A detailed research on Artificial Intelligence and Machine Learning market recently added by Hongchun Research, puts together a concise analysis of the growth factors impacting the current business scenario across assorted regions. Significant information pertaining to the industry size, share, application, and statistics are also summed in the report in order to present an ensemble prediction. In addition, this report undertakes an accurate competitive analysis illustrating the status of market majors in the projection timeline, while including their expansion strategies and portfolio.

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The global Artificial Intelligence and Machine Learning market focuses on encompassing major statistical evidence for the Artificial Intelligence and Machine Learning industry as it offers our readers a value addition on guiding them in encountering the obstacles surrounding the market. A comprehensive addition of several factors such as global distribution, manufacturers, market size, and market factors that affect the global contributions are reported in the study. In addition the Artificial Intelligence and Machine Learning study also shifts its attention with an in-depth competitive landscape, defined growth opportunities, market share coupled with product type and applications, key companies responsible for the production, and utilized strategies are also marked.

This intelligence and 2026 forecasts Artificial Intelligence and Machine Learning industry report further exhibits a pattern of analyzing previous data sources gathered from reliable sources and sets a precedented growth trajectory for the Artificial Intelligence and Machine Learning market. The report also focuses on a comprehensive market revenue streams along with growth patterns, analytics focused on market trends, and the overall volume of the market.

The study covers the following key players:BigML, Inc.SAP SEGoogle, Inc.Hewlett Packard Enterprise Development LP (HPE)Microsoft CorporationIntel CorporationIBM CorporationSAS Institute Inc.Baidu, Inc.Fair Isaac CorporationAmazon Web Services Inc.

Moreover, the Artificial Intelligence and Machine Learning report describes the market division based on various parameters and attributes that are based on geographical distribution, product types, applications, etc. The market segmentation clarifies further regional distribution for the Artificial Intelligence and Machine Learning market, business trends, potential revenue sources, and upcoming market opportunities.

Market segment by type, the Artificial Intelligence and Machine Learning market can be split into,HardwareSoftwareServices

Market segment by applications, the Artificial Intelligence and Machine Learning market can be split into,BFSIHealthcare and Life SciencesRetailTelecommunicationGovernment and DefenseManufacturingEnergy and UtilitiesOthers

The Artificial Intelligence and Machine Learning market study further highlights the segmentation of the Artificial Intelligence and Machine Learning industry on a global distribution. The report focuses on regions of North America, Europe, Asia, and the Rest of the World in terms of developing business trends, preferred market channels, investment feasibility, long term investments, and environmental analysis. The Artificial Intelligence and Machine Learning report also calls attention to investigate product capacity, product price, profit streams, supply to demand ratio, production and market growth rate, and a projected growth forecast.

In addition, the Artificial Intelligence and Machine Learning market study also covers several factors such as market status, key market trends, growth forecast, and growth opportunities. Furthermore, we analyze the challenges faced by the Artificial Intelligence and Machine Learning market in terms of global and regional basis. The study also encompasses a number of opportunities and emerging trends which are considered by considering their impact on the global scale in acquiring a majority of the market share.

The study encompasses a variety of analytical resources such as SWOT analysis and Porters Five Forces analysis coupled with primary and secondary research methodologies. It covers all the bases surrounding the Artificial Intelligence and Machine Learning industry as it explores the competitive nature of the market complete with a regional analysis.

Brief about Artificial Intelligence and Machine Learning Market Report with [emailprotected]https://hongchunresearch.com/report/artificial-intelligence-and-machine-learning-market-37063

Some Point of Table of Content:

Chapter One: Artificial Intelligence and Machine Learning Market Overview

Chapter Two: Global Artificial Intelligence and Machine Learning Market Landscape by Player

Chapter Three: Players Profiles

Chapter Four: Global Artificial Intelligence and Machine Learning Production, Revenue (Value), Price Trend by Type

Chapter Five: Global Artificial Intelligence and Machine Learning Market Analysis by Application

Chapter Six: Global Artificial Intelligence and Machine Learning Production, Consumption, Export, Import by Region (2014-2019)

Chapter Seven: Global Artificial Intelligence and Machine Learning Production, Revenue (Value) by Region (2014-2019)

Chapter Eight: Artificial Intelligence and Machine Learning Manufacturing Analysis

Chapter Nine: Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter Ten: Market Dynamics

Chapter Eleven: Global Artificial Intelligence and Machine Learning Market Forecast (2019-2026)

