ML Conference gives you the opportunity to learn about the latest machine learning tools and technologies, including TensorFlow 2.0, machine learning in the browser, deep learning and NLP. Seasoned ML experts will share insights on how to understand your data, optimize your models and more. The very early bird discount for ML Conference Singapore ends on January 23, so why not take a look?

ML Conference Singapore will take place from March 24-26, 2020. While the very early bird offer still stands, you can save up to $310 USD and get a free gift with your 3-day pass as well as a 10% team discount for registering 3+ colleagues.

This year, ML Conference will take place three times in different locations across the world. The usual suspects Munich and Berlin will of course be on the agenda, but now Singapore is also on the cards, meaning were bringing ML Conference to a second continent in 2020.

Check out the conference page for more information.

MLCon very early bird discount offers savings up to $310.Very early bird registration ends on January 23, 2020.Plus, theres also a few great extras included for those who strike while the iron is hot!

The tracks are:

The full program is not yet annnounced, but you can take a look at some of the highlights that are already online:

Machine Learning in the BrowserAthan Reines

Predictive Maintenance how does Data Science revolutionize the World of Machines? Victoriya Kalmanovich

Playing Doom with TF-Agents and TensorFlow 2.0 Andreas Eberle

Using Neural Networks for Natural Language Processing Christoph Henkelmann

Applying Machine Learning online at ScaleJon Bratseth

Theres still more to come, so stay tuned! Keep an eye on mlconference.ai for more.

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ML Conference Singapore very early bird offer ends January 23 - JAXenter

Following the global financial crisis, the banking industry has had to deal with more stringent risk capital requirements that demand agility, flexibility, speed, and ease of communication across traditionally siloed departments. Banks also needed a firm grasp of their enterprise-wide data to meet regulatory requirements, and also to ensure a return on capital. It is for this reason that Allen Whipple, co-founder and managing director at ActiveViam, says it makes sense for any regulatory solution to pivot from prescriptive to predictive analytics.

ActiveViam topped this category at this years AFTAs due to its FRTB Accelerator, part of a suite of Accelerator products that it launched in the past year. The products contain all the source code and formulae to meet a particular set of regulations and/or business requirements. In this case, it was those needed for the standardized approach (SA) and the internal model approach (IMA) risk framework, which stems from the Basel Committee on Banking Supervisions Fundamental Review of the Trading Book (FRTB).

The FRTB Accelerator includes capabilities such as the capital decomposition tool, which provides clients with the ability to deploy capital across an organization more precisely. This allows a client to take risk management a step further and perform predictive analysis, which can be applied to broader internal market risk scenarios, Whipple explains. He adds that banks can perform limit-monitoring and back-testing, which allows them to stay within the scope of their IMA status.

Looking ahead, ActiveViam will add a product for Python notebooks to facilitate data science work, reducing the time it takes to move from data to insight. Quants will no longer need to switch between notebooks, data visualization tools, and end-user business intelligence applications. Using the ActiveViam Python Library, they will be able to create dashboards and share them within the same environment. Coders can do everything in Jupyteror a Python notebook of choicefrom beginning to end, Whipple says.

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AFTAs 2019: Best New Technology Introduced Over the Last 12 MonthsAI, Machine Learning and AnalyticsActiveViam - http://www.waterstechnology.com

For open source development at cloud scale with a code-first experience. Basic + UI capabilities + secure and comprehensive machine learning lifecycle management for all skill levels. Automated machine learning Create and run experiments in notebooks Available Available Create and run experiments in studio web experience Not available Available Industry leading forecasting capabilities Not available Available Support for deep learning and other advanced learners Not available Available Large data support (up to 100GB) Not available Available Interpretability in UI Not available Available Machine Learning Pipelines Create, run, and publish pipelines using the Azure ML SDK Available Available Create pipeline endpoints using the Azure ML SDK Available Available Create, edit, and delete scheduled runs of pipelines using the Azure ML SDK Available Available Create and publish custom modules using the Azure ML SDK Available Available View pipeline run details in studio Available Available Create, run, visualize, and publish pipelines in Azure ML designer Not available Available Create pipeline endpoints in Azure ML designer Not available Available Create, edit, and delete scheduled runs of pipelines in Azure ML designer Not available Available Create and publish custom modules in Azure ML designer Not available Available Integrated notebooks Workspace notebook and file sharing Available Available R and Python support Available Available Notebook collaboration Available Available Compute instance Managed compute Instances for integrated Notebooks Available Available Sharing of compute instances Available Available Collaborative debugging of models Available Available Jupyter, JupyterLab, Visual Studio Code Available Available Virtual Network (VNet) support for deployment Available Available SDK Support R and Python SDK support Available Available Security Role Based Access Control (RBAC) support Available Available Virtual Network (VNet) support for training Available Available Virtual Network (VNet) support for inference Available Available Scoring endpoint authentication Available Available Compute Cross workspace capacity sharing and quotas Not available Available Data for machine learning Create, view or edit datasets and datastores from the SDK Available Available Create, view or edit datasets and datastores from the UI Available Available View, edit, or delete dataset drift monitors from the SDK Available Available View, edit, or delete dataset drift monitors from the UI Not available Available MLOps Create ML pipelines in SDK Available Available Batch inferencing Available Available Model profiling Available Available Interpretability in UI Not available Available Labeling Labeling Project Management Portal Available Available Labeler Portal Available Available Labeling using private workforce Available Available

