Category Archives: Machine Learning
Machine Learning Engineers Are In High Demand. So, What Do They Do? – Analytics Insight
Machine Learning Engineers Are In High Demand. So, What Do They Do?
With every organization digitizing its operations and taking advantage of data science tools, artificial intelligence, machine learning, the demand for professionals in their domain is always high. With machine learning being an important aspect of all automation tools, machine learning engineers are in the highest demand.
According to Brandon Purell, Senior Analyst at Forrester Research, one hundred percent of any companys future success depends on adopting machine learning. For companies to be successful in the age of the customer, they need to anticipate what customers want, and machine learning is absolutely essential for that.
Lets understand why the demand for a machine learning engineer is more than ever.
Machine learning engineers are a combination of two vital roles in the industry, data scientist and software engineer. While the main focus of a data scientist is to work with big data, a software engineer does the coding of a program. The job of a data scientist is analytical where they use a combination of mathematical, statistical, analytical skills, and machine learning tools to process and analyze massive pools of data for business insights. Whereas, software engineers are experts in writing scalable codes for programs and design complex software systems for companies. Their roles dont require working with machine learning tools.
The applications created by data scientists are difficult for software engineers to understand as they are complex and have no design pattern. This is why companies are looking to hire machine learning engineers who can put both the skills to work. A good machine learning engineer in this day and age should be to understand the data scientists code and make it more accessible.
A machine learning engineers work is similar to a data scientists role, both work with huge datasets. Hence, a machine learning engineer should have excellent data management skills. Their job roles require them to combine the rules of data science with programming to help companies leverage their business with AI and machine learning technologies.
Machine learning engineers work closely with data scientists. While data scientists extract meaningful insights from several GBs of datasets and communicate the insights to stakeholders. Machine learning scientists make sure that the models used by data scientists can analyze large amounts of data in real-time for getting accurate results. When these disciplines work together, they create technologies for companies that were once considered impractical and impossible. Machine learning engineers are paving the future of the tech world by enabling several industries to leverage disruptive technologies.
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Machine Learning Engineers Are In High Demand. So, What Do They Do? - Analytics Insight
Updated Report of Machine Learning Software Market with Current Trends, Drivers, Strategies, Applications and Competitive Landscape 2026 The Bisouv…
The Latest Machine Learning Software Market report offers an extensive analysis of key growth strategies, drivers, opportunities, key segments, Porters Five Forces analysis, and competitive landscape. This study is a helpful source of information for market players, investors, VPs, stakeholders, and new entrants to gain a thorough understanding of the industry and determine steps to be taken to gain a competitive advantage.
This report includes an in-depth analysis of the global Machine Learning Software market for the present as well as forecast period. The report encompasses the competition landscape entailing share analysis of the key players in the Machine Learning Software market based on their revenues and other significant factors. Further, it covers the several developments made by the prominent players of the Machine Learning Software market.
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Top Players in Machine Learning Software Market are
The report makes use of the market data sourced from the year 2015 to 2020 while the market analysis aims to forecast the market up to the year 2026. The various strategic developments have been studied to present the current market scenario.
Machine Learning Software Market Segmentation
The segment outlook section of the report is a highly decisive information hub to unravel segment potential in directing impressive growth and steady CAGR valuation. Additional details on SWOT analysis of each of the mentioned market participant is poised to accelerate growth tendencies besides reviewing the growth scope through 2020-2026.
Machine Learning Software Market by Type
Machine Learning Software Market, By Application
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By Regions:The report offers an accurate representation of the geographical scope of the Global Machine Learning Software Market, inclusive of graphical details of popular growth hotspots and performance of the various products and services aligning with end-user preferences and priorities.
Crucial data enclosed in the report:
Key Parameters of Machine Learning Software Market:
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Updated Report of Machine Learning Software Market with Current Trends, Drivers, Strategies, Applications and Competitive Landscape 2026 The Bisouv...