Chapter Twelve: Research Findings and Conclusion

Chapter Thirteen: Appendix continued

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List of tablesList of Tables and FiguresFigure Artificial Intelligence and Machine Learning Product PictureTable Global Artificial Intelligence and Machine Learning Production and CAGR (%) Comparison by TypeTable Profile of HardwareTable Profile of SoftwareTable Profile of ServicesTable Artificial Intelligence and Machine Learning Consumption (Sales) Comparison by Application (2014-2026)Table Profile of BFSITable Profile of Healthcare and Life SciencesTable Profile of RetailTable Profile of TelecommunicationTable Profile of Government and DefenseTable Profile of ManufacturingTable Profile of Energy and UtilitiesTable Profile of OthersFigure Global Artificial Intelligence and Machine Learning Market Size (Value) and CAGR (%) (2014-2026)Figure United States Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Europe Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Germany Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure UK Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure France Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Italy Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Spain Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Russia Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Poland Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure China Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Japan Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure India Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Southeast Asia Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Malaysia Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Singapore Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Philippines Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Indonesia Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Thailand Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Vietnam Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Central and South America Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Brazil Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Mexico Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Colombia Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Middle East and Africa Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Saudi Arabia Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure United Arab Emirates Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Turkey Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Egypt Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure South Africa Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Nigeria Artificial Intelligence and Machine Learning Revenue and Growth Rate (2014-2026)Figure Global Artificial Intelligence and Machine Learning Production Status and Outlook (2014-2026)Table Global Artificial Intelligence and Machine Learning Production by Player (2014-2019)Table Global Artificial Intelligence and Machine Learning Production Share by Player (2014-2019)Figure Global Artificial Intelligence and Machine Learning Production Share by Player in 2018Table Artificial Intelligence and Machine Learning Revenue by Player (2014-2019)Table Artificial Intelligence and Machine Learning Revenue Market Share by Player (2014-2019)Table Artificial Intelligence and Machine Learning Price by Player (2014-2019)Table Artificial Intelligence and Machine Learning Manufacturing Base Distribution and Sales Area by PlayerTable Artificial Intelligence and Machine Learning Product Type by PlayerTable Mergers & Acquisitions, Expansion PlansTable BigML, Inc. ProfileTable BigML, Inc. Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table SAP SE ProfileTable SAP SE Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Google, Inc. ProfileTable Google, Inc. Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Hewlett Packard Enterprise Development LP (HPE) ProfileTable Hewlett Packard Enterprise Development LP (HPE) Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Microsoft Corporation ProfileTable Microsoft Corporation Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Intel Corporation ProfileTable Intel Corporation Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table IBM Corporation ProfileTable IBM Corporation Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table SAS Institute Inc. ProfileTable SAS Institute Inc. Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Baidu, Inc. ProfileTable Baidu, Inc. Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Fair Isaac Corporation ProfileTable Fair Isaac Corporation Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Amazon Web Services Inc. ProfileTable Amazon Web Services Inc. Artificial Intelligence and Machine Learning Production, Revenue, Price and Gross Margin (2014-2019)Table Global Artificial Intelligence and Machine Learning Production by Type (2014-2019)Table Global Artificial Intelligence and Machine Learning Production Market Share by Type (2014-2019)Figure Global Artificial Intelligence and Machine Learning Production Market Share by Type in 2018Table Global Artificial Intelligence and Machine Learning Revenue by Type (2014-2019)Table Global Artificial Intelligence and Machine Learning Revenue Market Share by Type (2014-2019)Figure Global Artificial Intelligence and Machine Learning Revenue Market Share by Type in 2018Table Artificial Intelligence and Machine Learning Price by Type (2014-2019)Figure Global Artificial Intelligence and Machine Learning Production Growth Rate of Hardware (2014-2019)Figure Global Artificial Intelligence and Machine Learning Production Growth Rate of Software (2014-2019)Figure Global Artificial Intelligence and Machine Learning Production Growth Rate of Services (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption by Application (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption Market Share by Application (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of BFSI (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of Healthcare and Life Sciences (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of Retail (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of Telecommunication (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of Government and Defense (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of Manufacturing (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of Energy and Utilities (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption of Others (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption by Region (2014-2019)Table Global Artificial Intelligence and Machine Learning Consumption Market Share by Region (2014-2019)Table United States Artificial Intelligence and Machine Learning Production, Consumption, Export, Import (2014-2019)Table Europe Artificial Intelligence and Machine Learning Production, Consumption, Export, Import (2014-2019)Table China Artificial Intelligence and Machine Learning Production, Consumption, Export, Import (2014-2019)Table Japan Artificial Intelligence and Machine Learning Production, Consumption, Export, Import (2014-2019)Table India Artificial Intelligence and Machine Learning Production, Consumption, Export, Import (2014-2019)Table Southeast Asia Artificial Intelligence and Machine Learning Production, Consumption, Export, Import (2014-2019)Table Central and South America Artificial Intelligence and Machine Learning Production, Consumption, Export, Import (2014-2019) continued