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Pricing - Machine Learning | Microsoft Azure

Human resources has been slower to come to the table with machine learning and artificial intelligence than other fieldsmarketing, communications, even health care. But the value of machine learning in human resourcescan now be measured, thanks to advances in algorithms that can predict employee attrition, for example, or deep learning neural networks thatare edging toward more transparent reasoning in showing why a particular result or conclusion was made.

The value beyond numbers for CEOs and managersis the power inunderstanding whats actually happening within acompany i.e. withtheir people. AsGlintsJustin Black articulated in awebinar for the Human Capital Institute(HCI), executives and leaders need information that helps them point people in the right direction; informationsales data, KPIs, etc.change over time, and machine learning can react faster than people in helping draw out the insights and inferences that might otherwise take reams of manpower or not be uncovered at all.

Though not an exhaustive list, belowis an outline of solid examples of machine learning and artificial intelligence applications at work in human resources today, along with developing and near-future applications.

Applicant Tracking & Assessment

Applicant tracking and assessment has topped the list in early machine learning applications, especially for companies and roles that receive high volumes of applicants.Glintis not an AI company, but they use AI tools to help companies save money and provide a better work experience. Machine learning tools help HR and management personnel hirenew team members bytrackinga candidates journey throughout the interview process and helping speed up the processof getting streamlined feedback to applicants.

Peopliseis another solutionfor helpingcompanies calculate fit score for new talent, combining tools like digital screening and online interview results to help hiring managers arrive at decisions.

While competition for the best people has driven many HR departments to use algorithmic-based assessments, aCEBarticle on using machine learning to eliminate bias cautions that human oversight isstill of paramount importance. Its not enough to act directly on data insights, but to use this information in tandem with driving question such as: 1) how I can link applicant traits to business outcomes; 2) which outcomes should be our focus when hiring; and 3) can predictions (hiring and otherwise) be made in an unbiased way.

Attracting Talent

Attracting talent beforehiring has also seen an upswingin machine-learning based applications in the past few years. Black, who is Glints senior director of Organizational Development, named LinkedIn as an example of a company using one of the most common versions of basic machine learningrecommendingjobs. Other job-finding sites, including Indeed, Glassdoor, and Seek use similar algorithms to build interaction mapsbased on users data from previous searches, connections, posts, and clicks.

PhenomPeople is one example of a suite of machine learning-based toolsthat helps leadpotential talent to a companys career site through multiple social media and job search channels. Black notes that this is really just one step past a keyword search, albeit a big step computationally, as theres a lot more to do.

Attrition Detection

Understanding people and why they decide to stay at or leave a job is arguably one of the most important questions for HR to answer. Identifying attrition risk calls for advancedpattern recognition in surveying an array of variables.

In the earlier mentioned HCI webinar, Black describes a hypotheticalsituation of identifying specific risk factors based on scores to an employee survey. If a human were to try and detect attrition risk among female engineers in Palo Alto with less than 2 years of tenure, the variance analyses to reach that conclusion are innumerable, like finding a needle in haystack, but machine learning allows us to connect these dots in seconds, freeing HR representatives to spend time supporting teams instead of analyzing data.

Glints employee engagement platform

Advances in NLP have included the ability to process large amounts of unstructured data, and algorithms can also do things like identify emotional activity in comments and tease out prescriptive comments, or actionable suggestions. Black describesprototypicality algorithms that can pull out individual comments thatrepresent the sum of what everyones saying, allowing companies to get a broadly inclusive but digestible pulse on company processes and specific issues.