The Machine Learning and Big Data in Risk Evaluation PhD Scholarship – News – The University of Sydney
1. Background
a. This Scholarship has been established to provide financial assistance to a PhD student to undertake research funded by an ARC Grant headed by Buhui Qiu within the University of Sydney Business School.
b. The project will develop an innovative machine-learning-based approach for measuring, monitoring and evaluating bank lending activities and risk disclosures to take advantage of the big data available. It will use multidimensional data to produce more relevant metrics for assessing bank risks and risk disclosure quality and apply them in regulatory policy evaluation. The project findings will significantly advance the knowledge on mitigating banking misconduct. They will also equip regulatory authorities with an efficient monitoring tool and an early-warning device to promote better lending and risk disclosure practices, and foster a more transparent and stable financial system to support financial intermediation in Australia and worldwide.
a. The Scholarship is offered subject to the applicant having an unconditional offer of admission to study full-time in a PhD within the University of Sydney Business School at the University of Sydney.
b. Applicants must be willing to conduct research into the ARC project mentioned above.
c. Applicants must have Dr Buhui Qiu and Dr Eliza Wu on their supervisory team within University of Sydney Business School.
d. Applicants must hold an Honours degree (First Class or Second Class upper) or a Master's degree in a related field with a substantial research component.
a. The successful applicant will be awarded the Scholarship on the basis of:
I. academic merit,
II. curriculum vitae,
III. a preliminary research proposal which demonstrates an understanding of, and interest in, the area of research
b. The successful applicant will be awarded the Scholarship by the nomination of a selection committee consisting of the Buhui Qiu, an academic supervisor, and the Director of Doctoral Studies in the University of Sydney Business School.
a. The Scholarship will provide a stipend allowance of $35,000 per annum (fixed rate) for three years subject to satisfactory academic performance. An extension of 6 months is possible upon approval from Associate Dean Research.
b. The Scholarship will provide academic course fee for 12 research periods for a PhD student, subject to satisfactory academic performance. An extension of 2 research periods is possible upon approval from Associate Dean Research.
c. The Scholarship is for commencement in relevant research period in which it is offered and cannot be deferred or transferred to another area of research.
d. No other amount is payable.
e. The Scholarship is subject to availability of funding.
a. Progression is subject to passing the annual progress evaluation, maintaining satisfactory progress in coursework and completing school research milestones.
a. Holders of the Scholarship receive up to 20 working days recreation leave every 12 months of the Scholarship and this may be accrued. Any unused leave when the Scholarship is terminated or completed will be forfeited. Recreation leave does not attract a leave loading. The supervisor's agreement must be obtained before leave is taken.
b. Holders of the Scholarship may take up to 10 working days sick leave every 12 months of the Scholarship and this may be accrued over the tenure of the Scholarship. Students with carer responsibility may convert up to five days of their annual sick leave entitlement to carers leave on presentation of medical certificate/s. Students taking sick leave must inform their supervisor as soon as practicable.
c. Holders of the Scholarship may receive additional paid sick leave of up to a total of twelve weeks during their scholarship for medically substantiated periods of illness where the student has insufficient sick leave entitlements available under Clause 6b above. Students applying for additional paid sick leave must do so at the start of absence or as soon as practicable. Periods of additional paid sick leave are added to the duration of the Scholarship.
d. Once female holders of the Scholarship have completed twelve months of their award, they are entitled to a maximum of twelve weeks paid maternity leave during the tenure of the Scholarship if she is the person who gives birth. Students applying for paid maternity leave should do so at least four weeks prior to the expected date of confinement. Periods of paid maternity leave are added to the duration of the Scholarship. Holders of the Scholarship who have not completed twelve months of their award may access unpaid maternity leave through the suspension provisions.
e. For non-birth giving scholarship holders, upon completed twelve months of their award, they are entitled to a maximum of 5 days paid paternity leave. Periods of paid paternity leave are added to the duration of the Scholarship. Holders of the Scholarship who have not completed twelve months of their award may access unpaid maternity leave through the suspension provisions.
a. The Scholarship recipient may not normally conduct research overseas within the first six months of award.