About HongChun Research:HongChun Research main aim is to assist our clients in order to give a detailed perspective on the current market trends and build long-lasting connections with our clientele. Our studies are designed to provide solid quantitative facts combined with strategic industrial insights that are acquired from proprietary sources and an in-house model.

Contact Details:Jennifer GrayManager Global Sales+ 852 8170 0792[emailprotected]

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Artificial Intelligence and Machine Learning Market Size, Share, 2019 with Detailed Analysis of Services, Consumption, Software Technology, Trends,...

The real possibility of advancing intelligence through deep learning and other AI-driven technology applied to video is that, in the long term, were not going to be looking at the video until after something has happened. The goal of gathering this high level of intelligence through video has the potential to be automated to the point that security operators will not be required to make the decisions necessary for response. Instead, the intelligence-driven next steps will be automatically communicated to various stakeholders from on-site guards to local police/fire departments. Instead, when security leaders access the video that corresponds to an incident, it will be because they want to see the incident for themselves. And isnt the automation, the ability to streamline response, and the instantaneous response the goal of an overall, data-rich surveillance strategy? For almost any enterprise, the answer is yes.

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Benefits of AI and machine learning | Expert Panel | Security News - SourceSecurity.com

New Jersey, United States,- Market Research Intellect aggregates the latest research on Machine Learning Software Market to provide a concise overview of market valuation, industry size, SWOT analysis, revenue approximation, and regional outlook for this business vertical. The report accurately addresses the major opportunities and challenges faced by competitors in this industry and presents the existing competitive landscape and corporate strategies implemented by the Machine Learning Software market players.

The Machine Learning Software market report gathers together the key trends influencing the growth of the industry with respect to competitive scenarios and regions in which the business has been successful. In addition, the study analyzes the various limitations of the industry and uncovers opportunities to establish a growth process. In addition, the report also includes a comprehensive research on industry changes caused by the COVID-19 pandemic, helping investors and other stakeholders make informed decisions.

Key highlights from COVID-19 impact analysis:

Unveiling a brief about the Machine Learning Software market competitive scope:

The report includes pivotal details about the manufactured products, and in-depth company profile, remuneration, and other production patterns.

The research study encompasses information pertaining to the market share that every company holds, in tandem with the price pattern graph and the gross margins.

Machine Learning Software Market, By Type

Machine Learning Software Market, By Application

Other important inclusions in the Machine Learning Software market report:

A brief overview of the regional landscape:

Reasons To Buy:

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

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Market Research Intellect

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Machine Learning Software Market 2020 Size by Product Analysis, Application, End-Users, Regional Outlook, Competitive Strategies and Forecast to 2027...

The emergence of technologies such as AI and machine learning, along with sophisticated analytics, offers opportunities for smart manufacturers to transform their businesses radically to create new product and service offerings while maximising the efficiency of supply chains and processes.

Contemporary computing models such as Cloud and, increasingly, Edge computing release huge amounts of sensor- and device-related data, to help with decision-making.

To succeed, however, companies need to be able to leverage this vast trove of data. In far too many cases, much of the data that is produced at the Edge is discarded, rather than transferred to a core environment for long-term storage.

For now, at least, the sector is lagging many other industries when it comes to capturing this opportunity.

That is a key finding in a report written by Seagate based on IDCs surveys, called ReThink Data: Put More of your Business Data to Work from Edge to Cloud. That study, which surveyed 1500 global enterprises, delved into the performance of several sectors including manufacturing and revealed that the vast majority of data available to organisations simply goes unused.

Manufacturing, despite having one of the highest enterprise data centre footprints and high levels of instrument connectivity, is actually recording one of the lowest levels of data growth compared to other sectors. In part, this is because it is difficult to expand the capacity of all that on-premises infrastructure especially when compared to scalable Cloud infrastructure more common in other sectors.

