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According toBlueWeave Consulting, The globalArtificial Intelligence market&Advanced Machine has reached USD 29.8 Billion in 2019 and projected to reach USD 281.24 Billion by 2026 and anticipated to grow with a CAGR of 37.95% during the forecast period from 2020-2026, owing to increasing overall global investment in Artificial Intelligence Technology.

Artificial Intelligence (AI) is a computer science algorithm and analytics-driven approach to replicate human intelligence in a machine and Machine learning (ML) is an enhanced application of artificial intelligence, which allows software applications to predict the resulted accurately. The development of powerful and affordable cloud computing infrastructure is having a substantial impact on the growth potential of artificial intelligence and the advanced machine learning market. In addition, diversifying application areas of the technology, as well as a growing level of customer satisfaction by users of AI & ML services and products is another factor that is currently driving the Artificial Intelligence & Advanced Machine Learning market. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Proliferation in data generation is another major driving factor for the AI & Advanced ML market. As natural learning develops, artificial intelligence and advanced machine learning technology are paving the way for effective marketing, content creation, and consumer interactions.

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Large enterprises segment in global Artificial Intelligence & Advanced Machine Learning market estimated to have the fastest growth during the forecast period

In the organization size segment, the large enterprises segment is estimated to have the largest market share and the SMEs segment is estimated to grow at the highest CAGR over the forecast period of 2026. The rapidly developing and highly active SMEs have raised the adoption of artificial intelligence and machine learning solutions globally, as a result of the increasing digitization and raised the cyber risks to critical business information and data. Large enterprises have been heavily adopting artificial intelligence and machine learning to extract the required information from large amounts of data and forecast the outcome of various problems.

The rising trend of AI in machine learning and predictive analysis is the key factor for driving global market with a lucrative growth rate in upcoming years.

Predictive analysis and machine learning and are rapidly used in retail, finance, and healthcare. The trend is estimated to continue as major technology companies are investing resources in the development of AI and ML. Due to the large cost-saving, effort-saving, and reliable benefits of AI automation, machine learning is anticipated to drive the global artificial intelligence and advanced machine learning market during the forecast period of 2026.

The rising digitalization boosting growth trend during the forecast period

Digitalization has become a vital driver of artificial intelligence and the advanced machine learning market across the region. Digitalization is increasingly propelling everything from hotel bookings, transport to healthcare in many economies around the globe. Digitalization had led to a rise in the volume of data generated by business processes. Moreover, business developers or crucial executives are opting for solutions that let them act as data modelers and provide them an adaptive semantic model. With the help of artificial intelligence and advanced machine learning, business users are able to modify dashboards and reports as well as help users filter or develop reports based on their key indicators.

North America is expected to dominate the Artificial Intelligence & Advanced Machine Learning market during the anticipated period.

Geographically, the Global Artificial Intelligence & Advanced Machine Learning market is bifurcated into North America, Asia Pacific, Europe, Middle East, Africa & Latin America. North America is dominating the market due to the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. North America has rapidly changed, and the most competitive global market in the world. The Asia-pacific region is estimated to be the fastest-growing region in the global AI & Advanced ML market. The rising awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the Asia-pacific region, has led Asia-pacific to become a highly potential market.

Browse Detailed Table of Contents, Artificial Intelligence & Advanced Machine Learning Market Size, By Function (Manufacturing, Operations, Sales and Marketing, Customer Support, Research and Development, Others), By Organization Size (Small and Medium Enterprise, Large Enterprise), By Industry Vertical (Consumer Goods and Retail, Healthcare, Automotive, IT and Telecom, Banking, Financial Services and Insurance, Government, Others (Education, Media and Entertainment etc.)), and By Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa); Trend Analysis, Competitive Market Share & Forecast, 2016-26

AThttps://www.blueweaveconsulting.com/artificial-intelligence-and-advanced-machine-learning-market-bwc19415

Artificial Intelligence & Advanced Machine Learning Market: Competitive Landscape

The major market players in the Artificial Intelligence & Advanced Machine Learning market are ICarbonX, TIBCO Software Inc., SAP SE, Fractal Analytics Inc., Next IT, Iflexion, Icreon, Prisma Labs, AIBrain, Oracle Corporation, Quadratyx, NVIDIA, Inbenta, Numenta, Intel, Domino Data Lab, Inc., Neoteric, UruIT, Waverley Software, and Other Prominent Players are expanding their presence in the market by implementing various innovations and technology.

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Artificial Intelligence & Advanced Machine learning market is expected to grow at a CAGR of 37.95% from 2020-2026 KSU | The Sentinel Newspaper -...

March 22, 2021 (Reports and Markets) Machine Learning as a Service Market

Reports And Markets newly added a research report on the Machine Learning as a Service market, which represents a study for the period from 2021 to 2027. The research study provides a near look at the market scenario and dynamics impacting its growth. This report highlights the crucial developments along with other events happening in the market which are marking on the growth and opening doors for future growth in the coming years. Additionally, the report is built on the basis of the macro- and micro-economic factors and historical data that can influence the growth.