b. The Scholarship holder may conduct up to 12 months of their research outside Australia. Approval must be sought from the student's supervisor, Head of School and the Faculty via application to the Higher Degree by Research Administration Centre (HDRAC), and will only be granted if the research is essential for completion of the degree. All periods of overseas research are cumulative and will be counted towards a student's candidature. Students must remain enrolled full-time at the University and receive approval to count time away.
a. Holders of the Scholarship cannot suspend their award within the first six months.
b. Subject to relevant VISA conditions, holders of the Scholarship may apply for up to 4 research periods suspension for any reason during the tenure of their award. Periods of suspension are cumulative and failure to resume study after suspension will result in the award being terminated. Approval to suspend must be given by the Head of the Department/School concerned. Periods of study towards the degree during suspension of the Scholarship will be deducted from the maximum tenure of the Scholarship.
c. Subject to relevant VISA conditions, female holders of the Scholarship are entitled to up to an additional 4 research periods suspension (less any period of paid maternity leave) following each birth. The Scholarship holder should apply for the suspension within four weeks of the expected date of confinement.
d. Whenever an international student suspends his/her studies the university must report the suspension to immigration authorities and the student may be obliged to return to their home country for the duration of the suspension. Therefore, if an international student wishes to remain in Australia during a period of suspension of studies, they must contact the closest Australian immigration office within 28 days of the approval notice to seek approval to remain in Australia during the suspension.
a. The Scholarship recipient must notify HDRAC, and their supervisor promptly of any planned changes to their enrolment including but not limited to: attendance pattern, suspension, leave of absence, withdrawal, course transfer, and candidature upgrade or downgrade. If the award holder does not provide notice of the changes identified above, the University may require repayment of any overpaid stipend.
a. The Scholarship will terminate:
I. on resignation or withdrawal of the student from their research degree,
II. upon submission of the thesis or at the end of the award,
III. if the student ceases to be a full-time student and prior approval has not been obtained to hold the Scholarship on a part-time basis,
IV. upon the student having completed the maximum candidature for their degree as per the University of Sydney (Higher Degree by Research) Rule 2011 Policy,
V. if the recipient receives an alternative primary stipend scholarship. In such circumstances this Scholarship will be terminated in favour of the alternative stipend scholarship where it is of higher value,
VI. does not resume study at the end of a period of approved leave, or
VII. If the student ceases to meet the eligibility requirements specified for this Scholarship, (other than during a period in which the Scholarship has been suspended or during a period of approved leave).
b. The Scholarship may also be terminated by the University before this time if, in the opinion of the University:
I. the course of study is not being carried out with competence and diligence or in accordance with the terms of this offer,
II. the student fails to maintain satisfactory progress, or
III. the student has committed misconduct or other inappropriate conduct.
c. The Scholarship will be suspended throughout the duration of any enquiry/appeal process.
d. Once the Scholarship has been terminated, it will not be reinstated unless due to University error.
a. Where during the Scholarship a student engages in misconduct, or other inappropriate conduct (either during the Scholarship or in connection with the students application and eligibility for the Scholarship), which in the opinion of the University warrants recovery of funds provided, the University may require the student to repay payments made in connection with the Scholarship. Examples of such conduct include and without limitation; academic dishonesty, research misconduct within the meaning of the Research Code of Conduct (for example, plagiarism in proposing, carrying out or reporting the results of research, or failure to declare or manage a serious conflict of interests), breach of the Code of Conduct for Students and misrepresentation in the application materials or other documentation associated with the Scholarship.
b. The University may require such repayment at any time during or after the Scholarship period. In addition, by accepting this Scholarship, the student consents to all aspects of any investigation into misconduct in connection with this Scholarship being disclosed by the University to the funding body and/or any relevant professional body.
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The Machine Learning and Big Data in Risk Evaluation PhD Scholarship - News - The University of Sydney
Global Machine Learning Market Industry Analysis and Forecast (2019-2026) by Component, Service, Deployment Model, Organization Size, Vertical, and…
Maximize Market Research has recently published a Global Machine Learning Market 2019 Industry Research Report. It is comprehensive analysis of past and current status Machine Learning Marketwith the forecast till 2027. The report covers the past market from 2017 to 2019 and forecast of 2020 to 2027 with key developments, key trends, M&A activities by value and their strategic intents. The report has analysed complex data and presented in simple format to make it easlier to understand.The Market structure presented in the report gives detailed analysis of market leaders, followers and new entrants by region. Glance at couple of slides will give an idea about the market structure with the market share commanded by leaders, followers and unconsolidated/local but important players.
