Category Archives: Machine Learning
Machine Learning Answers: Facebook Stock Is Down 20% In A Month, What Are The Chances It’ll Rebound? – Trefis
Facebook stock (NASDAQ: FB) reached an all-time high of almost $305 less than a month ago before a larger sell-off in the technology industry drove the stock price down nearly 20% to its current level of around $250. But will the companys stock continue its downward trajectory over the coming weeks, or is a recovery in the stock imminent?
According to the Trefis Machine Learning Engine, which identifies trends in the companys stock price data since its IPO in May 2012, returns for Facebook stock average a little over 3% in the next one-month (21 trading days) period after experiencing a 20% drop over the previous month (21 trading days). Notably, though, the stock is very likely to underperform the S&P500 over the next month (21 trading days), with an expected excess return of -3% compared to the S&P500.
But how would these numbers change if you are interested in holding Facebook stock for a shorter or a longer time period? You can test the answer and many other combinations on the Trefis Machine Learning Engine to test Facebook stock chances of a rise after a fall. You can test the chance of recovery over different time intervals of a quarter, month, or even just 1 day!
Question 1: Is the average return for Facebook stock higher after a drop?
Consider two situations,
Case 1: Facebook stock drops by -5% or more in a week
Case 2: Facebook stock rises by 5% or more in a week
Is the average return for Facebook stock higher over the subsequent month after Case 1 or Case 2?
FB stock fares better after Case 2, with an average return of 2.4% over the next month (21 trading days) under Case 1 (where the stock has just suffered a 5% loss over the previous week), versus, an average return of 5.3% for Case 2.
In comparison, the S&P 500 has an average return of 3.1% over the next 21 trading days under Case 1, and an average return of just 0.5% for Case 2 as detailed in our dashboard that details the average return for the S&P 500 after a fall or rise.
Try the Trefis machine learning engine above to see for yourself how Facebook stock is likely to behave after any specific gain or loss over a period.
Question 2: Does patience pay?
If you buy and hold Facebook stock, the expectation is over time the near term fluctuations will cancel out, and the long-term positive trend will favor you at least if the company is otherwise strong.
Overall, according to data and Trefis machine learning engines calculations, patience absolutely pays for most stocks!
For FB stock, the returns over the next N days after a -5% change over the last 5 trading days is detailed in the table below, along with the returns for the S&P500:
Question 3: What about the average return after a rise if you wait for a while?
The average return after a rise is understandably lower than a fall as detailed in the previous question. Interestingly, though, if a stock has gained over the last few days, you would do better to avoid short-term bets for most stocks although FB stock appears to be an exception to this general observation.
FBs returns over the next N days after a 5% change over the last 5 trading days is detailed in the table below, along with the returns for the S&P500:
Its pretty powerful to test the trend for yourself for Facebook stock by changing the inputs in the charts above.
What if youre looking for a more balanced portfolio instead? Heres a high quality portfolio to beat the market, with over 100% return since 2016, versus 55% for the S&P 500. Comprised of companies with strong revenue growth, healthy profits, lots of cash, and low risk, it has outperformed the broader market year after year, consistently.
Machine Learning in Education Market Incredible Possibilities, Growth Analysis and Forecast To 2025 – The Daily Chronicle
Latest Research Report: Machine Learning in Education industry
Machine Learning in Education Market report is to provide accurate and strategic analysis of the Profile Projectors industry. The report closely examines each segment and its sub-segment futures before looking at the 360-degree view of the market mentioned above. Market forecasts will provide deep insight into industry parameters by accessing growth, consumption, upcoming market trends and various price fluctuations.
This has brought along several changes in This report also covers the impact of COVID-19 on the global market.
