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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

Get a Free Sample @ https://www.reportsandmarkets.com/sample-request/global-machine-learning-as-a-service-market-size-status-and-forecast-2019-2025?utm_source=bisouv&utm_medium=34

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

Get complete Report: https://www.reportsandmarkets.com/sample-request/global-machine-learning-as-a-service-market-size-status-and-forecast-2019-2025?utm_source=bisouv&utm_medium=34

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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.

The rest is here:
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

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.

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.

The rest is here:
Intel works with Deci to speed up machine learning on its chips - VentureBeat

Researchers from NVIDIA and Harvard University have introduced a machine learning-driven toolkit calledAtacWorksthat has the potential to bring about remarkable advancements in genome sequencing.

What is genome sequencing?

Genome sequencing was introduced by British biochemist Frederick Sanger and his team in 1977. The world was fascinated by how this new technology could uncover human similarity and genetic diversity in new ways.

A genome is a map of all the nucleotides in our body, andgenome sequencingis a technique used to generate this nucleotide map to decode our DNA. The human genome consists of over 3 billion nucleotides.

Genome sequencing has helped scientists figure out the location of various genes and how they work together to ensure the growth and maintenance of organisms. It has served as an essential tool in the study of hereditary diseases and genetic abnormalities.

Current challenges and limitations of genome sequencing techniques

The traditional technique,ATAC-seq, measures the intensity of signals across the genome and plots the data in a graph. However, ATAC-seq can perform DNA sequencing efficiently only if it has access to many cells. We need a large number of cells (in the order of thousands) to carry out reasonably efficient genome sequencing. The fewer cells available, the noisier the data, and the more challenging it is to analyze rare cell types.

In addition, the traditional process is time-consuming. This poses a significant challenge to studying genetic mutations in organisms, like viruses, that rapidly mutate.

Introducing AtacWorks: the latest game-changer in genome sequencing

A machine learning driven toolkit called,AtacWorks,was created by researchers from NVIDIA and Harvard University to help address some of the challenges we face in genome sequencing.

AtacWorks is a Pytorch basedConvolutional Neural Network (CNN)trained to differentiate between data and noise and pick out peaks in a noisy data set. AtacWorks can be combined with ATAC-seq data to obtain the same quality data using lesser data points (lesser number of cells in this case). Researchers have found that AtacWorks can produce the same quality data from 1 million data points as was earlier done using 50 million data points.

In addition, AtacWorks helps speed up analysis by usingtensor core GPUs. This makes it possible to complete the full genome analysis in just 30 minutes a radical difference compared to the traditional 15-hour time frame.

In theresearch paper published in Nature Communications, Harvard researchers applied AtacWorks to a dataset of stem cells that produce red and white blood cells. Stem cells are rare cell types and are often found in very small numbers in the human body at a time.

Using a sample of just 50 stem cells, the researchers were able to identify distinct regions of the DNA of a stem cell that causes it to evolve into a red blood cell or a white blood cell. They were also able to isolate DNA sequences that correspond to red blood cells.

This remarkable breakthrough made by machine learning in genome sequencing has the potential to lead to the discovery of new drugs and explore evolution through the study of new mutations.

Source: https://ngc.nvidia.com/catalog/resources/nvidia:atacworks

Paper: https://www.nature.com/articles/s41467-021-21765-5

Github: https://github.com/clara-parabricks/AtacWorks

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The rest is here:
NVIDIA and Harvard University Researchers Introduce AtacWorks: A Machine Learning Toolkit to Revolutionize Genome Sequencing - MarkTechPost