Request For View Sample Machine Learning Market Report Page : https://www.maximizemarketresearch.com/request-sample/23945
Global Machine Learning Market by Component:
Software ServicesGlobal Machine Learning Market by Service:
Professional Services Managed ServicesGlobal Machine Learning Market by Deployment Model:
Cloud On-premisesGlobal Machine Learning Market by Organization Size:
SMEs Large EnterprisesGlobal Machine Learning Market by Vertical:
BFSI Healthcare and Life Sciences Retail Telecommunication Government and Defense Manufacturing Energy and Utilities OthersMachine Learning Market by Region:
North America Asia-Pacific Europe Latin America Middle East & AfricaKey Players Operated in Market Include:
Amazon Apple Ayasdi Digital Reasoning Darktrace Dataiku Facebook Feedzai Google IBM Watson Luminoso N-iX QBurst Qualcomm Skytree Uber
PORTER, SVOR, PESTEL analysis by region is covered for the companies or individual investors who are looking at specific market for expansion or entry. Micro as well as Macro economic factors are analysed to understand its impact on market growth and key players top lines.The report also helps in comprehending the Machine Learning dynamics, structure by analysing the industry segments and project the Machine Learning Market size. To stand apart, the clear representation of competitive analysis of key market players by product, price, financial position, product portfolio, growth strategies, and regional presence in the Machine Learning Market make the report investors guide.Additionally, the Machine Learning market report 2019 2027, gives the competitive landscape of the global industry by region, key players products and services benchmarking, market domination by segment, pricing and end user penetration, investment in R&D and patents. Couple of slides will give the complete competitive landscape of the industry.
SWOT analysis of player will give a detailed strategic input about the key players in industry by region. Experienced research analysts in the field are following the key players that are profiled in the Machine Learning report that are considered while estimating the market size. Research analyst working in the field for more than ten years, give them advantage to follow same market for years and have become seasoned consultants in the Machine Learning industry.
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Table of Contents
Browse Complete Machine Learning Report details with ToC and List Of Figures Here: https://www.maximizemarketresearch.com/market-report/global-machine-learning-market/23945/
About Us:Maximize Market Research has served esteemed clients including Yamaha, Boeing, Sensata, Etnyre, Canada, ALCOR M&A, Microsoft, Harman, and other 200 MNCs worldwide. The Company provides B2B and B2C market research on 5000 high growth emerging technologies & opportunities in Transportation, Chemical, Healthcare, Pharmaceuticals, Electronics & Communications, Internet of Things, Food and Beverages, Aerospace and Defense and other manufacturing sectors.We, at Maximize Market Research, are a strong unified team of industry specialists and analysts across sectors to ensure entire Industry ecosystem is taken in perspective, factoring all recent development, latest trends and futuristic the technological impact of uniquely specific industries. In line with the agreed scope and objective of the study, our approach is uniquely custom detailed
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Ontario Systems Acquires Pairity to Embed the Power of Machine Learning in Its Industry-Leading Collections Technology – PRNewswire
MUNCIE, Ind., Feb. 23, 2021 /PRNewswire/ --Ontario Systems, a leading provider of enterprise software that automates complex workflows, accelerates revenue recovery and simplifies the payment process for healthcare, accounts receivable management (ARM) and government clients, today announced its acquisition of Pairity, a cutting-edge provider of artificial intelligence (AI) and machine-learning capabilities that allow collection teams to maximize revenue recovery by uncovering new data insights at scale.
Pairity's advanced AI technology extracts and surfaces actionable patterns, allowing collectors to continually adapt their contact strategies. With this integrated functionality, Ontario Systems' collection platforms will help clients significantly enhance productivity and collection results.