JPMorgan is apparently one of several financial institutions that hasalso put into place algorithms that can survey employee behavior and identify rogue employees before any criminal activity takes place, an obviously more insidious form of attrition with dire consequenceswatch the interview with Bloomberg Reporter Hugh Son as hediscussesthese new safeguards with Bloomberg Technology.

Individual Skills Management/Performance Development

Machine learning is showing its potential inboosting individual skill management and development. While there is definitely room for growth in this arena, platforms that cangive calibrated guidance without human coaches save time and provide the opportunity for more people togrow in their careers and stay engaged.Workday is just one example of a company building personalized training recommendations for employees based on a companys needs, market trends, and employee specifics.

Black elaborates that these types of performance development assessments are useful when actually read, which is why this type of machine-based feedback has been successful for individuals. But this becomes more difficult at the level of the organization, where its almost impossible to make sense of enormous amounts of varying data; this is an area wheremachine learning is evolving, with an increased focus on the overall performance of the corporate lattice.

Enterprise Management

As alluded to in the last example, enterprise management and engagement based on machine learning insights is already here in early forms but has yet to be taken to scale. KPMG promotes its customized Intelligent Enterprise Approach, leveraging predictive analytics and big data management to help companies make business decisions that optimize key KPIs and other metrics.re:Work, which provides best workplace practices and ideas from Google and other leading organizations (including KPMG), is an excellent resource for staying up-to-date on new tools and case studies in this space.

Googles People Analytics department has been a pioneer in building performance-management engines at the enterprise level. From an early stage, the team (led by Prasad Setty) posed existing questionsfor example, whats the ideal size for a given team or departmentbut focused on finding new ways to use data in order to help answer these questions. In turn, People Analytics has helped pave the way for solving fundamental business problems related to the employee life cycle, with afocus on improving Googlers'production and overall wellness. Asoutline by Chris Derose for The Atlantic, over the last half of a decade, the team has produced insights that have led to improvements in company-wide actions, such as:

Post-Hire Outcome Algorithms

CEB notes that theideal hiring algorithm would predicta post-hire outcome (for example, reducing time taking customer service calls while keeping customer satisfaction high) rather than just matching job requirements with items on an employees resume or pre-hire assessment results.

The article goes on to note that its sometimes the counterintuitive aspectsthat predict job performance, informationthat a machine is better at findingthrough analysis than human inference. For example, CEB describes a model created for a call center representative role that linked call center experience to resultingpoor performance. While a link to the source or actual model would be helpful, the idea is interesting and reflects machine learnings strengths in invisiblepattern recognition

Internal Management

WhenTalent AnalyticsChief Scientist Pasha Roberts discussed the role of predictive analytics in human resource management with Emerj in 2016, he brought up the internal movement of employees within a company as an issue unique to HR and analytics. You can use agent-based modeling to simulate and look at how people can move within a companyand be better able to hire a person at the entry-level that will be likely to move through corporate ladder, said Roberts. While there are early systems in place, more data over time should lead to a more robust and scalable model for internal management over the nextfive years.

Increased Behavior Tracking and Data-Based Decision Making

Ben Waber, president and CEO ofHumanyzeand also a past guest on Emerj, talked about the increasing use of IoT wearable data in the workplace. These types of gadgets are more common at the enterprise levelbluetooth headphones and smart ID badges, for exampleand companies are continuing toadd sensor technology to the workplace in order to collect data. This is an area that Waber researched while serving as a visiting scientist at the MIT Media Lab, using data collected from smart badges to look at things like employee dialogue, interaction, networks within a company, where people spent their time, etc. It would seem that privacy might be a concern, but technologies like smart badges are starting to proliferate quickly (with vendors like Atmel, in the below video, introducing new and updated apps for Android phones). This type of data, says Waber, allows us to pose and answer crucial business-driving questions that we couldnt ask before, such as how much does my sales team talk to my engineering team?

Things to Keep in Mind:Machine Learning in Human Resources

Google People Analytics Lead, Ian OKeefe, told a story at the People Analytics & Future of Work conference inJanuary 2016 about his teams efforts to quantify things like efficiency, effectiveness and employee experience by looking at hiring decisions, teamclimate, and personal development. In the end, his team found that people armed with better data make better decisions than algorithms alone can do.

Well-designed AI applications, says Black, have three main cross functions: main expertise, data science expertise, and design/user experience expertise. At present, very few providers do all three of these well. The best solutions today and in the near futuredont replace humans, but emphasize scaling better decision making with the use of machines as a tool and collaborator.