The irony is that the methods, strategies, and processes that typically make manufacturers successful seem to stop on the shop floor. For an industry steeped in the heritage of automation and integration, evidence suggests that these lessons are lost when it comes to managing data.

The authors of ReThink Data found that somewhat counterintuitively, manufacturing had the lowest level of task automation in data management and the lowest rate for full integration (single platform) of data management functions.

Among the key findings of the report:

The disconnect between assets and data management is not only inhibiting manufacturers from maximising the return from their current plant and equipment, it is also stifling innovation and opening up opportunities for more nimble and aggressive competitors to grab lucrative niches in the market.

So why does this disconnect exist?

A study in 2018 by industry analyst IDC, looking into the integration of Information technology and operational technology, said that many assets on the factory floor remain disconnected.

IDC says that this speaks to a strategic weakness around core enterprise architecture and infrastructure, with the researchers explaining that much current legacy infrastructure simply wont be able to cope with the inevitable growth of connected assets entering the plant.

The temptation for many companies will be to try to implement ad hoc processes to connect and manage assets, leaving them unable to rely on the underlying infrastructure for comprehensive management.

IDC also found that, even for companies that want to solve these issues, there is another problem: their aging workforces were trained for a different era of manufacturing. Too many lack the hard and soft IT skills that are prerequisites for the shop floor of tomorrow.

The Importance of DataOps

As plant and equipment become more digitised, generating vastly more data, the successful manufacturers will be those who can connect the data creators (which can include people and machines) with the data consumers (such as C-Suite and general executives).

Its a discipline in IT referred to as DataOps.

Seagate argues that DataOps should be part of every data-management strategy, which should also include data orchestration from endpoints to the core, data architecture, and data security.

The mission of DataOps is to provide managers with a holistic view of data and to enable users to access and derive the most value from data whether it is in motion or at rest.

This is another area, however, where many manufacturers are lagging. The ReThink Data report reveals that less than a third of manufacturers indicated that they have fully or even partially implemented a DataOps capacity.

The development and deployment of this skillset need to accelerate if manufacturers are to remain at the crest of the competitive curve. DataOps capabilities are essential to leveraging emerging technologies such as AI and machine learning, and to correlate data from core, Cloud, and Edge data sources.

The work of DataOps teams allows data science and data analytics professionals to use technologies like AI to transform data into the information needed by decision-makers.

And as the ReThink Data Report explains, Being able to correlate data from disparate sources is a capability not easily available through other means. Because it is difficult, those organisations able to master it can expect to have an edge over the competition.

More than Technology

The authors of ReThink Data also make clear that DataOps is an important part of implementing the necessary cultural change required to implement new ways of working by facilitating the sharing of data and breaking down organisational silos.

To succeed, however, leaders still need to drive a strategy that implements global standards, global data architecture, and global data management and delivers access to the same analytical tools by global teams.

Seagate says that rolling back reporting functions to IT can provide global tools, capabilities, and solutions that every group can leverage.

The various groups within the enterprise should get out of siloed management of their own data, and allow the IT-instituted tools to do that globally. In doing so, the teams will be freed to make decisions based on insights from reliable, global, accessible pools of data.

The article was written by Andrew Birmingham for the Which-50 Digital Intelligence Unit for Seagate. Members of the DIU provide their insights and analysis for the benefit of our readers. Membership fees apply.

Image source: Photo by Kiefer Likens on Unsplash

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Manufacturers need to maximise the competitive opportunity of data - Which-50

New Jersey, United States,- Market Research Intellect aggregates the latest research on Data Science and Machine-Learning Platforms Market to provide a concise overview of market valuation, industry size, SWOT analysis, revenue approximation, and regional outlook for this business vertical. The report accurately addresses the major opportunities and challenges faced by competitors in this industry and presents the existing competitive landscape and corporate strategies implemented by the Data Science and Machine-Learning Platforms market players.

The Data Science and Machine-Learning Platforms market report gathers together the key trends influencing the growth of the industry with respect to competitive scenarios and regions in which the business has been successful. In addition, the study analyzes the various limitations of the industry and uncovers opportunities to establish a growth process. In addition, the report also includes a comprehensive research on industry changes caused by the COVID-19 pandemic, helping investors and other stakeholders make informed decisions.

Key highlights from COVID-19 impact analysis:

Unveiling a brief about the Data Science and Machine-Learning Platforms market competitive scope:

The report includes pivotal details about the manufactured products, and in-depth company profile, remuneration, and other production patterns.