"Pairity shares Ontario's commitment to creating intelligent workflow solutions that streamline the collections process and accelerate payments," said Ontario Systems CEO Tim O'Brien. "Pairity's technology strengthens our ability to drive value for our clients and provides another foundation from which we can continue to innovate."
Recognized as the most innovative product at the 2019 CollectTech conference, Pairity allows users to continuously identify accounts with the highest probability of successful collection.Collectors in turn require fewer phone calls to realize value, increasing efficiency and revenue.
"We greatly look forward to joining Ontario," said Greg Allen, CEO of Pairity. "Their proven track record of success in delivering enterprise workflow, collection, and payment solutions is the perfect platform on which to expand the reach of Pairity's innovative approach to collections."
This acquisition follows Ontario Systems' acquisition of SwervePay in May 2020 as part of Ontario Systems' growth and SaaS-transformation strategies designed to deliver faster innovation and increasing business value to thousands of clients nationwide.
About Ontario Systems
Ontario Systems is a premier provider of enterprise technologies that streamline and accelerate revenue recovery for clients in the healthcare, government, and accounts receivable management (ARM) markets. Through process automation and modern communication and payment tools, Ontario Systems helps its clients generate more revenue at reduced cost and engage patients, constituents, and consumers compliantly and effectively.
With offices in Indiana, Massachusetts, New Mexico, and Washington state and employees across the country, Ontario Systems helps 600+ hospital networksincluding 5 of the 15 largest systems in the U.S.optimize cash collections and provide a seamless patient financial experience. Ontario Systems also serves 8 of the 10 largest ARM companies in addition to state and municipal governments nationwide.
About Pairity
Founded on the belief that advanced technology could more effectively address consumer debt for all stakeholders, Pairity offers leading artificial intelligence and machine-learning solutions that assist 40+ companies to manage over $40B of debt more effectively. Pairity's solutions shed light into their over 10 million unique consumers by learning, organizing, and scoring behavior that drives workflow strategy more efficiently. Pairity reduces friction in the collections process by harnessing their intelligence to boost productivity and revenue generating activities.
To learn more about Ontario Systems, visit http://www.ontariosystems.com
PRESS CONTACTDaniel Ward Vice President, Marketing 765-751-7469 [emailprotected]
SOURCE Ontario Systems
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Ontario Systems Acquires Pairity to Embed the Power of Machine Learning in Its Industry-Leading Collections Technology - PRNewswire
Is machine learning about to replace traditional coaching? TrainerRoad announces Adaptive Training – BikeRadar.com
TrainerRoad has today announced a potentially ground-breaking new feature called Adaptive Training, which will be available to all TrainerRoad subscribers.
Adaptive Training is said to use machine learning, science-based coaching principles and an unprecedented data set to individualise every TrainerRoad workout and training plan to the needs of each athlete.
Previously, TrainerRoad training plans were devised by professional coaches, but individual athletes were then left to their own devices to complete each session as prescribed, unless they employed a professional coach to monitor and optimise their training.
TrainerRoad, an online training ecosystem, says scheduling challenges and training interruptions are a fact of life and that the Adaptive Training function will monitor your performance in every workout you complete, as well as missed sessions or planned time off.
This will help to determine your Progression Levels (i.e. how well your fitness is improving in various aspects) and adapt your future training sessions to keep making improvements.
The new tool tracks performance in previous workouts, as well as any missed sessions, to determine your current Progression Levels. TrainerRoad
TrainerRoad will then use that info to adapt and personalise future workouts, similar to how a real coach might. TrainerRoad
The platform also says another new feature called TrainNow will be able to help athletes who dont follow a specific training plan. By using Progression Levels calculated from recently completed workouts, and using Adaptive Training insights, TrainerRoad will suggest a suitable structured workout to do next.
Every workout is intelligently chosen to be as effective as possible, and to address your unique needs and goals as an athlete, says TrainerRoad CEO Nate Pearson. Adaptive Training combines the science-based coaching principles TrainerRoad is built on, with a data set gleaned from millions of completed workouts. We truly believe this is the future of cycling training.