Our survey of machine learning in human resources illuminates the development of a more people-centric approach, paving the way for more more valuable programs and less wasted time; reduced bias in programs; less administration and more individual development; and the ability to act proactively rather than reactively, moving seamlessly fromthe level of the individual to the organization and back again.

Image credit: Corporate IT

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Machine Learning in Human Resources Applications and ...

As we reached the digital era, where computers became an integral part of the everyday lifestyle, people cannot help but be amazed at how far we have come since the time immemorial. The creation of the computers, as well as the internet, had led us into a more complex thinking, making information available to us with just a click. You just type in the words and information will be readily available for you.

However, as we approached this era, a lot of inventions and terms became confusing. Have you heard about Artificial intelligence? How about Deep Learning? Moreover, Machine Learning? These three words are familiar to us and can be used interchangeably, however, the exact meaning of this becomes uncertain. The more people used it, the more confusing it gets.

Also Read:Top 5 Data Science and Machine Learning Courses

Deep Learning and Machine Learning are words that followed after Artificial Intelligence was created. It is like breaking down the function of AI and naming them Deep Learning and Machine Learning. But before this gets more confusing, let us differentiate the three starting off with Artificial Intelligence.

AI is the like creating intelligence artificially. Artificial Intelligence is the broad umbrella term for attempting to make computers think the way humans think, be able to simulate the kinds of things that humans do and ultimately to solve problems in a better and faster way than we do. The AI itself is a rather generic term for solving tasks that are easy for humans, but hard for computers. It includes all kinds of tasks, such as doing creative work, planning, moving around, speaking, recognizing objects and sounds, performing social or business transactions and a lot more.

Digital era, brought an explosion of data in all forms and from every region of the world. This data, known simply as Big Data, is drawn from sources like social media, internet search engines, e-commerce platforms, online cinemas, etc. This enormous amount of data is readily accessible and can be shared through various applications like cloud computing. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to Artificial Intelligence (AI) systems for automated support.

More and more plans to try different approaches to use AI leads to the most promising and relevant area which is the Machine Learning. The most common way to process the Big Data is called Machine Learning. It is a self-adaptive algorithm that gets better and better analysis and patterns with experience or with newly added data.

For example, if a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern.

Deep learning, on the other hand, is a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning non-linear technique to weeding out a fraudulent transaction would include time, geographic location, IP address, type of retailer, and any other feature that is likely to make up a fraudulent activity.

Thus, these three are like a triangle where the AI to be the top that leads to the creation of Machine Learning with a subset of Deep Learning. These three had made our life easier as time goes by and helped make a faster and better way of gathering information that cannot be done by humans because of the enormous amount of information available.

Humans will take forever just to get a single information while these AI will only take minutes. As we become more and more comfortable using technology, the better humans can develop them into a better version of itself. You should also check our latest article:5 Best Programming Languages for Artificial Intelligence Systems

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Difference between AI, Machine Learning and Deep Learning

SAN FRANCISCO, Jan. 13, 2020 /PRNewswire/ -- The global machine learning marketsize is expected to reach USD 96.7 billion by 2025, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 43.8% from 2019 to 2025. Production of massive amounts of data has increased the adoption of technologies that can provide a smart analysis of that data.

Key suggestions from the report:

Read 100 page research report with ToC on "Machine Learning Market Size, Share & Trends Analysis Report By Component, By Enterprise Size, By End Use (Healthcare, BFSI, Law, Retail, Advertising & Media), And Segment Forecasts, 2019 - 2025" at: https://www.grandviewresearch.com/industry-analysis/machine-learning-market

Technologies such as Machine Learning (ML) are being rapidly adopted across various applications in order to automatically detect meaningful patterns within a data set. Software based on ML algorithms, such as search engines, anti-spam software, and fraud detection software, are being increasingly used, thereby contributing to market growth.

The rapid emergence of ML technology has increased its adoption across various application areas. It provides cloud computing optimization along with intelligent voice assistance. In healthcare, it is used for the diagnosis of individuals. In case of businesses, the use of ML models that are open source and have a standards-based structure has increased in recent years. These models can be easily deployed in various business programs and can help companies bridge the skills gap between IT programmers and information scientists.

Developments such as fine-tuned personalization, hyper-targeting, searching engine optimization, no-code environment, self-learning bots, and others are projected to change the machine learning landscape. The development of capsule network has replaced neural networks in order to provide more accuracy in pattern detection, with fewer errors. These advanced developments are anticipated to proliferate market growth in the foreseeable future.