The research study encompasses information pertaining to the market share that every company holds, in tandem with the price pattern graph and the gross margins.

Data Science and Machine-Learning Platforms Market, By Type

Data Science and Machine-Learning Platforms Market, By Application

Other important inclusions in the Data Science and Machine-Learning Platforms market report:

A brief overview of the regional landscape:

Reasons To Buy:

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

Contact Us:

Mr. Steven Fernandes

Market Research Intellect

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Tel: +1-650-781-4080

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Data Science and Machine-Learning Platforms Market 2020 Size by Product Analysis, Application, End-Users, Regional Outlook, Competitive Strategies and...

In the slow process of developing machine learning models, data scientists and data engineers need to work together, yet they often work at cross purposes. As ludicrous as it sounds, Ive seen models take months to get to production because the data scientists were waiting for data engineers to build production systems to suit the model, while the data engineers were waiting for the data scientists to build a model that worked with the production systems.

A previous article by VentureBeat reported that 87% of machine learning projects dont make it into production, and a combination of data concerns and lack of collaboration were primary factors. On the collaboration side, the tension between data engineers and data scientists and how they work together can lead to unnecessary frustration and delays. While team alignment and empathy building can alleviate these tensions, adopting some developing MLOps technologies can help mitigate issues at the root cause.

Before we dive into solutions, lets lay out the problem in more detail. Scientists and engineers (data and otherwise) have always been like cats and dogs, oil and water. A simple web search of scientists vs engineers will lead you to a lengthy debate about which group is more prestigious. Engineers are tasked with construction, operation and maintenance, so they focus on the simplest, most efficient and reliable systems possible. On the other hand, scientists are tasked with doing whatever it takes to build the most accurate models, so they want access to all the data, and they want to manipulate it in unique, sophisticated ways.

Instead of fixating on the differences, I find its much more productive to acknowledge theyre both immensely valuable and to think about how we can use each of their talents to the fullest capacity. By focusing on the things that unify data scientists and data engineers a dedication to timely, quality information and well-designed systems the two sides can foster a more collaborative environment. And by understanding each others pain points, the two teams can build empathy and understanding to make working together easier. There are also emerging tools and systems that can help bridge the gap between these two camps and help them meet more readily in the middle.

MLOps is an emerging area that applies the ideas and principles of DevOps practices to the data science and machine learning ecosystem. It lifts the burden of building and maintenance off of data engineers, while providing flexibility and freedom for data scientists. This is a win-win solution. Lets take a look at some common problems, and the tools that are emerging to more effectively solve them.

Model orchestration. The first major hurdle when trying to put a model into production is deployment: where to build it, how to host it, and how to manage it. This is largely an engineering problem, so when you have a team of data scientists and data engineers, it typically falls to the data engineers.

Building this system takes weeks, if not months time that the data or ML engineers could have spent improving data flows or improving models. Model orchestration platforms standardize model deployment frameworks and help make this step significantly easier. While companies like Facebook can invest resources in platforms like FBLearner to handle model orchestration, this is less feasible for smaller or emerging companies. Thankfully, open source systems have started to emerge to handle the process, namely MLFlow and KubeFlow. Both of these systems use containerization to help manage the infrastructure side of model deployment.

Feature stores. The second major hurdle to taking a model from the lab to production lies with the data. Oftentimes, models are trained using historical data housed in a data warehouse but queried with data from a production database. Discrepancies between these systems cause models to perform poorly or not at all and often require significant data engineering work to re-implement things in the production database.

Ive personally spent weeks building out and prototyping impactful features that never made it to production because the data engineers didnt have the bandwidth to productionize them. Feature stores, or data stores built specifically to support the training and productionization of machine learning models, are working to alleviate this issue by ensuring that data and features built in the lab are immediately production-ready. Data scientists have the peace of mind that their models are getting built, and data engineers dont have to worry about keeping two different systems perfectly in line. Larger corporations like Uber and Airbnb have built their own feature stores (Michelangelo and ZipLine respectively), but vendors that sell pre-built solutions have emerged. Logical Clocks, for example, offers a feature store for its Hopsworks platform. And my team at Kaskada is building a feature store for event-based data.

DataOps. Theres no experience quite like getting paged late at night because your model is behaving strangely. After briefly checking the model service, you come to the inevitable conclusion: something has changed with the data.