Online training platforms like TrainerRoad (and its competitors, Zwift, The Sufferfest, and other indoor cycling apps) have offered stock training plans for athletes to follow, at relatively affordable prices, for some time now. However, the traditional achilles heel of these is usually the lack of personalisation.
The wrong training load, or a mis-match between the riders goals and workouts performed, can easily lead to sub-optimal results.
Every athlete is different and following a stock training plan doesnt work for everyone. TrainerRoads new Adaptive Training tool is designed to solve this problem. TrainerRoad
In contrast, when you sign up with a real life cycling coach, they will help optimise your training load, fitness progression and training specificity, as well as adjust your future workouts to help you manage the rest of lifes challenges.
The downside is that a human coach can often be relatively expensive because this personal touch requires a lot of expertise and time investment.
TrainerRoads new Adaptive Training tool seems to suggest it can provide the best of both worlds, combining the accessibility of online training plans with the adaptability and personalised nature of a real-life coach.
Exactly how well it works in practice remains to be seen, of course, but its undoubtedly an exciting prospect.
The Adaptive Training feature is currently in closed beta, and TrainerRoad has not confirmed a precise release date. It does say, however, that individual aspects of the tool will begin rolling out soon, starting with TrainNow and data-driven updates to existing TrainerRoad training plans.
For those who cant wait, TrainerRoad says subscribers keen to get early access to the new tools can request priority access via TrainerRoad.com.
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Is machine learning about to replace traditional coaching? TrainerRoad announces Adaptive Training - BikeRadar.com
Mental health diagnoses and the role of machine learning – Health Europa
It is common for patients with psychosis or depression to experience symptoms of both conditions which has meant that traditionally, mental health diagnoses have been given for a primary illness with secondary symptoms of the other.
Making an accurate diagnosis often poses difficulties to mental health clinicians and diagnoses often do not accurately reflect the complexity of individual experience or neurobiology. For example, a patient being diagnosed with psychosis will often have depression regarded as a secondary condition, with more focus on the psychosis symptoms, such as hallucinations or delusions; this has implications on treatment decisions for patients.
A team at the University of Birminghams Institute for Mental Health and Centre for Human Brain Health, along with researchers at the European Union-funded PRONIA consortium, explored the possibility of using machine learning to create extremely accurate models of pure forms of both illnesses and using these models to investigate the diagnostic accuracy of a cohort of patients with mixed symptoms. The results of this study have been published in Schizophrenia Bulletin.
Paris Alexandros Lalousis, lead author, explains that the majority of patients have co-morbidities, so people with psychosis also have depressive symptoms and vice versa That presents a big challenge for clinicians in terms of diagnosing and then delivering treatments that are designed for patients without co-morbidity. Its not that patients are misdiagnosed, but the current diagnostic categories we have do not accurately reflect the clinical and neurobiological reality.
The researchers analysed questionnaire responses and detailed clinical interviews, as well as data from structural magnetic resonance imaging from a cohort of 300 patients taking part in the study. From this group of patients, they identified small subgroups of patients, who could be classified as suffering either from psychosis without any symptoms of depression, or from depression without any psychotic symptoms.
With the goal of developing a precise disease profile for each patient and testing it against their diagnosis to see how accurate it was, the research team was able to identify machine learning models of pure depression, and pure psychosis by using the collected data. They were then able to use machine learning methods to apply these models to patients with symptoms of both illnesses.
The team discovered that patients with depression as a primary illness were more likely to have accurate mental health diagnoses, whereas patients with psychosis with depression had symptoms which most frequently leaned towards the depression dimension. This may suggest that depression plays a greater part in the illness than had previously been thought.
Lalousis added: There is a pressing need for better treatments for psychosis and depression, conditions which constitute a major mental health challenge worldwide. Our study highlights the need for clinicians to understand better the complex neurobiology of these conditions, and the role of co-morbid symptoms; in particular considering carefully the role that depression is playing in the illness.
In this study we have shown how using sophisticated machine learning algorithms, which take into account clinical, neurocognitive, and neurobiological factors can aid our understanding of the complexity of mental illness. In the future, we think machine learning could become a critical tool for accurate diagnosis. We have a real opportunity to develop data-driven diagnostic methods this is an area in which mental health is keeping pace with physical health and its really important that we keep up that momentum.
