, enhancing their relevance in the company they will work for. In Data and ML related hirings, recruiters look for people who are proficient and knowledgeable and can prove to be assets to employers in the company. This certification program by upGrad is an excellent opportunity to make a credible career transition. Considering ML is one of the fastest-growing fields in Data today, Machine Learning engineers are getting hired at astounding pay packages. In fact, an Indeed survey revealed that there has been more than a 300% spike in ML hirings since 2015. Considering this shift, upGrads Advanced Program in Machine Learning and Cloud is the best way to flag off ones ML journey.

Top skills that the program will offer Programming: Learners will be working in core and necessary languages like Python and SQL since the former is required for ML and the latter for the Cloud.

Machine learning concepts: The program is set to offer a holistic understanding of both basic and advanced subjects within ML. This includes the application of the appropriate ML algorithm to categorize unknown data or make predictions about it. Also included is the ability to modify and craft algorithms of your own should and when the need arises.

Foundations of Cloud and Hadoop: It also included knowledge about Hadoop, Hive, and HDFS along with the implementation of ML algorithms in the cloud on Spark/ PySpark (AWS/ Azure/ GCP). Overall, the curriculum is designed so that students learn the local Python implementation as well as the cloud PySpark implementation of classical machine learning algorithms.

Who should apply for this program?Keeping the overall market landscape in mind, this program by upGrad is ideal for the following categories:

The pedagogy and content of upGrads Advanced Program in Machine Learning and Cloud is a perfect integration of online lectures, offline engagement, practical case studies, and interactive networking sessions. The platform provides full support to young professionals in their ML journey by also catering to the needs of employers by training the future workforce in all data-related aspects. Whether it is resume feedback, mock interview sessions with industry experts, or conducting placement drives with top-notch companies, upGrad has provided it all to its learners. Many of these learners have also been placed at companies like KPMG, Uber, Big Basket, Bain & Co, Pwc, Zivame, Fractal Analytics, Microsoft etc. with impressive salary shifts.

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Looking for an impressive salary hike? Power up your career with upGrads Machine Learning and Cloud prog - Times of India

In 2020, small to medium sized businesses (SMBs) are likely to focus more on supporting workers to travel and collaborate in ways that suit them, while still facing a clear economic imperative to keep costs under control.

This will likely involve increased use of technologies such as machine learning and automation to: help determine and enforce spending policies; ensure people travelling for work can optimise, track, and analyse their spend; and prioritise travel options that meet goals around environmental responsibility and sustainability.

Businesses that recognise and respond to these trends will be better-placed to save money while improving employee engagement and performance, according to SAP Concur.

Fabian Calle, General Manager, Small to Medium Business, ANZ, SAP Concur, said, As the new decade begins, the business environment will be subject to the same economic ups and downs seen in the previous decade. However, with new technologies and approaches, most businesses will be able to leverage automation and even artificial intelligence to smooth out those peaks and troughs.

SAP Concur has identified the top five 2020 predictions for SMBs, covering economics, technology, business, travel, the environment, diversity, and corporate social responsibility:

Calle said, 2020 will continue to drive significant developments as organisations of all sizes look to optimise efficiency and productivity through employee operations and satisfaction. Australian businesses need to be aware of these trends and adopt cutting edge technology to facilitate their workers need to travel and collaborate more effectively and with less effort.

Read more from the original source:
Machine learning and eco-consciousness key business trends in 2020 - Finfeed