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It is one of the fundamentals of all the best esports betting sites, yet it is something that still remains something of a mystery to many of those that enjoy betting on esports. Have you ever wondered how companies come up with the latest esports betting odds?

The sheer number of tournaments there are nowadays, means that it is almost impossible for humans to assess and update all the different markets and odds available on a typical esports event, so something more advanced is required. This is where software like machine learning applications come into their own.

As the name suggests, machine learning is the process by the way a computer program can develop an algorithm that uses the data it collects to effectively predict what will happen in the future. This type of software is not just used for esports betting technology but serves a huge number of uses in other fields too.

For example, similar applications are used to help predict future weather patterns using the same process, triggering billions of data points to process and predict what the weather will do. In terms of AI betting predictions, a similar process is followed, only this time to come up with the odds for a typical esports market or event.

Data is fundamental for any betting industry, and one of the big advantages of esports is that its data is readily available and accessible. Companies like PandaScore, Oddin, and Sportsflare are just three that have taken this approach to formulate esports odds using AI betting predictions.

This is particularly important when companies like to offer Live Odds on sports events. The ever-changing nature of In-Play betting means that things can change in an instant. This is almost impossible for a human to assess, but machine learning applications and assess the data in real time and make adjustments to the odds extremely quickly to reflect the ever-changing nature of esports contests.

As PandaScore founder and CEO Flavien Guillocheau explained:

Bookmakers are some of the most data-dependent companies out there. With esports tournaments and fantasy leagues becoming a more significant component of sportsbook offerings around the world, access to PandaScores abundance of real time esports odds and data is vital to all the bookmakers we work with. Our vision is to help esports grow by providing the data resources for any esports business.

Of course, it is one thing to create software that claims it can use data to predict betting outcomes and help set odds, but the proof of the pudding so to speak is when the software is put to work in a real world setting.

The good news here for esports betting services is that in the real world, the predictive models have worked extremely well.

Data provided by Sportsflare based on predictions for a CS:GO map winner market revealed exceptionally accurate predictions that were very well calibrated with the actual outcomes of those games.

The net result of this is that sites like Unikrn betting can be sure that the odds that their Ai betting predictions software are churning out, are not only incredibly accurate but that over time, that accuracy will only improve as more data becomes available.

AI has a huge role to play in the future of esports. It can not only be used to predict outcomes of events and help formulate odds, but it can also be used to help track fraudulent play, catching people attempting to cheat before they have had the chance to claim any sort of reward from their activities.

In fact, AI has so many potential applications in the esports betting industry that we are only seeing the tip of the iceberg in terms of how it can be used to benefit the industry and ensure that both punters and esports betting companies can thrive together.

Original post:
The role of AI Betting Predictions and Machine Learning in Esports Odds - EsportsBets

Entries for the AI & Machine Learning Awards 2021 in one week

Artificial intelligence adoption is increasing at an unprecedented rate, with new products, projects and solutions appearing every day, in every industry sector. Computing's AI & Machine Learning Awards 2021 recognise and honour the very best of these developments, each of which has the potential to drive massive change. But if you think you deserve a spot, you'd better hurry: entries close on the 1st April.

From data entry to chatbots to healthcare and the environment, AI has applications in every industry, sector, and role. Even the most basic implementations can free a workforce from time-consuming manual tasks, with more complex developments providing real insight into customer data.

Anexpert panel of judgesfrom some of the UK's most recognised organisations will assess each entry, including Rachel Anne Jones (CIO - Valuation Office Agency at HMRC); Aidan Hancock (Group CIO at Network Rail) and Sudip Trivedi (Head of Data and Analytics at London Borough of Camden).

TheAI & Machine Learning Awardsrecognise the best companies, individuals, and projects in the AI space today. The awards cover every corner of the industry: security, ethics, data analysis, innovation and more, as well as showcasing the technology heroes and projects that deserve industry-wide praise.

We refresh the categories for our awards each year to ensure they present the most up-to-date view of the industry. As well as returning favourites like Best Emerging Technology in AI andAI/ML Team of the Year, the 2021 awards have three new categories:Business Transformation of the Year, Best Marketing Automation Project, and a Special Award for Pandemic Performance. See below for a full list of categories for 2021.