Machine Learning in Education Market competition by top manufacturers as follow: , IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning
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Global Machine Learning in Education Market research reports growth rates and market value based on market dynamics, growth factors. Complete knowledge is based on the latest innovations in the industry, opportunities and trends. In addition to SWOT analysis by key suppliers, the report contains a comprehensive market analysis and major players landscape.The Type Coverage in the Market are: Cloud-BasedOn-Premise
Market Segment by Applications, covers:Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers
Market segment by Regions/Countries, this report coversNorth AmericaEuropeChinaRest of Asia PacificCentral & South AmericaMiddle East & Africa
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Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk – Healthcare IT News
Since the earliest days of the COVID-19 pandemic, one of the biggest challenges for health systems has been to gain an understanding of the community spread of this virus and to determine how likely is it that a person walking through the doors of a facility is at a higher risk of being COVID-19 positive.
Without adequate access to testing data, health systems early-on were often forced to rely on individuals to answer questions such as whether they had traveled to certain high-risk regions. Even that unreliable method of assessing risk started becoming meaningless as local community spread took hold.
Parkland Health & Hospital System, the safety net health system for Dallas County, Texas, and PCCI, a Dallas-based non-profit with expertise in the practical applications of advanced data science and social determinants of health, had a better idea.
Community spread of an infectious disease is made possible through physical proximity and density of active carriers and non-infected individuals. Thus, to understand the risk of an individual contracting the disease (exposure risk), it was necessary to assess their proximity to confirmed COVID-19 cases based on their address and population density of those locations.
If an "exposure risk" index could be created, then Parkland could use it to minimize exposure for their patients and health workers and provide targeted educational outreach in highly vulnerable zip codes.
PCCIs data science and clinical team worked diligently in collaboration with the Parkland Informatics team to develop an innovative machine learning driven predictive model called Proximity Index. Proximity Index predicts for an individuals COVID-19 exposure risk, based on their proximity to test positive cases and the population density.
This model was put into action at Parkland through PCCIs cloud-based advanced analytics and machine learning platform called Isthmus. PCCIs machine learning engineering team generated geospatial analysis for the model and, with support from the Parkland IT team, integrated it with their electronic health record system.
Since April 22, Parklands population health team has utilized the Proximity Index for four key system-wide initiatives to triage more than 100,000 patient encounters and to assess needs, proactively:
In the future, PCCI is planning on offering Proximity Index to other organizations in the community schools, employers, etc., as well as to individuals to provide them with a data driven tool to help in decision making around reopening the economy and society in a safe, thoughtful manner.
Many teams across the Parkland family collaborated on this project, including the IT team led by Brett Moran, MD, Senior Vice President, Associate Chief Medical Officer and Chief Medical Information Officer at Parkland Health and Hospital System.
Global Machine Learning Market Tends To Show Steady Growth Post Pandemic With Regional Overview and Top Key Players – Verdant News
The research study on Machine Learning Market added byReportspediapresents an extensive analysis of current Machine Learning Market size, drivers, trends, opportunities, challenges, as well as key market segments. In continuation of this data, the Machine Learning Market report covers various marketing strategies followed by key players and distributors.
During the estimated period, the report also mentions the predictable CAGR of the global Machine Learning Market. The report provides readers with accurate past statistics and predictions of the future. In order to get an in-depth overview of Global Machine Learning Market is valued at USD XX million in 2020 and is predictable to reach USD XX million by the end of 2027, growing at a CAGR of XX% between 2020 and 2027.
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Top Key Players:
Luminoso Technologies, Inc.Hewlett Packard Enterprise Development LPSAS Institute Inc.RapidMiner, Inc.Angoss Software CorporationAmazon Web Services Inc.TIBCO Software Inc.DataikuBigML, Inc.Oracle CorporationFractal Analytics Inc.Fair Isaac CorporationDomino Data Lab, Inc.TrademarkVisionGoogle, Inc.Alpine DataTeradataIBM CorporationDell Inc.Baidu, Inc.Intel CorporationKNIME.com AGSAP SEMicrosoft Corporation
The report on Machine Learning market is also provided, details of the company enclosed, SWOT analysis, and PESTEL, Porters five forces, and product life cycle. In the start, the report offers a basic introduction of the Machine Learning industry containing its definition, applications and production technique. Then, the report illustrates the international key Machine Learning industry players in detail.
