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Global Machine Learning (ML) Platforms Market Growth, Size, Analysis, Outlook by 2020 – Trends, Opportunities and Forecast to 2025 – AlgosOnline

A recent report added by Market Study Report, LLC, on ' Machine Learning (ML) Platforms Market' provides a detailed analysis on the industry size, revenue forecasts and geographical landscape pertaining to this business space. Additionally, the report highlights primary obstacles and latest growth trends accepted by key players that form a part of the competitive spectrum of this business.

The research study on the Machine Learning (ML) Platforms market projects this industry to garner substantial proceeds by the end of the projected duration, with a commendable growth rate liable to be registered over the estimated timeframe. Elucidating a pivotal overview of this business space, the report includes information pertaining to the remuneration presently held by this industry, in tandem with a meticulous illustration of the Machine Learning (ML) Platforms market segmentation and the growth opportunities prevailing across this vertical.

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A brief run-through of the industry segmentation encompassed in the Machine Learning (ML) Platforms market report:

Competitive landscape:

Companies involved:

Vital pointers enumerated:

The Machine Learning (ML) Platforms market report provides an outline of the vendor landscape that includes companies such as

The study mentions the products manufactured by these esteemed companies as well the product price prototypes, profit margins, valuation accrued, and product sales.

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Geographical landscape:

Regions involved: USA, Europe, Japan, China, India, South East Asia

Vital pointers enumerated:

Segmented into USA, Europe, Japan, China, India, South East Asia, as per the regional spectrum, the Machine Learning (ML) Platforms market apparently covers most of the pivotal geographies, claims the report, which compiles a highly comprehensive analysis of the geographical arena, including details about the product consumption patterns, revenue procured, as well as the market share that each zone holds.

The study presents details regrading the consumption market share and product consumption growth rate of the regions in question, in tandem with the geographical consumption rate with regards to the products and the applications.

Product landscape

Product types involved:

Vital pointers enumerated:

The Machine Learning (ML) Platforms market report enumerates information with respect to every product type among

Application landscape:

Application sectors involved:

Vital pointers enumerated:

The Machine Learning (ML) Platforms market report, with respect to the application spectrum, splits the industry into

The Machine Learning (ML) Platforms market report also includes substantial information about the driving forces impacting the commercialization landscape of the industry as well as the latest trends prevailing in the market. Also included in the study is a list of the challenges that this industry will portray over the forecast period.

Other parameters like the market concentration ratio, enumerated with reference to numerous concentration classes over the projected timeline, have been presented as well, in the report.

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2. Global Transformer Dismantling & Recycling Services Market Growth (Status and Outlook) 2020-2025Transformer Dismantling & Recycling Services Market Report covers the makers' information, including shipment, value, income, net benefit, talk with record, business appropriation and so forth., this information enables the buyer to think about the contenders better. This report additionally covers every one of the districts and nations of the world, which demonstrates a provincial advancement status, including market size, volume and esteem, and also value information. It additionally covers diverse enterprises customers data, which is critical for the producers.Read More:

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Global Machine Learning (ML) Platforms Market Growth, Size, Analysis, Outlook by 2020 - Trends, Opportunities and Forecast to 2025 - AlgosOnline

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Four Benefits Of Artificial Intelligence And Machine Learning In Banking – CIO Applications

Artificial intelligence in banking helps clients evaluate the vast amount of information, from the users request in social networks to make informed and safe decisions.

Fremont, CA: Artificial intelligence and machine learning in banking offer many opportunities for personalization, data analysis, tasks solving abilities, and also reasonable costs for implementation.

The widespread rise in the importance of artificial intelligence and machine learning for banking has strong foundations as the technologies offer new and useful benefit.

Here are four benefits of artificial intelligence and machine learning in banking:

A Cutting Edge Advantage:

Machine learning in banks have the capability to make users more competitive according to the task they want to solve.

Advanced Data Analysis:

Banks used to evaluate data with less access to information such as when a client comes with a request to issue a loan, the decision was made only based on the statement of income, current assets and liabilities of the client, and the credit history. Today, artificial intelligence in banking helps clients evaluate the vast amount of information, from the users request in social networks to make informed and safe decisions.