The report offers valuable insight into the Machine Learning as a Service market progress and approaches related to the Machine Learning as a Service market with an analysis of each region. The report goes on to talk about the dominant aspects of the market and examine each segment.

Key Players: Amazon, Oracle, IBM, Microsoftn, Google, Salesforce, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, and Xeround

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The global Machine Learning as a Service market segmented by company, region (country), by Type, and by Application. Players, stakeholders, and other participants in the global Machine Learning as a Service market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by region (country), by Type, and by Application for the period 2021-2027.

Market Segment by Regions, regional analysis covers

North America (United States, Canada and Mexico)

Europe (Germany, France, UK, Russia and Italy)

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

South America (Brazil, Argentina, Colombia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Key Points of the Geographical Analysis:

Data and information related to the consumption rate in each region

The estimated increase in the consumption rate

The expected growth rate of the regional markets

Proposed growth of the market share of each region

Geographical contribution to market revenue

Research objectives:

The report lists the major players in the regions and their respective market share on the basis of global revenue. It also explains their strategic moves in the past few years, investments in product innovation, and changes in leadership to stay ahead in the competition. This will give the reader an edge over others as a well-informed decision can be made looking at the holistic picture of the market.

Table of Contents: Machine Learning as a Service Market

Chapter 1: Overview of Machine Learning as a Service Market

Chapter 2: Global Market Status and Forecast by Regions

Chapter 3: Global Market Status and Forecast by Types

Chapter 4: Global Market Status and Forecast by Downstream Industry

Chapter 5: Market Driving Factor Analysis

Chapter 6: Market Competition Status by Major Manufacturers

Chapter 7: Major Manufacturers Introduction and Market Data

Chapter 8: Upstream and Downstream Market Analysis

Chapter 9: Cost and Gross Margin Analysis

Chapter 10: Marketing Status Analysis

Chapter 11: Market Report Conclusion

Chapter 12: Research Methodology and Reference

Key questions answered in this report

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Reports and Markets is not just another company in this domain but is a part of a veteran group called Algoro Research Consultants Pvt. Ltd. It offers premium progressive statistical surveying, market research reports, analysis & forecast data for a wide range of sectors both for the government and private agencies all across the world. The database of the company is updated on a daily basis. Our database contains a variety of industry verticals that include: Food Beverage, Automotive, Chemicals and Energy, IT & Telecom, Consumer, Healthcare, and many more. Each and every report goes through the appropriate research methodology, Checked from the professionals and analysts.

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Machine Learning as a Service Market Production, Revenue and Price Forecast by Type 2021 to 2027 Post Impact of Worldwide COVID-19 Spread Analysis|...

A novel machine learning method called DeepBAR could accelerate drug discovery and protein engineering, researchers say.

A new technology combining chemistry and machine learning could aid researchers during the drug discovery and screening process, according to scientists at MIT, US.

The new technique, called DeepBAR, quickly calculates the binding affinities between drug candidates and their targets. The approach yields precise calculations in a fraction of the time compared to previous methods. The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.

Our method is orders of magnitude faster than before, meaning we can have drug discovery that is both efficient and reliable, said Professor Bin Zhang, co-author of the studys paper.The affinity between a drug molecule and a target protein is measured by a quantity called the binding free energy the smaller the number, the better the bind.A lower binding free energy means the drug can better compete against other molecules, meaning it can more effectively disrupt the proteins normal function.

Calculating the binding free energy of a drug candidate provides an indicator of a drugs potential effectiveness. However, it is a difficult quantity to discover.Methods for computing binding free energy fall into two broad categories:

The researchers devised an approach to get the best of both worlds. DeepBAR computes binding free energy exactly, but requires just a fraction of the calculations demanded by previous methods.

The BAR in DeepBAR stands for Bennett acceptance ratio, a decades-old algorithm used in exact calculations of binding free energy. Using the Bennet acceptance ratio typically requires a knowledge of two endpoint states, eg, a drug molecule bound to a protein and a drug molecule completely dissociated from a protein, plus knowledge of many intermediate states, eg, varying levels of partial binding, all of which slow down calculation speed.

DeepBAR reduces in-between states by deploying the Bennett acceptance ratio in machine learning frameworks called deep generative models.

These models create a reference state for each endpoint, the bound state and the unbound state, said Zhang. These two reference states are similar enough that the Bennett acceptance ratio can be used directly, without all the costly intermediate steps.

It is basically the same model that people use to do computer image synthesis, says Zhang. We are sort of treating each molecular structure as an image, which the model can learn. So, this project is building on the effort of the machine learning community.

These models were originally developed for two-dimensional (2D) images, said lead author of the study Xinqiang Ding. But here we have proteins and molecules it is really a three-dimensional (3D) structure. So, adapting those methods in our case was the biggest technical challenge we had to overcome.