Grand View Research has segmented the global machine learning market based on component, enterprise size, end use, and region:

Find more research reports on Next Generation Technologies Industry, by Grand View Research:

Gain access to Grand View Compass, our BI enabled intuitive market research database of 10,000+ reports

About Grand View Research

Grand View Research, U.S.-based market research and consulting company, provides syndicated as well as customized research reports and consulting services. Registered in California and headquartered in San Francisco, the company comprises over 425 analysts and consultants, adding more than 1200 market research reports to its vast database each year. These reports offer in-depth analysis on 46 industries across 25 major countries worldwide. With the help of an interactive market intelligence platform, Grand View Research helps Fortune 500 companies and renowned academic institutes understand the global and regional business environment and gauge the opportunities that lie ahead.

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Sherry JamesCorporate Sales Specialist, USA Grand View Research, Inc. Phone: +1-415-349-0058 Toll Free: 1-888-202-9519 Email: sales@grandviewresearch.comWeb: https://www.grandviewresearch.comFollow Us: LinkedIn| Twitter

SOURCE Grand View Research, Inc.

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Machine Learning Market Size Worth $96.7 Billion by 2025 ...

Faced with mounting compliance costs and regulatory pressures, financial institutions are rapidly adopting Artificial Intelligence (AI) solutions, including machine learning and robotic process automation (RPA) to combat sophisticated and evolving financial crimes.

Over one third of financial institutions have deployed machine learning solutions, recognizing that AI has the potential to improve the financial services industry by aiding with fraud identification, AML transaction monitoring, sanctions screening and know your customer (KYC) checks (Financier Worldwide Magazine).

When deployed in financial crime management solutions, analytical agents that leverage machine learning can help to reduce false positives, without compromising regulatory or compliance needs.

It is well known that conventional, rules-based fraud detection and AML programs generate large volumes of false positive alerts. In 2018, Forbes reported With false positive rates sometimes exceeding 90%, something is awry with most banks legacy compliance processes to fight financial crimes such as money laundering.

Such high false positive rates force investigators to waste valuable time and resources working through large alert queues, performing needless investigations, and reconciling disparate data sources to piece together evidence.

The highly regulated environment makes AML a complex, persistent and expensive challenge for FIs but increasingly, AI can help FIs control not only the complexity of their AML provisions, but also the cost (Financier Worldwide Magazine).

In an effort to reduce the costs of fraud prevention and BSA/AML compliance efforts, financial institutions should consider AI solutions, including machine learning analytical agents, for their financial crime management programs.

Machine learning agents use mathematical and statistical models to learn from data without being explicitly programmed. Financial institutions can deploy dynamic machine learning solutions to:

To effectively identify patterns, machine learning agents must process and train with a large amount of quality data. Institutions should augment data from core banking systems with:

When fighting financial crime, a single financial institution may not have enough data to effectively train high-performance analytical agents. By gathering large volumes of properly labeled data in a cloud-based environment, machine learning agents can continuously improve and evolve to accurately detect fraud and money laundering activities, and significantly improve compliance efforts for institutions.

Importing and analyzing over a billion transactions every week in our Cloud environment, Verafins big data intelligence approach allows us to build, train, and refine a proven library of machine learning agents. Leveraging this immense data set, Verafins analytical agents outperform conventional detection analytics, reducing false positives and allowing investigators to focus their efforts on truly suspicious activity. For example:

With proven behavior-based fraud detection capabilities, Verafins Deposit Fraud analytics consistently deliver 1-in-7 true positive alerts.

By deploying machine learning, Verafin was able to further improve upon these high-performing analytics resulting in an additional 66% reduction in false positives. Training our machine learning agents on check returns mapped as true fraud in the Cloud, the Deposit Fraud detection rate improved to 1-in-3 true positive alerts, while maintaining true fraud detection.

These results clearly outline the benefits of applying machine learning analytics to a large data set in a Cloud environment. In todays complex and costly financial crime landscape, financial institutions should deploy financial crime management solutions with machine learning to significantly reduce false positives, while maintaining regulatory compliance.

In an upcoming article, we will explore how and when robotic process automation can benefit financial crime management solutions.

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Machine Learning: Higher Performance Analytics for Lower ...

What Is Machine Learning?

Machine learning is theconcept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computers built-in algorithms current regardless of changes in the worldwide economy.

Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information. As such, artificial intelligence measures are being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly utilized for big data processing is machine learning.

The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. However, the model shouldnt change.