Better Security:

Artificial intelligence in banking can be implemented in various ways to achieve higher security. Credit card fraud detection implementing machine learning has become a common application of the technology, and innovative cameras with face recognition can identify if a client has wrong intentions by judging the facial expressions.

Costs Cut:

Artificial intelligence and machine learning can help cut costs for banks and financial institutions based on how these technologies are used. Integrating robo-advisors in the support team can help reduce the cost of staff maintenance.

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TopBanking Technology Solution Companies

TopBanking Technology Consulting/Service Companies

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Four Benefits Of Artificial Intelligence And Machine Learning In Banking - CIO Applications

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National Grid sees machine learning as the brains behind the utility business of the future – TechCrunch

If the portfolio of a corporate venture capital firm can be taken as a signal for the strategic priorities of their parent companies, then National Grid has high hopes for automation as the future of the utility industry.

The heavy emphasis on automation and machine learning from one of the nations largest privately held utilities with a customer base numbering around 20 million people is significant. And a sign of where the industry could be going.

Since its launch, National Grids venture firm, National Grid Partners, has invested in 16 startups that featured machine learning at the core of their pitch. Most recently, the company backed AI Dash, which uses machine learning algorithms to analyze satellite images and infer the encroachment of vegetation on National Grid power lines to avoid outages.

Another recent investment, Aperio, uses data from sensors monitoring critical infrastructure to predict loss of data quality from degradation or cyberattacks.

Indeed, of the $175 million in investments the firm has made, roughly $135 million has been committed to companies leveraging machine learning for their services.

AI will be critical for the energy industry to achieve aggressive decarbonization and decentralization goals, said Lisa Lambert, the chief technology and innovation officer at National Grid and the founder and president of National Grid Partners.

National Grid started the year off slowly because of the COVID-19 epidemic, but the pace of its investments picked up and the company is on track to hit its investment targets for the year, Lambert said.

Modernization is critical for an industry that still mostly runs on spreadsheets and collective knowledge that has locked in an aging employee base, with no contingency plans in the event of retirement, Lambert said. Its that situation thats compelling National Grid and other utilities to automate more of their business.

Most companies in the utility sector are trying to automate now for efficiency reasons and cost reasons. Today, most companies have everything written down in manuals; as an industry, we basically still run our networks off spreadsheets, and the skills and experience of the people who run the networks. So weve got serious issues if those people retire. Automating [and] digitizing is top of mind for all the utilities weve talked to in the Next Grid Alliance.

To date, a lot of the automation work thats been done has been around basic automation of business processes. But there are new capabilities on the horizon that will push the automation of different activities up the value chain, Lambert said.

ML is the next level predictive maintenance of your assets, delivering for the customer. Uniphore, for example: youre learning from every interaction you have with your customer, incorporating that into the algorithm, and the next time you meet a customer, youre going to do better. So thats the next generation, Lambert said. Once everything is digital, youre learning from those engagements whether engaging an asset or a human being.

Lambert sees another source of demand for new machine learning tech in the need for utilities to rapidly decarbonize. The move away from fossil fuels will necessitate entirely new ways of operating and managing a power grid. One where humans are less likely to be in the loop.

In the next five years, utilities have to get automation and analytics right if theyre going to have any chance at a net-zero world youre going to need to run those assets differently, said Lambert. Windmills and solar panels are not [part of] traditional distribution networks. A lot of traditional engineers probably dont think about the need to innovate, because theyre building out the engineering technology that was relevant when assets were built decades ago whereas all these renewable assets have been built in the era of OT/IT.

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An introduction to data science and machine learning with Microsoft Excel – TechTalks

This article is part ofAI education, a series of posts that review and explore educational content on data science and machine learning. (In partnership withPaperspace)

Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasnt touched. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence.

But mastering machine learning is a difficult process. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch.

And if you want to create machine learning systems that integrate and scale, youll have to learn cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.

Naturally, not everyone needs to become a machine learning engineer. But almost everyone who is running a business or organization that systematically collects and processes can benefit from some knowledge of data science and machine learning. Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.