Entries forAI & Machine Learning Awards 2021 close in just a few days, so make sure to enter soon.

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Entries for the AI & Machine Learning Awards 2021 close in one week - http://www.computing.co.uk

GlobalMachine Learning Operationalization Software Marketresearch report is a comprehensive synopsis on the study of industry and its influence on the market environment. Some of the competitor strategies can be mentioned here as new product launches, expansions, agreements, partnerships, joint ventures, and acquisitions. This market report is a clear-cut solution which can be adopted by businesses to thrive in this swiftly changing marketplace. Not to mention all the topics included have been watchfully analysed with the best tools and techniques. Utilization of well-established tools and techniques in the comprehensive Machine Learning Operationalization Software business report helps to turn complex market insights into simpler version.

Machine Learning Operationalization Software market report endows with the data and statistics on the current state of the industry which directs companies and investors interested in this market. Because businesses can accomplish great benefits with the different and all-inclusive segments covered in the market research report, every bit of market that can be included here is tackled carefully. This market research report provides the best answers to many of the critical business questions and challenges. Competitive analysis studies of the Machine Learning Operationalization Software market document provides with the ideas about the strategies of key players in the market.

Download Sample (350 Pages PDF) Report: To Know the Impact of COVID-19 on this [emailprotected]https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-machine-learning-operationalization-software-market&AM

The development of the technology that is creating the Machine Learning Operationalization Software market is included in the report such asCapacity, production, price, income, expenses, gross margin, sales, income, consumption, growth, imports, exports, supplies, future strategy,and complete profile of the best manufacturers in the world such asThe Major Players Covered In The Machine Learning Operationalization Software Report Are The Mathworks, Inc, Sas Institute Inc, Microsoft, Parallelm, Inc, Algorithmia Inc, Tibco Software Inc, Sap, Ibm Corporation, Seldon Technologies Ltd, Actico Gmbh, Rapidminer, Inc And Knime Ag Among Other Domestic And Global Players. Market Share Data Is Available For Global, North America, Europe, Asia-Pacific, Middle East And Africa And South America Separately. Dbmr Analysts Understand Competitive Strengths And Provide Competitive Analysis For Each Competitor Separately.

Machine Learning Operationalization Software Market Is Expected To Gain Market Growth In The Forecast Period Of 2020 To 2027. Data Bridge Market Research Analyses The Market Growing At A Cagr Of 44.2% In The Above-Mentioned Forecast Period.

COVID-19 Impact Analysis on Machine Learning Operationalization Software Industry:

The report seeks to track the evolution of the market growth pathways and publish a medical crisis in an exclusive section publishing an analysis of the impact of COVID-19 on the Machine Learning Operationalization Software market. The new analysis on the COVID-19 pandemic provides a clear assessment of the impact on the Machine Learning Operationalization Software market and the expected volatility of the market during the forecast period. Various factors that can affect the general dynamics of the Machine Learning Operationalization Software market during the forecast period (2020-2027), including current trends, growth opportunities, limiting factors, etc., are discussed in detail in this market research.

The Best part of this report is, this analyses the current state where all are fighting with the COVID-19, the report also provides the market impact and new opportunities created due to the Covid19 catastrophe.

Analysis of external factors-

External analytics investigate the large business environments that affect your business. This industry classification covers all the items that you cannot control. Here, both micro and macro environmental factors are included.

Growth & Margins-

Leading companies with strong growth records are a must for analyst research. From 2014 to 2019, some companies showed huge sales figures, doubling their net profits during that period, and their sales margins and gross profit continued to grow. The increase in the gross profit margin over the past few years drives more than the increase in the cost of products that are selling strong price power from competitive companies in the industry for products and proposals.