Machine learning is used in different sectors for various reasons. Trading systems can be calibrated to identify new investment opportunities. Marketing and e-commerce platforms can be tuned to provide accurate and personalized recommendations to their users based on the users internet search history or previous transactions. Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model. Information hubs can use machine learning to cover huge amounts of news stories from all corners of the world. Banks can create fraud detection tools from machine learning techniques. The incorporation of machine learning in the digital-savvy era is endless as businesses and governments become more aware of the opportunities that big data presents.

How machine learning works can be better explained by an illustration in the financial world. Traditionally, investment players in the securities market like financial researchers, analysts, asset managers, individual investors scour through a lot of information from different companies around the world to make profitable investment decisions. However, some pertinent information may not be widely publicized by the media and may be privy to only a select few who have the advantage of being employees of the company or residents of the country where the information stems from. In addition, theres only so much information humans can collect and process within a given time frame. This is where machine learning comes in.

An asset management firm may employ machine learning in its investment analysis and research area. Say the asset manager only invests in mining stocks. The model built into the system scans the web and collects all types of news events from businesses, industries, cities, and countries, and this information gathered makes up the data set. The asset managers and researchers of the firm would not have been able to get the information in the data set using their human powers and intellects. The parameters built alongside the model extracts only data about mining companies, regulatory policies on the exploration sector, and political events in select countries from the data set. Saya mining company XYZ just discovered a diamond mine in a small town in South Africa, the machine learning app would highlight this as relevant data. The model could then use an analytics tool called predictive analytics to make predictions on whether the mining industry will be profitable for a time period, or which mining stocks are likely to increase in value at a certain time. This information is relayed to the asset manager to analyze and make a decision for his portfolio. The asset manager may make a decision to invest millions of dollars into XYZ stock.

In the wake of an unfavorable event, such as South African miners going on strike, the computer algorithm adjusts its parameters automatically to create a new pattern. This way, the computational model built into the machine stays current even with changes in world events and without needing a human to tweak its code to reflect the changes. Because the asset manager received this new data on time, they are able to limit his losses by exiting the stock.

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Machine Learning Definition

When deciding on a machine learning conference, why go to Machine Learning Week 2020? This five-conference event May 31 June 4, 2020 at Caesars Palace, Las Vegas delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques. In this video, Predictive Analytics World Founder Eric Siegel spills on the details and lists five reasons this is the most valuable machine learning event to attend this year.

Note: This article is based on the transcript of a special episode of The Dr. Data Show click here to view.

In this article, I give five reasons that Machine Learning Week May 31 June 4, 2020 at Caesars Palace, Las Vegas is the most valuable machine learning event to attend this year. MLW is the largest annual five-conference blow-out part of the Predictive Analytics World conference series, of which I am the founder.

First, some background info. Your business needs machine learning to thrive and even just survive. You need it to compete, grow, improve, and optimize. Your team needs it, your boss demands it, and your career loves machine learning.

And so we bring you Predictive Analytics World, the leading cross-vendor conference series covering the commercial deployment of machine learning. By design, PAW is where to meet the whos who and keep up on the latest techniques.

This June in Vegas, Machine Learning Week brings together five different industry-focused events: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, and Deep Learning World. This is five simultaneous two-day conferences all happening alongside one another at Caesars Palace in Vegas. Plus, a diverse range of full-day training workshops, which take place in the days just before and after.

Machine Learning Week delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learning deployment, and the hottest topics and techniques.

This mega event covers all the bases for both senior-level expert practitioners as well as newcomers, project leaders, and executives. Depending on the topic, sessions and workshops are either demarcated as the Expert/practitioner level, or for All audiences. So, you can bring your team, your supervisor, and even the line-of-business managers you work with on model deployment. About 60-70% of attendees are on the hands-on practitioner side, but, as you know, successful machine learning deployment requires deep collaboration between both sides of the equation.

PAW and Deep Learning World also takes place in Germany, and Data Driven Government takes place in Washington DC but this article is about Machine Learning Week, so see predictiveanalyticsworld.com for details about the others.

Here are the five reasons to go.

Five Reasons to Go to Machine Learning Week June 2020 in Vegas

1) Brand-name case studies

Number one, youll access brand-name case studies. At PAW, youll hear directly from the horses mouth precisely how Fortune 500 analytics competitors and other companies of interest deploy machine learning and the kind of business results they achieve. More than most events, we pack the agenda as densely as possible with named case studies. Each day features a ton of leading in-house expert practitioners who get things done in the trenches at these enterprises and come to PAW to spill on the inside scoop. In addition, a smaller portion of the program features rock star consultants, who often present on work theyve done for one of their notable clients.

