Having machine learning in a company’s 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, AI’s 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 company’s total funding to $47 million. 

For OctoML’s 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 isn’t fast? doesn’t scale? isn’t 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. Let’s take a look at couple of them:

Verta 

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.

“We’re obsessed with helping organizations get ML models into production because that’s 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.

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

“It’s [MLOps] going to be an essential component to enterprises industrializing their AI efforts in the future,” said Diego M. Oppenheimer, Algorithmia’s CEO in a recent interview with GitHub.

Databand.ai

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. Databand’s 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.

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. 

The post Machine Learning Deployment Is The Biggest Tech Trend In 2021 appeared first on Artificial Intelligence.

The key focus of Big Data in E-commerce market report is to evaluate the performance of the industry in the ensuing years to help stakeholders take better decisions and expand their business portfolio. The document highlights the key growth trends as well as the opportunities and how they can be exploited to generate maximum profits. In addition, it empowers industry partakers with methodologies that can be adopted to effectively deal with the existing and upcoming challenges. Besides, it gauges the impact of COVID-19 on this business sphere and attempts to monitor its future implications on the market scenario for a stronger realization of the growth prospects.

Key pointers from COVID-19 impact assessment:

• Socio-economic impact of COVID-19 on the global economy and Big Data in E-commerce market.
• Shifts in supply and demand share.
• Predicted long-term COVID-19 outlook on the growth of the industry.

Summary of the regional analysis:

• Geographically, the Big Data in E-commerce market is split into North America, Europe, Asia-Pacific, South America, Middle East & Africa, South East Asia.
• Contribution of each geography to the overall growth in given in the report.
• Growth rate, revenue, and sales of each regional market are discussed extensively.

Other crucial pointers from the Big Data in E-commerce market report:

• The Big Data in E-commerce market, based on the product terrain, is categorized into Cloud-based andOn-premises.
• Information regarding the estimated revenue and volume share of ever product type is documented.
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• Each application segment’s market share and predicted growth rate are thoroughly discussed.
• Leading organizations influencing the market dynamics are Dell Inc,Teradata Corp,Data Inc,Facebook,Hewlett Packard Enterprise (Hpe),Palantir Technologies, Inc,Hitachi, Ltd,Twitter,SAP Se,Whatsapp,IBM Corp,Oracle Corp,Microsoft Corp,Splunk Inc,Amazon Web Services, Inc andSAS Institute Inc.
• The study examines the mentioned firms with respect to their market share, gross margins, market remuneration, pricing pattern, production capacity, and product & service portfolio.
• The document elaborates the prevailing competition trends and their implications on businesses.
• A granular analysis of supply chain, including details about providers, consumers, as well as manufacturers is encompassed in the study.
• Moreover, the study determines the investment viability of a new project through several practices such as Porter’s Five Forces analysis and SWOT assessment.

Key features of the report:

• Intricate details of each organization.
• Information regarding market share, product sale price, manufacturing base distribution, total revenue generated, and sales.
• Latest developments of the leading players.
• Sales amassed by each company with respect to their operational areas.

Highlights of the Report:

• Accurate market size and CAGR forecasts for the period 2020-2025
• Identification and in-depth assessment of growth opportunities in key segments and regions
• Detailed company profiling of top players of the global Big Data in E-commerce market
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• Reliable industry value chain and supply chain analysis
• Comprehensive analysis of important growth drivers, restraints, challenges, and growth prospects

The scope of the Report:

The report offers a complete company profiling of leading players competing in the global  Big Data in E-commerce market with a high focus on the share, gross margin, net profit, sales, product portfolio, new applications, recent developments, and several other factors. It also throws light on the vendor landscape to help players become aware of future competitive changes in the global Big Data in E-commerce market.

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• Apart from the hottest technological advances in the global Big Data in E-commerce market, it brings to light the plans of dominant players in the industry

Table of Contents:

Industry Overview of Big Data in E-commerce Market

Industry Chain Analysis of Big Data in E-commerce Market

Manufacturing Technology of Big Data in E-commerce Market

Major Manufacturers Analysis of Big Data in E-commerce Market

Global Productions, Revenue and Price Analysis of Big Data in E-commerce Market by Regions, Manufacturers, Types, and Applications

Consumption Volumes, Consumption Value, Import, Export and Sale Price Analysis of Big Data in E-commerce by Regions

Gross and Gross Margin Analysis of Big Data in E-commerce Market

Marketing Traders or Distributor Analysis of Big Data in E-commerce Market

Global and Chinese Economic Impacts on Big Data in E-commerce Industry

Development Trend Analysis of Big Data in E-commerce Market

Contact information of Big Data in E-commerce Market

New Project Investment Feasibility Analysis of Big Data in E-commerce Market

Conclusion of the Global Big Data in E-commerce Market Industry 2020 Market Research Report

The post Big Data in E-commerce Market Size, Share 2020 By Development, Trend, Key Manufacturers appeared first on Artificial Intelligence.

