The report, titled Global Data Science and Machine Learning Service market defines and briefs readers about its products, applications, and specifications. The research lists key companies operating in the global market and also highlights the key changing trends adopted by the companies to maintain their dominance. By using SWOT analysis and Porter’s five force analysis tools, the strengths, weaknesses, opportunities, and threats of key companies are all mentioned in the report. All leading players in this global market are profiled with details such as product types, business overview, sales, manufacturing base, competitors, applications, and specifications.

Various factors are responsible behind the market’s growth trail, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the Data Science and Machine Learning Service market.

Different global regions such as North America, Latin America, Asia-Pacific, Europe, and India have been analyzed on the basis of the manufacturing base, productivity, and profit margin. This Data Science and Machine Learning Service market research report has been scrutinized on the basis of different practical oriented case studies from various industry experts and policymakers. It uses numerous graphical presentation techniques such as tables, charts, graphs, pictures and flowchart for easy and better understanding to the readers.

Different internal and external factors such as, Data Science and Machine Learning Service Market have been elaborated which are responsible for driving or restraining the progress of the companies. To discover the global opportunities different methodologies have been included to increase customers rapidly.

The report’s conclusion leads into the overall scope of the global market with respect to feasibility of investments in various segments of the market, along with a descriptive passage that outlines the feasibility of new projects that might succeed in the global Data Science and Machine Learning Service market in the near future.

Key questions answered in the report include:

• What will be the market size and the growth rate by the end of the forecast period?
• What are the key Data Science and Machine Learning Service Market trends impacting the growth of the market?
• What are the potential growth opportunities and threats faced by the leading competitors in the market?
• What are the key outcomes of Porter’s five forces analysis and the SWOT analysis of the key players functioning in the global Data Science and Machine Learning Service Market?
• This report gives all the information regarding industry Overview, analysis and revenue of this market.
• What are the market opportunities and threats faced by the vendors in the global Data Science and Machine Learning Service market?

Table of Content:

Global Data Science and Machine Learning Service Market Research Report 2020-2026

Chapter 1: Industry Overview

Chapter 2: Data Science and Machine Learning Service Market International and China Market Analysis

Chapter 3: Analysis of Revenue by Classifications

Chapter 4: Analysis of Revenue by Regions and Applications

Chapter 5: Analysis of Data Science and Machine Learning Service Market Revenue Market Status.

Chapter 6: Sales Price and Gross Margin Analysis

………Continue for TOC……..

The post Latest innovative report on Data Science and Machine Learning Service Market by 2026 with top key players like International Business Machine, DataScience.com appeared first on Artificial Intelligence.

Global Data Science and Machine Learning Service Market Report is recently published by Reports N Markets is a value addition to the studied Market. The compilation of the research report is conditioned by primary and secondary research techniques. The report also elaborates on factors that are fueling or hampering the growth of the current Global Data Science and Machine Learning Service Market. It focuses on recent trends and technologies that boost company performance. It also offers a panoramic view of the competitive landscape that will aid in differentiating competition at global and national level. To approximate risks and challenges involved, various business models have been presented.

It enumerates the financial aspects of the businesses such as profit margin, gross margin, market shares, and pricing structures. Business profiles of several top-level industries have been examined to get a clear idea about the successful strategies followed by them.  The report is rich with illustrations like infographics, charts, tables, and pictures to generate a strong footing for detailed analysis of recent trends and technological advancements in the Global Data Science and Machine Learning Service Market.

The report studies established names and emerging startups in the industry, to give the flavor of the entire business canvas. Different case studies from industry experts and policymakers have been mentioned for a clear understanding of the Global Data Science and Machine Learning Service Market. It also offers comprehensive information on the product or service portfolio. All these factors which are studied in this research report are predicted to propel the Global Data Science and Machine Learning Service Market.

Table of Contents

Chapter 1 Global Data Science and Machine Learning Service Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Data Science and Machine Learning Service Market Competition by Companies

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Data Science and Machine Learning Service Market Analysis by Application

Chapter 8 Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Global Data Science and Machine Learning Service Market Effect Factors Analysis

Chapter 12 Global Data Science and Machine Learning Service Market Forecast

The post Data Science and Machine Learning Service Market by 2021-2027 with Profiling Key Players International Business Machine, DataScience.com, Google, ZS, At&T, Fico, LatentView Analytics, Mango Solutions appeared first on Artificial Intelligence.

Tirupati: An international research workshop on “Advances in Deep learning and Applications” being organised by The Indian Institute of Information Technology (IIIT), Sri City from February 22-26 begun on Monday. Speaking on the occasion, IIIT Director Prof G Kannabiran said the workshop was aimed to give the participants the fundamentals and advances in deep learning with applications and hands-on experience from some of the leading experts in the field.