Geographical Analysis of Machine Learning Market:
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Machine Learning Market Segmentation:
Machine Learning Market Segmentation By Type:
Machine Learning Market Segmentation By Application:
BFSIHealthcare and Life SciencesRetailTelecommunicationGovernment and DefenseManufacturingEnergy and Utilities
Global Machine Learning Market: Competitive Analysis
This section of the report identifies a variety of key manufacturers in the market. It helps the reader know the strategies and collaboration that players are focus on combat competition in the market. The wide-ranging report provides a major microscopic look at the market. The reader can discover the footprints of the manufacturers by knowing about the global revenue of manufacturers and sales by manufacturers during the forecast period of 2020 to 2027.
In this Machine Learning market study, the following years are considered to project the market footprint:
History Year:2014 2018
Forecast Year:2020 2027
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Main points of the table of contents:
Chapter One: Report Overview
Chapter Two: Trends in Global Growth
Chapter Three: Market Share of Major Players
Chapter Four: Distribution by Type and Application
Chapter Five: United States
Chapter Six: Europe
Chapter Seven: China
Chapter Eight: Japan
Chapter Nine: Southeast Asia
Chapter Ten: India
Chapter Eleven: Central and South America
Chapter Twelve: Profiles of International Players
Chapter Thirteen: Market Forecast 2020-2027
Chapter Fourteen: Analyst Views / Findings
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Global Machine Learning Market Tends To Show Steady Growth Post Pandemic With Regional Overview and Top Key Players - Verdant News
A joint venture has seen the implementation of machine learning at HHLAs Container Terminal Burchardkai to optimise import container yard positioning and reduce re-handling moves.
The elimination of costly re-handling moves of import containers has recently been the focus of a joint project between container terminal operator HHLA, its affi liate Hamburg Port Consulting (HPC) and INFORM the Artificial Intelligence (AI) systems supplier. Machine learning sits at the heart of the system.
Dwell time is the unit of time used to measure the period in which a container remains in a container terminal with this typically running from its arrival off a vessel until leaving the terminal via truck, rail or another vessel.
For import containers there is often no specific information available on the pick-up time when selecting a storage slot in the container stack. This can lead to an inefficient container storage location in the yard generating, in turn, the requirement for additional shuffle moves that require extra resources including maintenance and energy consumption.
To mitigate this operational inefficiency, the project partners - HHLA, HPC and INFORM - have recently run a pilot project at HHLAs Container Terminal Burchardkai (CTB) focused on machine learning technology with this applied in order to predict individual import container dwell times and thereby reduce costly re-handling/shuffle moves.
As a specialist in IT software integration and terminal operations, HPC employed the deep learning approach to identify hidden patterns from historical data of container moves at HHLA CTB. This was undertaken over a period of two years and with the acquired information processed into high quality data sets. Assessed by the Syncrotess Machine Learning Module from INFORM and validated by the HPC simulation tool, the results show a significant reduction of shuffle moves resulting in a reduced truck turn time.
Dr. Alexis Pangalos, Partner at HPC discussing the project highlights notes: It was a productive implementation of INFORMs Artificial Intelligence (AI) solution for the choice of container storage positions at CTB. The Machine Learning (ML) Module was trained with data from CTBs container handling operations and the outcome from this is a system tailor-made for HHLAs operations.
HPC together with INFORM have integrated the Syncrotess ML Module into the slot allocation algorithms already running within CTBs terminal control system, ITS.
PREDICTING DWELL TIME
INFORMs AI solution predicts the dwell time (i.e., the time period the container is expected to be stored in the yard) and the outbound mode of transport (e.g., rail, truck, vessel) both of which are crucial criteria for selecting an optimised container storage location within the yard. A location that avoids unnecessary re-handling.
Utilising machine learning and AI and integrating these technologies into existing IT infrastructure are the success factors for reaching the next level of optimisations, says Jens Hansen, Executive Board Member responsible for IT at HHLA. A detailed analysis, and a smooth interconnectivity between all different systems, enable the value of improved safety while reducing costs and greenhouse gas emissions, he underlines.
DETAILED DOMAIN KNOWLEDGE
Data availability and data processing are key elements when it comes to utilising AI technology, says Pangalos. It requires a detailed domain knowledge of terminal operations to unlock greater productivity of the terminal equipment and connected processes.