This market report plots an intentional review of macroeconomic signs, parent affiliations, and new startup adventures. The report gives the customers data identified with classes, for instance, augmentation, divisions, and locales, expose type, and applications. This market report exhibits the rapidly creating conditions, the top dimension appearing at do genuine execution and settle on worthwhile decisions for advancement and prospering ahead. This market report speaks to a precise methodology of key data that would be given to customers who are searching for it. This report can guide the client to choose the correct strides in basic leadership and key plans that can be useful in the market.

This market research report joins the latest mechanical overhauls and new releases to interface with the clients to design, settle on smart business decisions, and complete their future required executions. The report focuses more on current business and developments, future framework changes, and opportunities and trends that the market is experiencing or going to experience. The report additionally portrays the primary players and how they perform in the market all through. It reveals insight into their financials, SWOT analysis, review, significant and late improvements, developments, and so on

Cloud microservices market is expected to grow at a CAGR of 21.7% in the forecast period of 2020 to 2027. Data Bridge Market Research report on cloud microservices market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecasted period while providing their impacts on the market’s growth.

The global cloud microservices market accounted for USD 631.1 million in 2017 and is projected to grow at a CAGR of 24.1% forecast to 2025.

The renowned players in cloud microservices market are

• Amazon Web Services, Inc.,
• CA Technologies.,
• IBM,
• Microsoft,
• Infosys Limited,
• NGINX Inc.,
• Oracle,
• Pivotal Software, Inc.,
• Syntel, Inc.,
• Gurock,

Marlabs Inc., RapidValue Solutions, Kontena, Inc., Macaw Software Inc.,  UNIFYED.,  Idexcel, Inc. and among others.

The titled segments and sub-section of the market are illuminated below:

• The global cloud microservices market is based on component, organization size, deployment mode, vertical and geographical segments.
• Based on component, the global cloud microservices market is segmented into platform and services. Service is sub segmented into consulting services, integration services, training, support, and maintenance services.
• Based on organization size, the global cloud microservices market is segmented into large enterprises  and small and medium-sized enterprises.
• Based on Deployment Mode, the global cloud microservices market is segmented into public cloud, private cloud and hybrid cloud.
• Based on vertical, the global cloud microservices market is segmented into retail and ecommerce, healthcare, media and entertainment, banking, financial services, and insurance, IT AND ITes, government, transportation and logistics, manufacturing, telecommunication and others).

The regions that have been considered in the study are:

North America

Europe

Asia Pacific

Latin America

Middle East and Africa

The report is inclusive of all the information that is valuable for market entrants. This will enhance the ability of the user to foresee trends and make beneficial and informed decisions. The report is also available for customization according to the requests of the user. These help in detailing the report around the regions or participants that comes under the users’ concern and targets.

Key Coverage of Report:

Total addressable market

Regional analysis [North America, Europe, Asia Pacific, Latin America, Middle East & Africa]

Country-wise market segmentation

Market size breakdown by the product/ service types

Market size breakdown by application/industry verticals/ end-users

Market share and revenue/sales of the key players in the market

Production capacity of prominent players

Market Trends like emerging technologies/products/start-ups, SWOT Analysis, Porter’s Five Forces, and others.

Pricing Trend Analysis

Brand wise ranking of the key market players worldwide

Sales Forecast:

The report contains historical revenue and volume that backing information about the market capacity, and it helps to evaluate conjecture numbers for key areas in the Cloud Microservices market. Additionally, it includes a share of each segment of the Cloud Microservices market, giving methodical information about types and applications of the market.

Reasons for Buying Cloud Microservices Market Report

This report gives a forward-looking prospect of various factors driving or restraining market growth.

It renders an in-depth analysis for changing competitive dynamics.

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It gives a six-year forecast evaluated on the basis of how the market is predicted to grow.

It assists in making informed business decisions by performing a pin-point analysis of market segments and by having complete insights of the Cloud Microservices market.

This report helps the readers understand key product segments and their future.

Which emerging technologies are believed to impact the Cloud Microservices market performance?

Which regulations that will impact the industry?

Who are the most prominent vendors and how much market share do they occupy?

What are the latest technologies or discoveries influencing the Cloud Microservices market growth worldwide?

(**NOTE: Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.)

About Data Bridge Market Research:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process. Data bridge is an aftermath of sheer wisdom and experience which was formulated and framed in the year 2015 in Pune.

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