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Category Archives: Machine Learning
Comprehensive Report on Machine Learning as a Service Market Set to Witness Huge Growth by 2026 | Microsoft, International Business Machine, Amazon…
Machine Learning as a Service Marketresearch report is the new statistical data source added byA2Z Market Research. It uses several approaches for analyzing the data of target market such as primary and secondary research methodologies. It includes investigations based on historical records, current statistics, and futuristic developments.
The report gives a thorough overview of the present growth dynamics of the global Machine Learning as a Service with the help of vast market data covering all important aspects and market segments. The report gives a birds eye view of the past and present trends as well the factors expected to drive or impede the market growth prospects of the Machine Learning as a Service market in the near future.
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Machine Learning as a Service Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.
Top Key Players Profiled in this report are:
Microsoft, International Business Machine, Amazon Web Services, Google, Bigml, Fico, Hewlett-Packard Enterprise Development, At&T.
The key questions answered in this report:
Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Machine Learning as a Service market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Machine Learning as a Service markets trajectory between forecast periods.
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The cost analysis of the Global Machine Learning as a Service Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.
The report provides insights on the following pointers:
Market Penetration:Comprehensive information on the product portfolios of the top players in the Machine Learning as a Service market.
Product Development/Innovation:Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.
Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.
Market Development:Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.
Market Diversification:Exhaustive information about new products, untapped geographies, recent developments, and investments in the Machine Learning as a Service market.
Table of Contents
Global Machine Learning as a Service Market Research Report 2020 2026
Chapter 1 Machine Learning as a Service Market Overview
Chapter 2 Global Economic Impact on Industry
Chapter 3 Global Market Competition by Manufacturers
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 Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Machine Learning as a Service Market Forecast
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Comprehensive Analysis On Machine Learning in Education Market Based On Types And Application – Crypto Daily
Dataintelo, one of the worlds leading market research firms has rolled out a new report on Machine Learning in Education market. The report is integrated with crucial insights on the market which will support the clients to make the right business decisions. This research will help both existing and new aspirants for Global Machine Learning in Education Market to figure out and study market needs, market size, and competition. The report provides information about the supply and demand situation, the competitive scenario, and the challenges for market growth, market opportunities, and the threats faced by key players.
The report also includes the impact of the ongoing global crisis i.e. COVID-19 on the Machine Learning in Education market and what the future holds for it. The pandemic of Coronavirus (COVID-19) has landed a major blow to every aspect of life globally. This has lead to various changes in market conditions. The swiftly transforming market scenario and initial and future assessment of the impact are covered in the report.
Request a sample Report of Machine Learning in Education Market: https://dataintelo.com/request-sample/?reportId=69421
The report is fabricated by tracking the market performance since 2015 and is one of the most detailed reports. It also covers data varying according to region and country. The insights in the report are easy to understand and include pictorial representations. These insights are also applicable in real-time scenarios. Components such as market drivers, restraints, challenges, and opportunities for Machine Learning in Education are explained in detail. Since the research team is tracking the data for the market from 2015, therefore any additional data requirement can be easily fulfilled.
The scope of the report has a wide spectrum extending from market scenarios to comparative pricing between major players, cost, and profit of the specified market regions. The numerical data is supported by statistical tools such as SWOT analysis, BCG matrix, SCOT analysis, and PESTLE analysis. The statistics are depicted in a graphical format for a clear picture of facts and figures.
The generated report is strongly based on primary research, interviews with top executives, news sources, and information insiders. Secondary research techniques are utilized for better understanding and clarity for data analysis.
The Machine Learning in Education Market is divided into the following segments to have a better understanding:
Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers
By Geographical Regions:
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The Machine Learning in Education Market industry Analysis and Forecast 20192026 help clients with customized and syndicated reports holding key importance for professionals requiring data and market analytics. The report also calls for market-driven results providing feasibility studies for client requirements. Dataintelo promises qualified and verifiable aspects of market data operating in the real-time scenario. The analytical studies are carried out ensuring client requirements with a thorough understanding of market capacities in the real-time scenario.
Some of the prominent companies that are covered in this report:
Key players, major collaborations, merger & acquisitions along with trending innovation and business policies are reviewed in the report. Following is the list of key players:
IBMMicrosoftGoogleAmazonCognizanPearsonBridge-UDreamBox LearningFishtreeJellynoteQuantum Adaptive Learning
*Note: Additional companies can be included on request
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Amazon has announced the fourth-generation version of its main Echo smart speaker, bringing a new spherical design and better sound performance. But the biggest change is a new, on-device speech recognition module that will locally process your audio on the Echo, making your requests faster than ever before.
Another big addition to the new Echo is what Amazons calling the AZ1 Neural Edge silicon module, which will process the audio of your voice requests using local machine learning speech recognition algorithms before sending the command to the cloud. The process promises to save hundreds of milliseconds in response time for your Echo.