Delivering the inaugural address the Chairman, Board of Directors M Balasubramaniam has emphasised the importance of AI and Machine Learning as the building block of the intellectual or digital highway for accessibility of new technologies. The Government of India is working on developing an AI ecosystem where AI will be utilised at all levels. Deep learning has witnessed a rapid growth in the area of artificial intelligence.

The post International workshop on ‘Advances in Deep Learning’ begins at IIIT appeared first on Artificial Intelligence.

A web-based online international training on ‘Automation and robotics in agriculture’ has been organised by the Department of Farm Machinery and Power Engineering at Punjab Agricultural University (PAU). The 10-day international training is being conducted under the aegis of the School of Natural Resource Management (SNRM), a project sponsored by the Indian Council of Agricultural Research (ICAR) under the umbrella of the National Higher Education Project-Centre of Advanced Agricultural Science and Technology (NAHEP-CAAST), which is operational at the PAU.

Selected young faculty, representatives of the industry, research scholars and students from state agricultural universities, ICAR institutes and countries such as Canada, South Korea, USA and Kenya are attending the course.

Dr AK Garg, Director, International Cooperation and Bilateral Trade, Innovation and IPR Division (R&D) in Electronics Group, Ministry of Electronics and IT, New Delhi, was the chief guest during the inaugural function. Dr GK Sangha, Dean, Postgraduate Studies, was the guest of honour. Dr OP Choudahry, head, Department of Soil Science, and project investigator of the NAHEP-CAAST project, shed light on different activities conducted under the SNRM.

Earlier, Dr Manjeet Singh, head, Department of Farm Machinery and Power Engineering, and Director, International Training, welcomed guests and participants. He said speakers and experts, who are delivering their presentation, are from various parts of the world as well as different disciplines such as computer science, electronics, agricultural engineering and agriculture.

Coordinators of international training Dr Vishal Bector, associate director, International Linkages, and Dr Aseem Verma, scientist from the Department of Farm Machinery and Power Engineering, were among those others present online during the inaugural function.

The post International training on automation, robotics in agriculture held appeared first on Artificial Intelligence.

AMES, Iowa — A team of Iowa State University graduate students beat nearly 150 teams from 114 universities in 28 countries, bringing home the top prize from the 20th annual Data Mining Cup. The winners were announced July 3 in Berlin.

Prudsys AG, a leading European data mining company, sponsors the intelligent-data analysis competition for universities. Data mining is essentially “finding and quantifying useful non-random patterns in large data sets,” said Stephen Vardeman, University Professor of statistics and industrial engineering.

Teams had six weeks to come up with a mathematical model to detect cases of fraud at self-checkout stations in grocery stores without putting innocent customers through unnecessary inspections.

“I was really confident in our model because it captured the relationship between input and outcome,” said Iowa State’s first place team leader and statistics Ph.D. candidate Qihao Zhang. “Sometimes people will try to use a complicated model, but the outcome may not be that stable. We developed a really simple model — which is not easy to find — to achieve our winning performance.”

In addition to Iowa State’s first place win, a second Iowa State team placed eighth in the competition.

This year’s achievement adds to an impressive history at the Data Mining Cup. In 2013, Iowa State entered the competition for the first time and placed fifth. In 2014, Iowa State was the first U.S. team to win the competition. Iowa State placed second and third in 2015, and second and fifth in 2016. This year, only 10 U.S. universities ranked in the competition, with George Washington University finishing third and the next closest to Iowa State’s eighth place finish landing in 45th place.

“Often, the top teams in these data mining prediction contests are within the margin of error from each other,” Vardeman said. “But Iowa State’s winners clearly blew the competition away this year.”

A jury scored all of the submitted solutions, and invited the top 10 teams to Berlin to present their solution methods at last week’s Prudsys retail intelligence summit, which examined how retailers can use artificial intelligence to be successful. Each team gave a 10-minute presentation.

The first place Iowa State team received a prize of 2,000 euros (about $2,244) and a plaque. Switzerland’s University of Geneva took second place, and Washington, D.C.’s George Washington University placed third.

Iowa State’s first place team members and their departments are Xingche Guo (statistics), Gang Han (statistics), Haoyan Hu (statistics), Qinglong Tian (statistics), Shaodong Wang (industrial and manufacturing systems engineering), Yueying Wang (statistics), Haihan Yu (statistics), Lijin Zhang (statistics), Qihao Zhang (statistics), Zerui Zhang (bioinformatics and computational biology), Wenting Zhao (statistics) and Yifan Zhu (statistics).

