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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
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 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.
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.
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
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.
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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.
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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
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