2) Cross-industry coverage

Number two, youll benefit from cross-industry coverage. As I mentioned, Machine Learning Week features these five industry-focused events. This amounts to a total of eight parallel tracks of sessions.

Bringing these all together at once fosters unique cross-industry sharing, and achieves a certain critical mass in expertise about methods that apply across industries. If your work spans industries, Machine Learning Week is one-stop shopping. Not to mention that convening the key industry figures across sectors greatly expands the networking potential.

The first of these, PAW Business, itself covers a great expanse of business application areas across many industries. Marketing and sales applications, of course. And many other applications in retail, telecommunications, e-commerce, non-profits, etc., etc.

The track topics of PAW Business 2020

PAW Business is a three-track event with track topics that include: analytics operationalization & management i.e., the business side core machine learning methods and advanced algorithms i.e., the technical side innovative business applications covered as case studies, and a lot more.

PAW Financial covers machine learning applications in banking including credit scoring insurance applications, fraud detection, algorithmic trading, innovative approaches to risk management, and more.

PAW Industry 4.0 and PAW Healthcare are also entire universes unto themselves. You can check out the details about all four of these PAWs at predictiveanalyticsworld.com.

And the newer sister event Deep Learning World has its own website, deeplearningworld.com. Deep learning is the hottest advanced form of machine learning with astonishing, proven value for large-signal input problems, such as image classification for self-driving cars, medical image processing, and speech recognition. These are fairly distinct domains, so Deep Learning World does well to complement the four Predictive Analytics World events.

3) Pure-play machine learning content

Number three, youll get pure-play machine learning content. PAWs agenda is not watered down with much coverage of other kinds of big data work. Instead, its ruthlessly focused specifically on the commercial application of machine learning also known as predictive analytics. The conference doesnt cover data science as a whole, which is a much broader and less well-defined area, that, for example, can include standard business intelligence reporting and such. And we dont cover AI per se. Artificial intelligence is at best a synonym for machine learning that tends to over-hype, or at worst an outright lie that promises mythological capabilities.

4) Hot new machine learning practices

Number four, youll learn the latest and greatest, the hottest new machine learning practices. Now, we launched PAW over a decade ago, so far delivering value to over 14,000 attendees across more than 60 events. To this day, PAW remains the leading commercial event because we keep up with the most valuable trends.

For example, Deep Learning World, which launched more recently in 2018 covers deep learnings commercial deployment across industry sectors. This relatively new form of neural networks has blossomed, both in buzz and in actual value. As I mentioned, it scales machine learning to process, for example, complex image data.

And what had been PAW Manufacturing for some years has now changed its name to PAW Industry 4.0. As such, the event now covers a broader area of inter-related work applying machine learning for smart manufacturing, the Internet of Things (IoT), predictive maintenance, logistics, fault prediction, and more.

In general, machine learning continues to widen its adoption and apply in new, innovative ways across sectors in marketing, financial risk, fraud detection, workforce optimization, and healthcare. PAW keeps up with these trends and covers todays best practices and the latest advanced modeling methods.

5) Vendor-neutral content

And finally, number five, youll access vendor-neutral content. PAW isnt run by an analytics vendor and the speakers arent trying to sell you on anything but good ideas. PAW speakers understand that vendor-neutral means those in attendance must be able to implement the practices covered and benefit from the insights delivered without buying any particular analytics product.

During the event, some vendors are permitted to deliver short presentations during a limited minority of demarcated sponsored sessions. These sessions often are also substantive and of great interest. In fact, you can access all the sponsors and tap into their expertise at will in the exhibit hall, where theyre set up for just that purpose.

By the way, if youre an analytics vendor yourself, check out PAWs various sponsorship opportunities. Our events bring together a great crowd of practitioners and decision makers.

Summary Five Reasons to Go

1) Brand-name case studies

2) Cross-industry coverage

3) Pure-play machine learning content

4) Hot new machine learning practices

5) Vendor-neutral content

and those are the reasons to come to Machine Learning Week: brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques.

Machine Learning Week not only delivers unique knowledge-gaining opportunities, its also a universal meeting place the industrys premier networking event. It brings together the whos who of machine learning and predictive analytics, the greatest diversity of expert speakers, perspectives, experience, viewpoints, and case studies.

This all turns the normal conference stuff into a much richer experience, including the keynotes, expert panels, and workshop days, as well as opportunities to network and talk shop during the lunches, coffee breaks, and reception.