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Mental health diagnoses and the role of machine learning - Health Europa
There Is No Silver Bullet Machine Learning Solution – Analytics India Magazine
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A recommendation engine is a class of machine learning algorithm that suggests products, services, information to users based on analysis of data. Robust recommendation systems are the key differentiator in the operations of big companies like Netflix, Amazon, and Byte Dance (TikTok parent) etc.
Alok Menthe, Data Scientist at Ericsson, gave an informative talk on building Custom recommendation engines for real-world problems at the Machine Learning Developers Summit (MLDS) 2021. Whenever a niche business problem comes in, it has complicated intertwined ways of working. Standard ML techniques may be inadequate and might not serve the customers purpose. That is where the need for a custom-made engine comes in. We were also faced with such a problem with our service network unit at Ericsson, he said.
Menthe said the unit wanted to implement a recommendation system to provide suggestions for assignment workflow a model to delegate the incoming projects to the most appropriate team or resource pool
Credit: Alok Menthe
There were three kinds of data available:
Pool definition data: It relates to the composition of a particular resource poolthe number of people, their competence, and other metadata.
Historical demand data: This kind of data helps in establishing a relationship between the feature demand and a particular resource pool.
Transactional data: It is used for operational purposes.
Menthe said building a custom recommendation system in this context involves the following steps:
Credit: Alok Menthe
After building our model, the most difficult part was feature engineering, which is imperative for building an efficient system. Among the two major modules classification and clusteringwe faced challenges with respect to the latter. We had only categorical information making it difficult to find distances within the objects. We went out of the box to see if we can do any special encoding for the data. We adopted data encoding techniques and frequency-based encoding in this regard, said Menthe.
Clustering module: For this module, initially the team implemented K-modes and agglomerative. However, the results were far from perfect, prompting the team to consider the good-old K-means algorithm. For evaluation purposes, it was done manually with the help of subject matter experts.
The final model had 700 resource pools condensed to 15 pool clusters.
Classification module: For this module, three kinds of algorithm iterations were usedRandom Forest, Artificial Neural Network, XGBoost. Classification accuracy was used as an evaluation metric. Finally, upon 50,00,000 training records, this module demonstrated an accuracy of 71 percent.
Menthe said this recommendation model is monitored on a fortnightly basis by validating the suggested pools against the allocated pools for project demands:
The model has proved to be successful on three fronts:
Menthe summarised the three major takeaways from this project in his concluding remarks: the need to preserve business nuances in ML solutions; thinking beyond standard ML approaches; and understanding that there is no silver bullet ML solution.
I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.
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There Is No Silver Bullet Machine Learning Solution - Analytics India Magazine
Scientists use machine learning to tackle a big challenge in gene therapy – STAT
As the world charges to vaccinate the population against the coronavirus, gene therapy developers are locked in a counterintuitive race. Instead of training the immune system to recognize and combat a virus, theyre trying to do the opposite: designing viruses the body has never seen, and cant fight back against.
Its OK, really: These are adeno-associated viruses, which are common and rarely cause symptoms. That makes them the perfect vehicle for gene therapies, which aim to treat hereditary conditions caused by a single faulty gene. But they introduce a unique challenge: Because these viruses already circulate widely, patients immune systems may recognize the engineered vectors and clobber them into submission before they can do their job.
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Scientists use machine learning to tackle a big challenge in gene therapy - STAT
Getting Help from HAL: Applying Machine Learning to Predict Antibiotic Resistance – Contagionlive.com
Highlighted Study: Lewin-Epstein O, Baruch S, Hadany L, Stein GY, Obolski U. Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records. Clin Infect Dis. Published online October 18, 2020. doi:10.1093/cid/ciaa1576
Appropriate empirical antimicrobial therapy is paramount for ensuring the best outcomes for patients. The literature shows that inappropriate antimicrobial therapy for infections caused by resistant pathogens leads to worse outcomes.1,2 Additionally, increased use of broad spectrum antibiotics in patients without resistant pathogens can lead to unintended consequences.3-5 As technology advances, it may enable clinicians to better prescribe empiric antimicrobials. Lewin-Epstein et al studied the potential for machine learning to optimize the use of empiric antibiotics in patients who may be harboring resistant bacteria.