The implementation is based on a machine learning assessment INFORM undertook in 2018 whereby it set out to determine if they could improve optimisation and operational outcomes using INFORMs broader ML algorithms developed for use in other industries such as finance and aviation.
As of 2019, system results indicated a prediction accuracy of 26% for dwell time predictions and 33% for outbound mode of transport predictions.
Dr. Eva Savelsberg, Senior Vice President of INFORMs Logistic Division notes: AI and machine learning allows us to leverage data from our past performance to inform us about how best to approach our future operations our ML Module gives our Operations Research based algorithms the best footing for making complex decisions about what to do in the future.
INFORMs Machine Learning Module allows CTB to leverage insights generated from algorithms that continuously learn from historical data."
Further Information: Matthew Wittemeier email@example.com
PREDICTING THE OPTIMUM PATH - Port Strategy
The year 2020 has witnessed some massive transformations of the decade with countries going under lockdown and millions of professionals losing their jobs overnight. Post seven months of isolation, when things are now beginning to return to normal, businesses have started looking to survive the post-COVID era. With tech professionals becoming a critical asset amid this crisis, companies have started putting out their resources for recruiting the right tech talent to remain relevant. And, thus, there has been a gradual rise in demand for professionals with advanced technical skills.
While tech skills have always been sought after, some skills are expected to gain more traction than others in the post-COVID world. In fact, various reports and studies conducted at the beginning of year highlighted a rise in domains like artificial intelligence, deep learning, data analytics and machine learning. Despite the lockdown, these domains continued to develop at a steady pace.
According to a recent study done by Analytics India Magazine, it has been noted that open jobs for analytics professionals have the highest proportion at 33.7%, which is then followed by machine learning at 20.4% and cybersecurity at 15.4%. This could be attributed to the immense amount of data that is generated in every transaction across B2B and B2C segments. Such data also shows that despite the recession, there is still a requirement for data analysis and automation to reduce redundancies, and to safeguard critical data.
Also Read: Why Indian IT Professionals Are Looking To Upskill Themselves In Cloud Computing
To remain relevant in the era beyond COVID, businesses are critically looking for tech talent and skills that can help them adapt to the new change. And for that, artificial intelligence and machine learning have turned out to be the critical skills that are creating a buzz in the industry. Not only the technologies have vast use cases in the industry but are also helping in significant innovations for the post-pandemic world.
With businesses expected to automate their operations and primary manual roles, the requirement for AI and ML experts have been on the rise. As a matter of fact, LinkedIns job report for this year noted that the hiring growth for AI specialists has grown to 74% annually in the past few years. On the other hand, according to another job portal, Indeed, the average base salary of a machine learning engineer is $145,539 per year in the US and a median wage of 13.6 Lakhs in India.
The data show that in spite of the economic downturn, the requirement for AI and ML talents has to be steady without any decrease in their salaries. The report further noted that skills like TensorFlow, Python, Natural Language Processing are the highest in demand and thus will be critical for professionals to land on AI/ML jobs.
Agreeing to this, Ammar Jagirdar, Product Head at Qure.ai said, while the demand for skilled data scientists and engineers has always been strong, in the post COVID era, we appreciate the importance of adaptability. Our world has changed rapidly and may transform further, but well be ready for these challenges, said Jagirdar. At Qure, we are looking for candidates with experience in deep learning R&D and product deployment, specifically for healthcare. We are also presently hiring for data science positions as well as front-end and back-end engineering roles.
Even Lakshya Sivaramakrishnan, Program Lead at Google believes that machine learning and data science will be the tech skills that will massively gain traction in the post COVID era. This is because more and more companies will be looking for more adaptability and flexibility post the pandemic and would adapt to the required change to stay relevant. However, she also believes that businesses cannot keep building models without having professionals with engineering standpoint, and thats where mean stack developers come into play.