In tests using small protein-like molecules, DeepBAR calculated binding free energy nearly 50 times faster than previous methods. The researchers add that, in addition to drug screening, DeepBAR could aid protein design and engineering, since the method could be used to model interactions between multiple proteins.

In the future, the researchers plan to improve DeepBARs ability to run calculations for large proteins, a task made feasible by recent advances in computer science.

This research is an example of combining traditional computational chemistry methods, developed over decades, with the latest developments in machine learning, said Ding. So, we achieved something that would have been impossible before now.

The research is published in Journal of Physical Chemistry Letters.

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Machine learning calculates affinities of drug candidates and targets - Drug Target Review

Machine learning is becoming the go-to solution for greater automation and intelligence. A recent study fielded amongst the subscribers of DBTA found that 48% currently have machine learning initiatives underway with another 20% considering adoption. At the same time, most projects are still in the early phases.

DBTA recently held a roundtable webinar with Gaurav Deshpande, VP of marketing, TigerGraph; Santiago Giraldo, director of product marketing data engineering and machine learning, Cloudera; and Paige Roberts, open source relations manager, Vertica, who discussed key technologies and strategies for maximizing machine learnings impact.

Advanced analytics and machine learning on connected data allows organizations to connect all data sets and pipelines, analyze that connected data, and learn from that connected data, Deshpande explained.

TigerGraph is a scalable graph database for the enterprise that is foundational for AI and ML solutions, he said. It offers flexible schema, high performance for complex transactions, and high performance for deep analytics.

The success of machine learning adoption is intertwined, collaboration is critical, said Giraldo. It requires an enterprise data platform that streamlines the full data lifecycle.

Machine learning with Cloudera provides customers with a hybrid platform across multiple clouds and data centers. Cloudera is one of the only offerings with integrated experiences with SDX backed security and governance, said Giraldo. It enables collaborative and integrated BI and augmentation from expert data scientists to data analysts.

Applications and services that enable our data-driven world use both BI and data science, according to Roberts.

When choosing the best platform that includes machine learning, she suggests not committing to only open source, only proprietary, or only one brand.

Dont lock yourself in to only one deployment optionsolution only works on-prem, only works on cloud, or only works on this cloud, Roberts said.

Users should not tightly couple componentseverything should be interchangeable, Roberts said. Switching out one component shouldnt break everything. And plan for the future, dont get locked in, she said.

An archived on-demand replay of this webinar is available here.

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Experts Talk Machine Learning Best Practices for Database Management - Database Trends and Applications

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Intel today announced a strategic business and technology collaboration with Deci to optimize machine learning on the formers processors. Deci says that in the coming weeks, it will work with Intel to deploy innovative AI technologies to the companies mutual customers.

Machine learning deployments have historically been constrained by the size and speed of algorithms and the need for costly hardware. In fact, areportfrom MIT found that machine learning might be approaching computational limits. A separate Syncedstudy estimated that the University of Washingtons Grover fake news detection modelcost $25,000 to train in about two weeks. OpenAI reportedly racked up a whopping $12 million to train itsGPT-3 language model, and Google spent an estimated $6,912 trainingBERT, a bidirectional transformer model that redefined the state of the art for 11 natural language processing tasks.

Intel and Deci say the partnership will enable machine learning at scale on Intel chips, potentially enabling new applications of inference through reductions in costs and latency. Already, Deci has worked to accelerate the inference speed of the well-known ResNet-50 neural network on Intel processors, achieving a reduction in the models latency by a factor of 11.8 and increasing throughput by up to 11 times.

By optimizing the AI models that run on Intels hardware, Deci enables customers to get even more speed and will allow for cost-effective and more general deep learning use cases on Intel CPUs, Deci CEO and cofounder Yonatan Geifman said. We are delighted to collaborate with Intel to deliver even greater value to our mutual customers and look forward to a successful partnership.

Deci achieves runtime acceleration through a combination of data preprocessing and loading, selecting model architectures and hyperparameters (i.e., the variables that influence a models predictions) as well as datasets optimized for inference. It also takes care of steps like deployment, serving, monitoring, and explainability. Decis accelerator redesigns models to create new models with several computation routes, all optimized for a given inference device.

Decis router component ensures that each data input is directed via the proper route. (Each route is specialized with a prediction task.) As for the companys accelerator, it works in synergy with other compression techniques like pruning and quantization. The accelerator can even act as a multiplier for complementary acceleration solutions such as AI compilers and specialized hardware, according to the company.

Deci was cofounded by Geifman, entrepreneur Jonathan Elial, and Ran El-Yaniv, a computer science professor at Technion in Haifa, Israel. Geifman and El-Yaniv met at Technion, where Geifman is a PhD candidate at the universitys computer science department. To date, the Tel Aviv-based company, a participant in Intels Ignite startup accelerator, has raised $9.1 million from investors including Square Peg.

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Intel works with Deci to speed up machine learning on its chips - VentureBeat