As machine learning and artificial intelligence technology improves, investigators are examining new ways to implement it in practice. Lewin-Epstein et al studied the potential for machine learning to predict antibiotic resistance in hospitalized patients.6 This study specifically targeted the use of empiric antibiotics, attempting to reduce their use in patients who may be harboring resistant bacteria.
The single-center retrospective study was conducted in Israel from May 2013 through December 2015 using electronic medical records of patients who had positive bacterial culture results and resistance profiles for the antibiotics of interest. The investigators studied 5 antibiotics from commonly prescribed antibiotic classes: ceftazidime, gentamicin, imipenem, ofloxacin, and sulfamethoxazole-trimethoprim. The data set included 16,198 samples for patients who had positive bacterial culture results and sensitivities for these 5 antibiotics. The most common bacterial species were Escherichia coli, Klebsiella pneumoniae, coagulase negative Staphylococcus, and Pseudomonas aeruginosa. The investigators also collected patient demographics, comorbidities, hospitalization records, and information on previous inpatient antibiotic use.
Employing a supervised machine learning approach, they created a model comprising 3 submodels to predict antibiotic resistance. The first 85% of data were used to train the model, whereas the remainder were used to test it. During training, the investigators identified the variable with the highest effect on predictionthe rate of previous antibiotic-resistant infections, regardless of whether the bacterial species was included in the analysis. Other important variables included previous hospitalizations, nosocomial infections, previous antibiotic usage, and patient functioning and independence levels. The model was trained in multiple ways to identify which manner of use would be the most accurate. In one analysis, the model was trained and evaluated on each antibiotic individually. In another, it was trained and evaluated on all 5 antibiotics. The model was also evaluated when the bacterial species was included and excluded. The models success was defined by the area under the receiver-operating characteristic (auROC) curve and balanced accuracy, which is the unweighted average of the sensitivity and specificity rates.
The ensemble model, which was made up of the 3 submodels, was effective at predicting bacterial resistance, especially when the bacteria causing the infection were identified for the model. When the bacterial species was identified, the auROC score ranged from 0.8 to 0.88 versus 0.73 to 0.79 when the species was not identified. These results are more promising than previous studies on the use of machine learning in identifying resistant infections, despite this study incorporating heterogenous data and multiple antibiotics. Previous studies that only included 1 species or 1 type of infection yielded auROC scores of 0.7 to 0.83. This shows that using the composite result of multiple models may be more successful at predicting antibiotic resistance.
One limitation of this study is that it did not compare the model with providers abilities to recognize potentially resistant organisms and adjust therapy accordingly. Although this study did not directly make a comparison, a previous study involving machine learning showed that a similar model performed better than physicians when predicting resistance. The model in this study performed better than the one in the previous study, which suggests that this model may perform better than providers when predicting resistance. Another limitation of this study is that it did not evaluate causal effects of antibiotic resistance. The authors believe that further research should be conducted in this area to evaluate whether machine learning could be employed to determine further causes of antimicrobial resistance. A third limitation is that this study only evaluated the 5 antibiotics included, which are the 5 antibiotics most commonly tested for resistance at that facility. Additional research and machine learning would likely need to be incorporated to apply this model to other antibiotics.
The authors concluded that the model used in this study could be used as a template for other health systems. Because resistance patterns vary by region, this seems to be an appropriate conclusion. A model would have to be trained at each facility that was interested in employing machine learning in antimicrobial stewardship, and additional training would have to occur periodically to keep up with evolving resistance patterns. Additionally, if a facility would like to incorporate this type of model, they might want to also incorporate rapid polymerase chain reaction testing to provide the model with a bacterial species for optimal predictions. Overall, the results of this study indicate that great potential exists for machine learning in antimicrobial stewardship programs.
References
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Getting Help from HAL: Applying Machine Learning to Predict Antibiotic Resistance - Contagionlive.com