Read More @https://www.databridgemarketresearch.com/reports/global-machine-learning-operationalization-software-market?AM

Table of Contents: Machine Learning Operationalization Software Market

Part 01: Executive Summary

Part 02: Scope of the Report

Part 03: Research Methodology

Part 04: Market Landscape

Part 05: Pipeline Analysis

Part 06: Market Sizing

Part 07: Five Forces Analysis

Part 08: Market Segmentation

Part 09: Customer Landscape

Part 10: Regional Landscape

Part 11: Decision Framework

Part 12: Drivers and Challenges

Part 13: Market Trends

Part 14: Vendor Landscape

Part 15: Vendor Analysis

Part 16: Appendix

See the Complete Table Of Contents And List Of Exhibits, As Well As Selected Illustrations And Example Pages From This Report.

For More Insights Get PDF version of Detailed Table of Content with Respective Images and Charts @ https://www.databridgemarketresearch.com/toc/?dbmr=global-machine-learning-operationalization-software-market&AM

Fierce competition with ambitious growth plans-

Industry Players will launch new products in various markets around the world, considering their application / end use. Given the general development activity of the major industries, the profile of some players is worth attention-seeking.

Where the Machine Learning Operationalization Software Industry is today?

In the last year, the market sector has shown modest gains, so it may not be very exciting. Unlike in the past, appropriate valuations and emerging investment cycles occurred in Asia-Pacific, North America, Europe, South America, the Middle East, and Africa. When companies have a lot of growth opportunities in 2020, they now seem to fall, but higher revenues are expected thereafter.

Reasons to buy this report:

Thanks for your interest. You can obtain section versions of individual chapters or regional reports such as ASEAN, GCC, LATAM, Western Europe / Eastern Europe, and Southeast Asia.

About Data Bridge Market Research:

Data Bridge Market Researchis a versatile market research and consulting firm with over 500 analysts working in different industries. We have catered more than 40% of the fortune 500 companies globally and have a network of more than 5000+ clientele around the globe. Our coverage of industries includes Medical Devices, Pharmaceuticals, Biotechnology, Semiconductors, Machinery, Information and Communication Technology, Automobiles and Automotive, Chemical and Material, Packaging, Food and Beverages, Cosmetics, Specialty Chemicals, Fast Moving Consumer Goods, Robotics, among many others.

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Machine Learning Operationalization Software Market Potential Growth, Share, Demand and Analysis of Key Players Research Forecasts The Market Eagle...

After much pining, AMD PC enthusiastsas well as console gamers, potentiallywill finally be getting FidelityFX Super Resolution (FSR) this year. That's the red team's answer to Nvidia's DLSS, and could mean ray tracing isn't the restrictive force it is right now for the Radeon RX 6000-series cards. There's been no word as to an exact release date but, at some point in 2021 those harbouring an RDNA2 graphics card will be able to enjoy the new, resolution-based performance improving techwith no need for machine learning.

FSR is AMD's equivalent to Nvidia's DLSS (Deep Learning Super Sampling) which uses AI to sharpen up frames and stabilise frame rates at higher resolutions, and is essentially what allows GeForce cards to deliver decent performance when using ray traced lighting effects. Though, as AMD's VP of graphics, Scott Herkelman, explains in his recent talk with PCWorld, "you don't need machine learning to do it."

Herkelman admitted there's still some work to be done, but it's coming along well. He explains that the company has made an effort to involve it's followers in the design process, giving them a chance to really influence the direction the company goes with the technology.

This dedication to open development may have hampered the process in terms of speed, but it it means developers are more ready and able to collaborate to improve the tech.

Despite AMD's focus is on getting FSR out to PC gamers first, it should also be rolling out as a cross-platform technology. Meaning this isn't just going to benefit PC gamers, but console gamers too thanks to AMD components being packed inside the likes of the PlayStation 5 and Xbox series X and S.

There was some potential for the FSR feature to have released alongside the Radeon RX 6700 XT, but it seems AMD is waiting for the entire lineup to be availableI use that term looselybefore hitting us with the new tech.

Still, the list of general FidelityFX-supporting games is growing, showing that the forerunner features for FSR are being taken seriously by developers. And, with each step, AMD comes closer to rolling out this impressive-sounding technological development.