I encourage you to check out the detailed agenda see all the speakers, case studies, and advanced methods covered. Each of the five conferences has its own agenda webpage, or you can also view the entire five-conference, eight-track mega-agenda at once. This view pertains if youre considering registering for the full Machine Learning Week pass, or if youll be attending along with other team members in order to divide and conquer.

Visit our website to see all these details, register, and sign up for informative event updates by email.

Or to learn more about the field in general, check out our Predictive Analytics Guide, our publication The Machine Learning Times, which includes revealing PAW speaker interviews, and, episodes of this show, The Dr. Data Show which, by the way, is generally about the field of machine learning in general, rather than about our PAW events.

This article is based on a transcript from The Dr. Data Show.

CLICK HERE TO VIEW THE FULL EPISODE

About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics. Click here to view more episodes and to sign up for future episodes of The Dr. Data Show.

About the Author

Eric Siegel, Ph.D., founder of the Predictive Analytics Worldand Deep Learning World conference series and executive editor ofThe Machine Learning Times, makes the how and why of predictive analytics (aka machine learning) understandable and captivating. He is the author of the award-winning bookPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, the host of The Dr. Data Show web series, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. Follow him at @predictanalytic.

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Five Reasons to Go to Machine Learning Week 2020 - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

What IT team wouldnt like to have a crystal ball that could predict the IT future, letting them fix application and infrastructure performance problems before they arise? Well, the current shortage ofcrystal balls makes the union of artificial intelligence (AI), machine learning (ML), and utilisation forecasting the next best thing for anticipating and avoiding issues that threaten the overall health and performance of all IT infrastructure components. The significance of AI has not been lost to organisations in the United Kingdom, with 43 per cent of them believing that AI will play a big role in their operations.

Utilisation forecasting is a technique that applies machine learning algorithms to produce daily usage forecasts for all utilisation volumes across CPUs, physical and virtual servers, disks, storage, bandwidth, and other network elements, enabling networking teams to manage resources proactively. This technique helps IT engineers and network admins prevent downtime caused by over-utilisation.

The AI/ML driving forecasting solution produces intelligent and reliable reports by taking advantage of the current availability of ample historic records and high-performance computing algorithms. Without AI/ML, utilisation forecasting relies on reactive monitoring. You set predefined thresholds for given metrics such as uptime, resource utilisation, network bandwidth, and hardware metrics like fan speed and device temperature. When a threshold is exceeded, an alert is issued. However, that reactive approach will not detect the anomalies that happen below that threshold and create other, indirect issues. Moreover, it will not tell you when you will need to upgrade your infrastructure based on current trends.

To forecast utilisation proactively, you need accurate algorithms that can analyze usage patterns and to detect anomalieswithout false positivesin daily usage trends. Thats how you predict usage in the future. Let us take a look at a simple use case.

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With proactive, AI/ML-driven utilisation forecasting, you can find a minor increase in your officebandwidth usage during the World Series, the FIFA World Cup, and other sporting events. Thatanomalous usage can be detected even if you have a huge amount of unused internet bandwidth. Similarly, proactive utilisation forecasting lets you know when to upgrade your infrastructure based on new recruitment and attrition rates.

A closer look at the predictive technologies reveals the fundamental difference between proactive and reactive forecasting. Without AI and ML, utilisation forecasting uses linear regression models to extrapolate and provide prediction based on existing data. This method involves no consideration of newly allocated memory or anomalies in utilisation patterns. Also, pattern recognition is a foreign concept. Although useful, linear regression models do not give IT admins complete visibility.

AI/ML-driven utilisation forecasting, on the other hand, uses the Seasonal and Trend decomposition using Loess (STL) method. STL lets you study the propagation and degradation of memory as well as analyze pattern matching whereby periodic changes in the metric configuration will be automatically adjusted. Bottom line, STL dramatically improves accuracy thanks to those dynamic, automated adjustments. And if any new memory is allocated, or if memory size is increased or decreased for the device, the prediction will change accordingly. This option was not possible with linear regression.

Beyond forecasting, ML can be used to improve anomaly detection. Here, adaptive thresholds for different metrics are established using ML and analysis of historic data will reveal any anomalies and trigger appropriate alerts. Other application and infrastructure monitoring functions will also be improved when enhanced with AI and ML technologies. Sometime in the not-too-distant future, AI/ML-driven forecasting and monitoring will rival the predictive powers of the fabled crystal ball.

by Rebecca D'Souza, Product Consultant, ManageEngine

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Optimising Utilisation Forecasting with AI and Machine Learning - Gigabit Magazine - Technology News, Magazine and Website