Indeed shared a recent report on top ten most in-demand tech jobs, which highlighted a 30% growth in tech jobs listings and how developers, systems integration engineers and SAS programmers are most coveted. It stated that the average annual salary of a software engineer manager has increased to $144,793 and a SAS ABAP developers highest salary earring is $139,920. These numbers highlight the increased requirement of these lucrative skills in the industry.
With that being said, no one can deny the importance of AI and ML in almost every industry, including healthcare, finance, retail, manufacturing, education, etc. When asked, Abhinav Tushar, Head of AI at Vernacular.ai, stated that, with machine learning gaining traction, there is more demand for work involving automated analysis of the text, audio and video media content. Therefore, I think ML for analysis on speech/text has overall been exceptionally high in demand in recent times.
Thus, the possibilities are endless, and one can apply machine learning skills to every requirement starting from developing chatbots for better customer service to improving workplace communication and enhancing cybersecurity. Thus professionals who are delving into the fields of AI and ML are definitely going to be most in-demand for the post-COVID world.
With such evidence in hand, it can be established that the post-COVID world will be way advanced than today with enterprises adopting emerging technologies like artificial intelligence and machine learning. Since AI and ML will be the new norm rather than the exception, it is critical for companies as well as professionals to look at their strategies and find ways to delve into these evolving fields to stay relevant post-COVID.
Sejuti currently works as Senior Technology Journalist at Analytics India Magazine (AIM). Reach out at firstname.lastname@example.org
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AI/ML Remains The Most In-Demand Tech Skill Post COVID - Analytics India Magazine
Panalgo Brings the Power of Machine-Learning to the Healthcare Industry Via Its IHD Software – AiThority
Panalgos new Data Science module seamlessly integrates machine-learning techniques to identify new insights for patient care
Panalgo, a leading healthcare analytics company, announced the launch of its new Data Sciencemodule for Instant Health Data (IHD), which allows data scientists and researchers to leverage machine-learning to uncover novel insights from the growing volume of healthcare data.
Panalgos flagshipIHD Analytics softwarestreamlines the analytics process by removing complex programming from the equation and allows users to focus on what matters mostturning data into insights. IHD Analytics supports the rapid analysis of a wide range of healthcare data sources, including administrative claims, electronic health records, registry data and more. The software, which is purpose-built for healthcare, includes the most extensive library of customizable algorithms and automates documentation and reporting for transparent, easy collaboration.
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Panalgos new IHD Data Science module is fully integrated with IHD Analytics, and allows for analysis of large, complex healthcare datasets using a wide variety of machine-learning techniques. The IHD Data Science module provides an environment to easily train, validate and test models against multiple datasets.
Healthcare organizations are increasingly using machine-learning techniques as part of their everyday workflow. Developing datasets and applying machine-learning methods can be quite time-consuming, said Jordan Menzin, Chief Technology Officer of Panalgo. We created the Data Science module as a way for users to leverage IHD for all of the work necessary to apply the latest machine-learning methods, and to do so using a single system.
Our new IHD Data Science product release is part of our mission to leverage our deep domain knowledge to build flexible, intuitive software for the healthcare industry, said Joseph Menzin, PhD, Chief Executive Officer of Panalgo. We are excited to empower our customers to answer their most pressing questions faster, more conveniently, and with higher quality.
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The IHD Data Science module provides advanced analytics to better predict patient outcomes, uncover reasons for medication non-adherence, identify diseases earlier, and much more. The results from these analyses can be used by healthcare stakeholders to improve patient care.
Research abstracts using Panalgos IHD Data Science module are being presented at this weeks International Conference on Pharmacoepidemiology and Therapeutic Risk Management, including: Identifying Comorbidity-based Subtypes of Type 2 Diabetes: An Unsupervised Machine Learning Approach,andIdentifying Predictors of a Composite Cardiovascular Outcome Among Diabetes Patients UsingMachine Learning.