Original post:
AMD's DLSS-alternative doesn't need machine learning to work - PC Gamer

What good is an ML model if it isnt fast? doesnt scale? isnt accurate enough? takes weeks to deploy? and costs too much?

Having machine learning in a companys portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster.

According to PwC, AIs potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the companys total funding to $47 million.

Image credits: OctoML

90% of machine learning models dont make it to production.

For OctoMLs CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware. Even if your determined machine learning team has hurtled through this gauntlet, they still have to go through a whole different set of challenges to package and deploy at the edge, explained Ceze.

A good performance in ML models requires long hours of manual optimizations. These long hours will then translate into hefty cloud bills. Added to this is the model packaging which varies with devices and platforms. According to Ceze, there are no modern CI/CD integrations to keep up with model changes.

What good is an ML model if it isnt fast? doesnt scale? isnt accurate enough? takes weeks to deploy? and costs too much?, questioned Ceze as he made a case for OctoML.

OctoML addressed these pain points with their open-source machine learning compiler framework Apache TV, which according to the team, has quickly become the go-to solution for developers and ML engineers to maximize ML model performance on any hardware backend. With OctoML we are establishing the first Machine Learning Acceleration Platform that will automatically maximize model performance while enabling seamless deployment on any hardware, cloud provider, or edge devices, said Ceze.

Be it MLOps or XOps, these services are designed to ease the developers of technical debt that these mega ML models accumulate with changing complexities. Apart from OctoML, there are a few other startups that have succeeded in convincing the investors. Lets take a look at couple of them:

Funding till date: $10 million

The team at Verta is building software for data science teams to address the problem of model management how to track, version, and audit models used across products. Verta MLOps software supports model development, deployment, operations, monitoring, and collaboration enabling data scientists to manage models across their lifecycle. So far, the company has $10 million in funding and it promises to make robust, scalable, mature deployable models a reality.

Funding till date: $38.1 million

Image credits: Algorithmia

Were obsessed with helping organizations get ML models into production because thats the only way they can generate business value, said the team at Algorithmia. Their enterprise MLOps platform manages all stages of the production ML lifecycle within existing operational processes, so you can put models into production quickly, securely, and cost-effectively. Unlike inefficient and expensive do-it-yourself MLOps management solutions that lock users into specific technology stacks, Algorithmia automates ML deployment, optimizes collaboration between operations and development, leverages existing SDLC and CI/CD systems, and provides advanced security and governance.

Algorithmias funding (Source: Crunchbase)

Today Algorithmias services are used by over 130,000 engineers and data scientists, including the United Nations, government intelligence agencies, and Fortune 500 companies.

Its [MLOps] going to be an essential component to enterprises industrializing their AI efforts in the future, said Diego M. Oppenheimer, Algorithmias CEO in a recent interview with GitHub.

Funding: $14.5 million

Databand brings in the similar flavor into the ML ecosystem. The team Databand is trying to solve the problems that arise due to increasing data workloads. The company founded by Josh Benamram, Victor Shafran and Evgeny Shulmanhelps helps data engineering teams catch data pipeline issues and trace the impact of those problems across end-to-end data flows. Databands platform includes an application for visualizing pipeline metadata, and an open source library for integrating with your Python, Java, Scala, or SQL data processes. Data pipeline monitoring is a key aspect of machine learning deployment. We can clearly see how targeting even a niche aspect of the whole ML deployment can land big investors.

Image credits: Gartner

Modern day software companies are in the process of or have already embraced machine learning as a key tool. Now they are at a crucial juncture where they can either leverage the MLOps services offered by these startups or build everything on their own. But, there are not many reasons why an organization looking to transition to ML will take the pain of MLOps. As companies look to leverage ML minus the deployment headache, niche players like OctoML will continue to pop up. Even the latest Gartner survey lists scalability and acceleration of machine learning deployment as two driving forces that will continue to trend this year. According to Gartner, XOps a variant of MLOps that deals with efficiencies in data, machine learning, model, platform will try to implement best DevOps practices and ensure reliability, reusability and repeatability.

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Machine Learning Deployment Is The Biggest Tech Trend In 2021 - Analytics India Magazine