But in my experience, a good understanding of data science and machine learning requires some hands-on experience with algorithms. In this regard, a very valuable and often-overlooked tool is Microsoft Excel.

To most people, MS Excel is a spreadsheet application that stores data in tabular format and performs very basic mathematical operations. But in reality, Excel is a powerful computation tool that can solve complicated problems. Excel also has many features that allow you to create machine learning models directly into your workbooks.

While Ive been using Excels mathematical tools for years, I didnt come to appreciate its use for learning and applying data science and machine learning until I picked up Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods by Hong Zhou.

Learn Data Mining Through Excel takes you through the basics of machine learning step by step and shows how you can implement many algorithms using basic Excel functions and a few of the applications advanced tools.

While Excel will in no way replace Python machine learning, it is a great window to learn the basics of AI and solve many basic problems without writing a line of code.

Linear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes. Linear regression is especially useful when your data is neatly arranged in tabular format. Excel has several features that enable you to create regression models from tabular data in your spreadsheets.

One of the most intuitive is the data chart tool, which is a powerful data visualization feature. For instance, the scatter plot chart displays the values of your data on a cartesian plane. But in addition to showing the distribution of your data, Excels chart tool can create a machine learning model that can predict the changes in the values of your data. The feature, called Trendline, creates a regression model from your data. You can set the trendline to one of several regression algorithms, including linear, polynomial, logarithmic, and exponential. You can also configure the chart to display the parameters of your machine learning model, which you can use to predict the outcome of new observations.

You can add several trendlines to the same chart. This makes it easy to quickly test and compare the performance of different machine learning models on your data.

In addition to exploring the chart tool, Learn Data Mining Through Excel takes you through several other procedures that can help develop more advanced regression models. These include formulas such as LINEST and LINREG formulas, which calculate the parameters of your machine learning models based on your training data.

The author also takes you through the step-by-step creation of linear regression models using Excels basic formulas such as SUM and SUMPRODUCT. This is a recurring theme in the book: Youll see the mathematical formula of a machine learning model, learn the basic reasoning behind it, and create it step by step by combining values and formulas in several cells and cell arrays.

While this might not be the most efficient way to do production-level data science work, it is certainly a very good way to learn the workings of machine learning algorithms.

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Beyond regression models, you can use Excel for other machine learning algorithms. Learn Data Mining Through Excel provides a rich roster of supervised and unsupervised machine learning algorithms, including k-means clustering, k-nearest neighbor, nave Bayes classification, and decision trees.

The process can get a bit convoluted at times, but if you stay on track, the logic will easily fall in place. For instance, in the k-means clustering chapter, youll get to use a vast array of Excel formulas and features (INDEX, IF, AVERAGEIF, ADDRESS, and many others) across several worksheets to calculate cluster centers and refine them. This is not a very efficient way to do clustering, youll be able to track and study your clusters as they become refined in every consecutive sheet. From an educational standpoint, the experience is very different from programming books where you provide a machine learning library function your data points and it outputs the clusters and their properties.

In the decision tree chapter, you will go through the process calculating entropy and selecting features for each branch of your machine learning model. Again, the process is slow and manual, but seeing under the hood of the machine learning algorithm is a rewarding experience.

In many of the books chapters, youll use the Solver tool to minimize your loss function. This is where youll see the limits of Excel, because even a simple model with a dozen parameters can slow your computer down to a crawl, especially if your data sample is several hundred rows in size. But the Solver is an especially powerful tool when you want to finetune the parameters of your machine learning model.

Learn Data Mining Through Excel shows that Excel can even advanced machine learning algorithms. Theres a chapter that delves into the meticulous creation of deep learning models. First, youll create a single layer artificial neural network with less than a dozen parameters. Then youll expand on the concept to create a deep learning model with hidden layers. The computation is very slow and inefficient, but it works, and the components are the same: cell values, formulas, and the powerful Solver tool.