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Microchip Partners with Machine-Learning (ML) Software Leaders to Simplify AI-at-the-Edge Design Using its 32-Bit Microcontrollers (MCUs) – EE Journal
Cartesiam, Edge Impulse and Motion Gestures integrate their machine-learning (ML) offerings into Microchips MPLAB X Integrated Development Environment
CHANDLER, Ariz., September 15, 2020 Microchip Technology(Nasdaq: MCHP)today announced it has partnered with Cartesiam, Edge Impulse and Motion Gestures to simplify ML implementation at the edge using the companys ARM Cortex based 32-bit micro-controllers and microprocessors in its MPLAB X Integrated Development Environment (IDE). Bringing the interface to these partners software and solutions into its design environment uniquely positions Microchip to support customers through all phases of their AI/ML projects including data gathering, training the models and inference implementation.
Adoption of our 32-bit MCUs in AI-at-the-edge applications is growing rapidly and now these designs are easy for any embedded system developer to implement, said Fanie Duvenhage, vice president of Microchips human machine interface and touch function group. It is also easy to test these solutions using our ML evaluation kits such as the EV18H79A or EV45Y33A.
About the Partner Offerings
Cartesiam, founded in 2016,is a software publisher specializing in artificial intelligence development tools for microcontrollers. NanoEdge AI Studio, Cartesiams patented development environment, allows embedded developers, without any prior knowledge of AI, to rapidly develop specialized machine learning libraries for microcontrollers. Devices leveraging Cartesiamstechnology are already in production at hundreds ofsites throughout theWorld
Edge Impulse is the end-to-end developer platform for embedded machine learning, enabling enterprises in industrial, enterprise and wearable markets. The platform is free for developers, providing dataset collection, DSP and ML algorithms, testing and highly efficient inference code generation across a wide range of sensor, audio and vision applications. Get started in just minutes thanks to integrated Microchip MPLAB X and evaluation kit support.
Motion Gestures, founded in 2017, provides powerful embedded AI-based gesture recognition software for different sensors, including touch, motion (i.e. IMU) and vision. Unlike conventional solutions, the companys platform does not require any training data collection or programming and uses advanced machine learning algorithms. As a result, gesture software development time and costs are reduced by 10x while gesture recognition accuracy is increased to nearly 100 percent.
See Demonstrations During Embedded Vision Summit
The MPLAB X IDE ML implementations will be featured during theEmbedded Vision Summit 2020 virtual conference, September 15-17. Attendees can see video demonstrations at the companys virtual exhibit, which will be staffed each day from 10:30 a.m. to 1 p.m. PDT.
Please let us know if you would like to speak to a subject matter expert on Microchips enhanced MPLAB X IDE for ML implementations, or the use of 32-bit microcontrollers in AI-at-the-edge applications. For more information visitmicrochip.com/MLCustomers can get a demo by contacting a Microchip sales representative.
Microchips offering of ML development kits now includes:
EV18H79A: SAMD21 ML Evaluation Kit with TDK 6-axis MEMS
EV45Y33A: SAMD21 ML Evaluation Kit with BOSCH IMU
SAMC21 xPlained Pro evaluation kit (ATSAMC21-XPRO) plus its QT8 xPlained Pro Extension Kit (AC164161): available for evaluating the Motion Gestures solution.
VectorBlox Accelerator Software Development Kit (SDK): enables developers to create low-power, small-form-factor AI/ML applications on Microchips PolarFireFPGAs.
About Microchip Technology
Microchip Technology Inc. is a leading provider of smart, connected and secure embedded control solutions. Its easy-to-use development tools and comprehensive product portfolio enable customers to create optimal designs which reduce risk while lowering total system cost and time to market. The companys solutions serve more than 120,000 customers across the industrial, automotive, consumer, aerospace and defense, communications and computing markets. Headquartered in Chandler, Arizona, Microchip offers outstanding technical support along with dependable delivery and quality. For more information, visit the Microchip website atwww.microchip.com.
Wayne Blodwell, founder and chief exec of The Programmatic Advisory & The Programmatic University, battles through the buzzwords to explain why custom machine learning can help you unlock differentiation and regain a competitive edge.
Back in the day, simply having programmatic on plan was enough to give you a competitive advantage and no one asked any questions. But as programmatic has grown, and matured (84.5% of US digital display spend is due to be bought programmatically in 2020, the UK is on track for 92.5%), whats next to gain advantage in an increasingly competitive landscape?
The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.