Amazon says that the new Echo will combine the features from both the third-gen Echo and the Echo Plus and will feature a built-in Zigbee smart home hub, in addition to working with Amazon Sidewalk, the companys local networking system.
The Echo will be available in three colors: charcoal, chalk, and steel blue, and itll ship on October 22nd. Preorders are already open over at Amazons website.
Go here to read the rest:
Amazon unveils its 4th-generation Echo - The Verge
Machine Learning Answers: Facebook Stock Is Down 20% In A Month, What Are The Chances Itll Rebound? – Forbes
BRAZIL - 2020/07/10: In this photo illustration a Facebook logo seen displayed on a smartphone. ... [+] (Photo Illustration by Rafael Henrique/SOPA Images/LightRocket via Getty Images)
Facebook stock (NASDAQ: FB) reached an all-time high of almost $305 less than a month ago before a larger sell-off in the technology industry drove the stock price down nearly 20% to its current level of around $250. But will the companys stock continue its downward trajectory over the coming weeks, or is a recovery in the stock imminent?
According to the Trefis Machine Learning Engine, which identifies trends in the companys stock price data since its IPO in May 2012, returns for Facebook stock average a little over 3% in the next one-month (21 trading days) period after experiencing a 20% drop over the previous month (21 trading days). Notably, though, the stock is very likely to underperform the S&P500 over the next month (21 trading days), with an expected excess return of -3% compared to the S&P500.
But how would these numbers change if you are interested in holding Facebook stock for a shorter or a longer time period? You can test the answer and many other combinations on the Trefis Machine Learning Engine to test Facebook stock chances of a rise after a fall. You can test the chance of recovery over different time intervals of a quarter, month, or even just 1 day!
MACHINE LEARNING ENGINE try it yourself:
IFFB stock moved by -5% over 5 trading days,THENover the next 21 trading days, FB stock moves anaverageof 3.2 percent, which implies anexcess returnof 1.7 percent compared to the S&P500.
More importantly, there is 62% probability of apositive returnover the next 21 trading days and 53.8% probability of apositive excess returnafter a -5% change over 5 trading days.
Some Fun Scenarios, FAQs & Making Sense of Facebook Stock Movements:
Question 1: Is the average return for Facebook stock higher after a drop?Answer:
Consider two situations,
Case 1: Facebook stock drops by -5% or more in a week
Case 2: Facebook stock rises by 5% or more in a week
Is the average return for Facebook stock higher over the subsequent month after Case 1 or Case 2?
FB stockfares better after Case 2, with an average return of 2.4% over the next month (21 trading days) under Case 1 (where the stock has just suffered a 5% loss over the previous week), versus, an average return of 5.3% for Case 2.
In comparison, the S&P 500 has an average return of 3.1% over the next 21 trading days under Case 1, and an average return of just 0.5% for Case 2 as detailed in our dashboard that details theaverage return for the S&P 500 after a fall or rise.
Try the Trefis machine learning engine above to see for yourself how Facebook stock is likely to behave after any specific gain or loss over a period.
Question 2: Does patience pay?
If you buy and hold Facebook stock, the expectation is over time the near term fluctuations will cancel out, and the long-term positive trend will favor you at least if the company is otherwise strong.
Overall, according to data and Trefis machine learning engines calculations, patience absolutely pays for most stocks!
For FB stock, the returns over the next N days after a -5% change over the last 5 trading days is detailed in the table below, along with the returns for the S&P500:
Question 3: What about the average return after a rise if you wait for a while?
The average return after a rise is understandably lower than a fall as detailed in the previous question. Interestingly, though, if a stock has gained over the last few days, you would do better to avoid short-term bets for most stocks although FB stock appears to be an exception to this general observation.
FBs returns over the next N days after a 5% change over the last 5 trading days is detailed in the table below, along with returns for the S&P 500.
Its pretty powerful to test the trend for yourself for Facebook stock by changing the inputs in the charts above.
What if youre looking for a more balanced portfolio? Heres a high quality portfolio to beat the market with over 100% return since 2016, versus 55% for the S&P 500. Comprised of companies with strong revenue growth, healthy profits, lots of cash, and low risk, it has outperformed the broader market year after year consistently
See allTrefis Price EstimatesandDownloadTrefis Datahere
Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams |Product, R&D, and Marketing Teams
In machine learning, while building a predictive model for classification and regression tasks there are a lot of steps that are performed from exploratory data analysis to different visualization and transformation. There are a lot of transformation steps that are performed to pre-process the data and get it ready for modelling like missing value treatment, encoding the categorical data, or scaling/normalizing the data. We do all these steps and build a machine learning model but while making predictions on the testing data we often repeat the same steps that were performed while preparing the data.
So there are a lot of steps that are followed and while working on a big project in teams we can often get confused about this transformation. To resolve this we introduce pipelines that hold every step that is performed from starting to fit the data on the model.
Through this article, we will explore pipelines in machine learning and will also see how to implement these for a better understanding of all the transformations steps.
What we will learn from this article?