In the last chapter, youll create a rudimentary natural language processing (NLP) application, using Excel to create a sentiment analysis machine learning model. Youll use formulas to create a bag of words model, preprocess and tokenize hotel reviews and classify them based on the density of positive and negative keywords. In the process youll learn quite a bit about how contemporary AI deals with language and how much different it is from how we humans process written and spoken language.

Whether youre making C-level decisions at your company, working in human resources, or managing supply chains and manufacturing facilities, a basic knowledge of machine learning will be important if you will be working with data scientists and AI people. Likewise, if youre a reporter covering AI news or a PR agency working on behalf a company that uses machine learning, writing about the technology without knowing how it works is a bad idea (I will write a separate post about the many awful AI pitches I receive every day). In my opinion, Learn Data Mining Through Excel is a smooth and quick read that will help you gain that important knowledge.

Beyond learning the basics, Excel can be a powerful addition to your repertoire of machine learning tools. While its not good for dealing with big data sets and complicated algorithms, it can help with the visualization and analysis of smaller batches of data. The results you obtain from a quick Excel mining can provide pertinent insights in choosing the right direction and machine learning algorithm to tackle the problem at hand.

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An introduction to data science and machine learning with Microsoft Excel - TechTalks

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Amazon AWS says Very, very sophisticated practitioners of machine learning are moving to SageMaker – ZDNet

AWS's AmazonSageMaker software, a set of tools for deploying machine learning, is not only spreading throughout many companies, it is becoming a key tool for some of the more demanding kinds of practitioners of machine learning, one of the executives in charge of it says.

"We are seeing very, very sophisticated practitioners moving to SageMaker because we take care of the infrastructure, and so it makes them an order-of-magnitude more productive," said Bratin Saha, AWS's vice president in charge of machine learning and engines.

Saha spoke with ZDNet during the third week of AWS's annual re:Invent conference, which this year was held virtually because of the pandemic.

The benefits of SageMaker have to do with all the details of how to stage training tasks and deploy inference tasks across a variety of infrastructure.

SageMaker, introduced in 2017, can automate a lot of the grunt work that goes into setting up and running such tasks.

"Amazon dot com has invested in machine learning for more than twenty years, and they are moving on to SageMaker, and we have very sophisticated machine learning going on at Amazon dot com," says Amazon AWS's vice president for ML and engines, Bratin Saha.

While SageMaker might seem like something that automates machine learning for people who don't know how to do the basics, Saha told ZDNet that even experienced machine learning scientists find value in speeding up the routine tasks in a program's development.

"What they had to do up till now is spin up a cluster, make sure that the cluster was well utilized, spend a lot of time checking as the model is deployed, am I getting traffic spikes," said Saha, describing the traditional deployment tasks that had to be carried out by a machine learning data scientist. That workflow extends from initially gathering the data to labeling the data (in the case of labeled training), refine the model architecture, and then deploying trained models for inference usage and monitoring and maintaining those inference models as long as they are running live.

"You don't have to do any of that now," said Saha. "SageMaker gives you training that is server-less, in the sense that your billing starts when your model starts training, and stops when your model stops training."

Also: Amazon AWS unveils RedShift ML to 'bring machine learning to more builders'

Added Saha, "In addition, it works with spotinstances in a very transparent way; you don't have to say, Hey, have my spot instances been pre-empted, is my job getting killed, SageMaker takes care of all of that." Such effective staging of jobs can reduce costs by ninety percent, Saha contends.

Saha said that customers such as Lyft and Intuit, despite having machine learning capabilities of their own, are more and more taking up the software to streamline their production systems.

"We have some of the most sophisticated customers working on SageMaker," said Saha.

"Look at Lyft, they are standardizing their training on SageMaker, their training times have come down from several days to a few hours," said Saha. "MobileEye is using SageMaker training," he said, referring to the autonomous vehicle chip unit within Intel. "Intuit has been able to reduce their training time from six months to a few days." Other customers include the NFL, JP Morgan Chase, Georgia Pacific, Saha noted.