(Oxford Dictionary, 2020)
Youve probably head of machine learning as it exists in many Demand Side Platforms in the form of automated bidding. Automated bidding functionality does not require a manual CPM bid input nor any further bid adjustments instead, bids are automated and adjusted based on machine learning. Automated bids work from goal inputs, eg achieve a CPA of x or simply maximise conversions, and these inputs steer the machine learning to prioritise certain needs within the campaign. This tool is immensely helpful in taking the guesswork out of bids and the need for continual bid intervention.
These are what would be considered off-the-shelf algorithms, as all buyers within the DSP have access to the same tool. There is a heavy reliance on this automation for buying, with many even forgoing traditional optimisations for fear of disrupting the learnings and holding it back but how do we know this approach is truly maximising our results?
Well, we dont. What we do know is that this machine learning will be reasonably generic to suit the broad range of buyers that are activating in the platforms. And more often than not, the functionality is limited to a single success metric, provided with little context, which can isolate campaign KPIs away from their true overarching business objectives.
Custom machine learning
Instead of using out of the box solutions, possibly the same as your direct competitors, custom machine learning is the next logical step to unlock differentiation and regain an edge. Custom machine learning is simply machine learning that is tailored towards specific needs and events.
Off-the-self algorithms are owned by the DSPs; however, custom machine learning is owned by the buyer. The opportunity for application is growing, with leading DSPs opening their APIs and consoles to allow for custom logic to be built on top of existing infrastructure. Third party machine learning partners are also available, such as Scibids, MIQ & 59A, which will develop custom logic and add a layer onto the DSPs to act as a virtual trader, building out granular strategies and approaches.
With this ownership and customisation, buyers can factor in custom metrics such as viewability measurement and feed in their first party data to align their buying and success metrics with specific business goals.
This level of automation not only provides a competitive edge in terms of correctly valuing inventory and prioritisation, but the transparency of the process allows trust to rightfully be placed with automation.
For custom machine learning to be effective, there are a handful of fundamental requirements which will help determine whether this approach is relevant for your campaigns. Its important to have conversations surrounding minimum event thresholds and campaign size with providers, to understand how much value you stand to gain from this path.
Furthermore, a custom approach will not fix a poor campaign. Custom machine learning is intended to take a well-structured and well-managed campaign and maximise its potential. Data needs to be inline for it to be adequately ingested and for real insight and benefit to be gained. Custom machine learning cannot simply be left to fend for itself; it may lighten the regular day to day load of a trader, but it needs to be maintained and closely monitored for maximum impact.
While custom machine learning brings numerous benefits to the table transparency, flexibility, goal alignment its not without upkeep and workflow disruption. Levels of operational commitment may differ depending on the vendors selected to facilitate this customisation and their functionality, but generally buyers must be willing to adapt to maximise the potential that custom machine learning holds.
Find out more on machine learning in a session The Programmatic University are hosting alongside Scibids on The Future Of Campaign Optimisation on 17 September. Sign up here.
PODCAST: NVIDIA’s Director of Data Science Talks Machine Learning for Airlines and Aerospace – Aviation Today
Geoffrey Levene is the Director of Global Business Development for Data Science and Space at NVIDIA.
On this episode of the Connected Aircraft Podcast, we learn how airlines and aerospace manufacturers are adopting the use of data science workstations to develop task-specific machine learning models with Geoffrey Levene, Director, Global Business Development for Data Science and Space at NVIDIA.
In a May 7 blog, NVIDIA one of the worlds largest suppliers of graphics processing units and computer chips to the video gaming, automotive and other industries explained how American Airlines is using its data science workstations to integrate machine learning into its air cargo operations planning. During this interview, Levene expands on other airline and aerospace uses of those same workstations and how they are creating new opportunities for efficiency.
Have suggestions or topics we should focus on in the next episode? Email the host, Woodrow Bellamy email@example.com, or drop him a line on Twitter@WbellamyIIIAC.
Listen to this episode below, orcheck it out on iTunesorGoogle PlayIf you like the show, subscribe on your favorite podcast app to get new episodes as soon as theyre released.