Pipelines are nothing but an object that holds all the processes that will take place from data transformations to model building. Suppose while building a model we have done encoding for categorical data followed by scaling/ normalizing the data and then finally fitting the training data into the model. If we will design a pipeline for this task then this object will hold all these transforming steps and we just need to call the pipeline object and rest every step that is defined will be done.
This is very useful when a team is working on the same project. Defining the pipeline will give the team members a clear understanding of different transformations taking place in the project. There is a class named Pipeline present in sklearn that allows us to do the same. All the steps in a pipeline are executed sequentially. On all the intermediate steps in the pipeline, there has to be a first fit function called and then transform whereas for the last step there will be only fit function that is usually fitting the data on the model for training.
As soon as we fit the data on the pipeline, the pipeline object is first transformed and then fitted on each of the steps. While making predictions using the pipeline, all the steps are again repeated except for the last function of prediction.
Implementation of the pipeline is very easy and involves 4 different steps mainly that are listed below:-
Let us now practically understand the pipeline and implement it on a data set. We will first import the required libraries and the data set. We will then split the data set into training and testing sets followed by defining the pipeline and then calling the fit score function. Refer to the below code for the same.
We have defined the pipeline with the object name as pipe and this can be changed according to the programmer. We have defined sc objects for StandardScaler and rfcl for Random Forest Classifier.
If we do not want to define the objects for each step like sc and rfcl for StandardScaler and Random Forest Classifier since there can be sometimes many different transformations that would be done. For this, we can make use of make_pipeling that can be imported from the pipeline class present in sklearn. Refer to the below example for the same.
from sklearn.pipeline import make_pipeline
pipe = make_pipeline(StandardScaler(),(RandomForestClassifier()))
We have just defined the functions in this case and not the objects for these functions. Now lets see the steps present in this pipeline.
Through this article, we discussed pipeline construction in machine learning. How these can be helpful while different people working on the same project to avoid confusion and get a clear understanding of each step that is performed one after another. We then discussed steps for building a pipeline that had two steps i.e scaling and the model and implemented the same on the Pima Indians Diabetes data set. At last, we explored one other way of defining a pipeline that is building a pipeline using make a pipeline.
I am currently enrolled in a Post Graduate Program In Artificial Intelligence and Machine learning. Data Science Enthusiast who likes to draw insights from the data. Always amazed with the intelligence of AI. It's really fascinating teaching a machine to see and understand images. Also, the interest gets doubled when the machine can tell you what it just saw. This is where I say I am highly interested in Computer Vision and Natural Language Processing. I love exploring different use cases that can be build with the power of AI. I am the person who first develops something and then explains it to the whole community with my writings.
New machine learning, automation capabilities added to PagerDuty’s digital operations management platform – SiliconANGLE News
During a time when it seems as though the entire planet has gone digital, the role of PagerDuty Inc. has come into sharper focus as a key player in keeping the critical work of IT organizations up and running.
Mindful of enterprise and consumer need at such an important time, the company has chosen this weeksvirtual Summit event to unveil a significant number of new product releases.
We have the biggest set of releases and investments in innovation that were unleashing in the history of the company, said Jonathan Rende (pictured), senior vice president of product and marketing at PagerDuty. PagerDuty has a unique place in that whole ecosystem in whats considered crucial and critical now. These services have never been more important and more essential to everything we do.
Rende spoke with Lisa Martin, host of theCUBE, SiliconANGLE Medias livestreaming studio, during thePagerDuty Summit 2020. They discussed the companys focus on automation to help customers manage incidents, the introduction of new tools for organizational collaboration and a trend toward full-service ownership. (* Disclosure below.)
The latest releases are focused on PagerDutys expertise in machine learning and automation to leverage customer data for faster and more accurate incident response.
In our new releases, we raised the game on what were doing to take advantage of our data that we capture and this increase in information thats coming in, Rende said. A big part of our releases has also been about applying machine learning to add context and speed up fixing, resolving and finding the root cause of issues. Were applying machine learning to better group and intelligently organize information into singular incidents that really matter.
PagerDuty is also leveraging its partner and customer network to introduce new tools for collaboration as part of its platform.
One of the things weve done in the new platform is were introducing industry-first video war rooms with our partners and customers, Zoom as well as Microsoft Teams, and updating our Slack integrations as well, Rende explained. Weve also added the ability to manage an issue through Zoom and Microsoft Teams as a part of PagerDuty.
These latest announcements are a part of what Rende describes as a move in larger companies toward broader direct involvement of both developers and IT staff in operational responsibility.
There is a material seismic shift towards full-service ownership, Rende said. Were seeing larger organizations have major initiatives around this notion of the front-line teams being empowered to work directly on these issues. Full-service ownership means you build it, you ship it, you own it, and thats for both development and IT organizations.
Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEs coverage of PagerDuty Summit 2020. (* Disclosure: TheCUBE is a paid media partner for PagerDuty Summit 2020. Neither PagerDuty Inc., the sponsor for theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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