Also: Amazon AWS analytics director sees analysis spreading much more widely throughout organizations

Amazon itself has moved its AI work internally to SageMaker, he said. "Amazon dot com has invested in machine learning for more than twenty years, and they are moving on to SageMaker, and we have very sophisticated machine learning going on at Amazon dot com." As one example, Amazon's Alexa voice-activated appliance uses SageMaker Neo, an optimization tool that compiles trained models into a binary program with settings that will make the model run most efficiently when being used for inference tasks.

There are numerous other parts of SageMaker, such as pre-built containers with select machine learning algorithms; a "Feature Store" where one can pick out attributes to use in training; and what's known as the Data Wrangler to create original model features from training data.

AWS has been steadily adding to the tool set.

During his AWS re:Invent keynote two weeks ago, Amazon's vice president of machine learning, Swami Sivasubramanian, announced that SageMaker can now automatically break up the parts of a large neural net and distribute those parts across multiple computers. This form of parallel computing, known as model parallelism, is usually something that takes substantial effort.

Amazon was able to reduce neural network training time by forty percent, said Sivasubramanian, for very large deep learning networks, such as "T5," a version of Google's Transformer natural language processing.

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Way to Grow in Career – Upskilling in AI and Machine Learning – Analytics Insight

There are at least two clear patterns that show a demand-supply mismatch in tech occupations in front line IT fields, for example, Artificial Intelligence and Machine Learning. One is by means of industry predictions that gauge growth in the AI market from $21.46 Bn to $190.61 Bn somewhere in the range of 2018 and 2025.

Machine learning and AI, cloud computing, cybersecurity and data science are the most pursued fields of knowledge and skills, and as innovation experts contend in the digital space quickly being surpassed by automation, huge numbers of them are upskilling themselves.

As indicated by the report from Gartner, AI-related job creation will arrive at 2,000,000 net-new openings in 2025. Notwithstanding, there arent that numerous experts with the range of abilities to match this requirement. To overcome this issue, there is an expanding need for experts to upskill in these spaces.

A study led by e-learning stage Simplilearn among 1,750 experts, discovered that 33% of respondents were spurred to take up courses to assist them with procuring better pay rates. Other persuading factors incorporate getting opportunities related to hands-on and industry-relevant projects, said 27% of those participated, while another 21% said rewards and recognition pushed them to upskill themselves.

Essentially all types of enterprise programming, transport, factory automation and different enterprises are progressively utilizing AI-based interfaces in their every day operations. Truth be told, by 2030, AI may wind up contributing USD 15.7 trillion to the worldwide economy.

Mathematical and programming aptitudes are integral to gaining competency in this field. Notwithstanding, for seasoned tech experts, it is likewise very critical to build great communication skills. A comprehension of how business functions and the common processes utilized in everyday operations will help you better use your core skills to improve authoritative work processes.

The average yearly compensation of data scientist would go between Rs 5 lakh to 42 lakh from junior to mid-range positions, trailed by the range of Rs 4.97 lakh to 50 lakh for each year for an expert skilled in cloud computing, and Rs 5 lakh to 70 lakh for every year for occupations in Artificial Intelligence, the survey anticipated.

Among the all the more exciting opportunities, one can expect in 2021 is the rising utilization of AI in healthcare to identify and analyze medical problems in people. Smart infrastructure to help balance rapid development in urban centres in India is additionally an option being explored by the government.

Tech programs presently obligatorily have AI, IoT, Machine Learning and some other basic components of arising technologies. Nonetheless, steady changes in this unique field have made it obligatory for experts to keep upskilling by means of a prominent organization to stay relevant and work commendably. When experts have upskilled themselves to fulfill the needs of the market, it is significant that they articulate their expertise productively to the hiring companies.

As robotization progressively replaces traditional entry-level technical jobs, for example, data entry and monitoring, it is turning out to be clear that moving up to cutting-edge skills, for example, AI, DL, ML and Cloud is the route forward. Artificial intelligence is assessed to supplant almost 7,000,000 positions by 2037. Automation (generally powered by AI) is probably going to affect 69% of occupations in India as corporations progressively receive the whatever can be automated, will be automated mantra to boost profitability.

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Way to Grow in Career - Upskilling in AI and Machine Learning - Analytics Insight

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