Ive had variations on the following conversation more times than I like to admit:

Finding the issue is like finding a needle in a haystack. Fortunately, new frameworks and tools are coming into place that set up monitoring and testing for data and data sources and can save valuable time. Great Expectations is one of these emerging tools to improve how databases are built, documented, and monitored. Databand.ai is another company entering the data pipeline monitoring space; in fact the company published a great blog post herethat explores in greater detail why traditional pipeline monitoring solutions dont work for data engineering and data science.

By using tools to reduce the complexity of asks and by growing empathy and trust between data scientists and data engineers, data scientists can be empowered to deliver without overly burdening data engineers. Both teams can focus on what they do best and what they enjoy about their jobs, instead of fighting with each other. These tools can help turn a combative relationship into a collaborative one where everyone ends up happy.

Max Boyd is a Data Science Lead at Kaskada. He has built and deployed models as a Data Scientist and Machine Learning Engineer at several Seattle-area tech startups in HR, finance and real estate.

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How to get your data scientists and data engineers rowing in the same direction - VentureBeat

Im pleased to announce that, after a successful run with a batch of beta test learners, Coursera has just launched my new three-course specialization, Machine Learning for Everyone. There is no cost to access this program of courses. This end-to-end course series empowers you to launch machine learning. Accessible to business-level learners and yet pertinent for techies as well, it covers both the state-of-the-art techniques and the business-side best practices. Click here to access the complete three-course series for free LEARNING OBJECTIVES After these three courses, you will be able to: Lead ML:Manage or participate in the end-to-end implementation

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The Smith School of Business at Queens University has introduced Canadas very first Fintech Masters course.

According to the Smith School of Business, the course has been designed mainly for financial or tech professionals who are already working in the industry. The first Master of Financial Innovation and Technology (MFIT) program will reportedly begin in November of this year.

The courses will be delivered during the evening and there will also be some sessions held over the weekends. Graduates of the Fintech Masters program can expect to receive professional training in data science, finance, and emerging machine learning (ML) technologies. This rigorous academic training should give them the practical knowledge they need about leading technologies which have begun to transform the global finance sector (among many other related industries).

Ryan Riordan, director of the MFIT program and distinguished finance professor and research director at the Institute for Sustainable Finance, stated:

Until now, employers hiring in the financial technology sector have had to choose between candidates who specialize in either finance or technology; its been a challenge to find talent with strengths in both who understand how one impacts the other, including the opportunities and risks.

He added:

With the launch of this new programme, weve created a unique educational path that bridges both sectors and equips graduates to succeed in a quickly evolving marketplace.

He also mentioned that the Fintech-focused curriculum will be designed specifically for students who have a background in finance. The course will also be relevant for professionals who are involved in digital transformation projects related to finance, and also for tech specialists who are interested in branching out into the finance industry.

The course will take a full year or 12 months to complete. There will be 12 classes offered via remote and in-person sessions. Courses will be scheduled in the evening, once per week, and on also on alternating weekends. This should help students meet the challenging demands of their full-time jobs while being able to learn the latest trends in Fintech.

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Fintech Masters Course, including Machine Learning, Offered by Smith School of Business at Queens University for Digital Transformation Specialists -...

A Silicon Valley startup is using machine learning to create individualized fitness plans designed to reduce injury risk.

Why it matters: Musculoskeletal injuries are a major cause of lost time for both athletes and members of the military. A platform like Sparta Science that can leverage machine learning to identify weak points before an injury could result in major health care savings.

How it works: Subjects carry out three different kinds of fitness assessments on Sparta's force plates: one involving balance, one involving the plank position and one involving a jump.

More recently Sparta Science has branched out to the military, where "non-combat-related musculoskeletal injuries" account for up to 65% of soldiers who can't deploy for medical reasons.

The bottom line: As health monitoring devices grow cheaper and more precise, expect to see similar solutions that aim to use AI to assess health individually and prevent injury and sickness.

Go deeper: How new tech raises the risk of nuclear war

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Using AI to build a more resilient soldier - Axios

Market Overview

Machine learninghas become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry.

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Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfil reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly.

The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.

Market Analysis

According to Infoholic Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 20172023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.

Segmentation by Components

The market has been analyzed and segmented by the following components Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.

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Segmentation by End-users

The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.

Segmentation by Deployment Mode

The market has been analyzed and segmented by the following deployment mode, namely public and private.

Regional Analysis

The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.

Vendor Analysis

Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.

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Benefits

The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.

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Machine Learning As A Service In Manufacturing Market: Industry Quantitative and Qualitative Insights into Present and Future Development Prospects to...