Iowa State’s eighth place team members and their departments are Oscar Aguilar (statistics), Reyhaneh Bijari (industrial and manufacturing systems engineering), Souradeep Chattopadhyay (statistics), Kanak Choudhury (statistics), Samira Karimzadeh (industrial and manufacturing systems engineering) and Hanisha Vemireddy (industrial and manufacturing systems engineering).

Guo, Tian, Zhu and Yueying Wang went to Berlin for the presentation and announcement.

“This is absolutely independent external validation of the strength of our students and our programs,” Vardeman said.

The post Iowa State students develop self-checkout fraud detection model to win international data mining competition appeared first on Artificial Intelligence.

International Business Machines Corporation IBM recently updated Watson Studio with AutoAI capabilities.

As the name suggests, AutoAI enables enterprises to automate complex arduous processes, comprising optimizing, designing and other related tasks by leveraging robust AI capabilities.

Markedly, innovation in machine learning (ML) and improvement in data set quality drives development of AI tools. IBM rolled out AutoAI with an aim to free up time for data engineers to design sturdy ML models to expedite AI deployment.

AutoAI capabilities function in sync with Watson Machine Learning to accelerate AI processes considerably. AutoAI is available in “Watson Studio on the IBM Cloud.”

IBM is leaving no stone unturned to strategically infuse AI capabilities across its offerings, which hold promise. AutoAI is anticipated to boost adoption of IBM Watson Studio services, consequently bolstering the company’s top line in the days ahead.

Noteworthy Features

With AutoAI, data scientists can utilize “hyperparameter optimization capabilities” which facilitates development of AI models. Moreover, AutoAI is loaded with robust enterprise data science models, including gradient boosted trees which allow users to upscale ML deployment processes.

In a bid to empower users with deep learning (DL) capabilities, AutoAI allows access to IBM Neural Networks Synthesis (NeuNetS). Notably, NeuNetS utilizes AI to aid development of DL model and automates data synthesis across custom-designed neural networks. Further, NeuNetS provides users with the flexibility to prioritize accuracy or speed of the data model.

Increasing Spend on AI Favors Growth Prospects

Huge volume of data is being generated through a wide range of sources. In a bid to harness this data constructively, the companies intend to leverage AI to provide specific solutions and enhance solutions’ capabilities. However, deployment of AI tools is not trouble free.

For instance, per an IBM study, Shifting Toward Enterprise-Grade AI , conducted last year, 63% of surveyed individuals cited “lack of proper technical skills was a prime challenge to AI implementations.”

Moreover, according to a Forrester study, 60% of surveyed candidates claimed management of data quality to be one of the headwinds in delivering AI applications.

Markedly, IBM’s new Watson AutoAI capabilities provide companies with robust data models, consequently accelerating their AI processes.

We must note that the number of companies utilizing enterprise AI is increasing, which favors the prospects of AutoAI tool in Watson Studio. Per a report by IBM, customer satisfaction is the primary reason behind around 77% of organizations focusing on AI investments.

Further, IDC data projects worldwide spending on AI systems to hit $79.2 billion by 2022 at a CAGR of 38% from 2018 to 2022.

The move is expected to strengthen the company’s position in machine learning as a service (MLaaS) market, which is envisioned to hit $8.31 billion by 2023 per a MarketReportsWorld report, as revealed by MarketWatch.

The aforementioned secular trends reinforce IBM’s growth prospects in the longer haul.

Competition a Woe

Rising competition from Amazon Web Services (AWS) and Microsoft Azure, which dominate the cloud infrastructure services market, is a headwind. Notably, the cloud players are strengthening their IT infrastructure with advanced ML capabilities. For instance, Microsoft recently rolled out additional functionalities to Azure Machine Learning.

According to latest first quarter data from Synergy Research, IBM Cloud is classified as “strong niche player” with comparatively “lower growth rates.”

Zacks Rank & Key Picks

IBM carries a Zacks Rank #3 (Hold).

Some better-ranked stocks worth considering in the broader sector are eGain Corporation EGAN , Rosetta Stone Inc. RST and j2 Global, Inc. JCOM , each sporting Zacks Rank #1 (Strong Buy). You can see the complete list of today’s Zacks #1 Rank stocks here .

Long-term earnings growth rate for eGain, Rosetta Stone and j2 Global is pegged at 30%, 12.5% and 8%, respectively.

Today’s Best Stocks from Zacks

Would you like to see the updated picks from our best market-beating strategies? From 2017 through 2018, while the S&P 500 gained +15.8%, five of our screens returned +38.0%, +61.3%, +61.6%, +68.1%, and +98.3%.

This outperformance has not just been a recent phenomenon. From 2000 – 2018, while the S&P averaged +4.8% per year, our top strategies averaged up to +56.2% per year.

The post IBM Watson Studio’s Latest AutoAI Tool to Automate Processes appeared first on Artificial Intelligence.