AI in Fashion Market Consumption and Revenue Analysis by Top Key Company's (AWS, Microsoft, IBM, Google, Oracle, Vue.ai, Lily AI, mode.ai, Stitch Fix, Wide Eyes, Huawei) | Forecast to 2025

Overview of Global AI in Fashion Market:

This report provides in-depth study of "Global AI in Fashion Market 2020" using SWOT analysis i.e. Strength, Weakness, Opportunities, and Threat to the organization. The AI in Fashion Market report also provides an in-depth survey of key players in the market organization.

Some of the significant key factors driving the growth of the Global AI in Fashion Market are customer's demand for a personalized experience, increasing need for inventory management and the growing influence of social media in the fashion industry. In addition, Identifying future fashion trends in advance with customer's buying behaviour analysis and Growing fast fashion retail to boost the adoption of AI in fashion industry marketing to create many opportunities for vendors of AI in fashion solutions.

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Firstly, the report provides a basic overview of the Market including its definition, applications and manufacturing technology. Then, the report explores the international and Chinese major Market players in detail. In this part, the report presents the company profile, product specifications, capacity, production value, and 2020-2025 market shares for each company.

In terms of the regional analysis, North America is expected to hold the largest market size during the forecast period. Asia-Pacific region shows rapid growth in the market, owing to various reasons, such as growing social media adoption and the expansion of local enterprises and the government initiatives that are focused on advancements in the AI technology. Investments by tech giants are also expected to be fuelling the growth of AI-based solutions across the region.

In the end, the report makes some important proposals for a new project of AI in Fashion Market before evaluating its feasibility. Overall, the report provides an in-depth insight of 2013-2025 global AI in Fashion market covering all important parameters.

Global Key Vendors:

1 Microsoft

2 IBM

3 Google

4 AWS

5 SAP

6 Facebook

7 Adobe

8 Oracle

8 Vue.ai

9 Lily AI

10 Wide Eyes

11 FINDMINE

12 Catchoom

13 Huawei

14 Intelistyle

15 Pttrns.ai and More.................

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Product Type Segmentation

1 Apparel

2 Accessories

3 Footwear

4 Beauty and Cosmetics

5 Jewelry and Watches

Industry Segmentation

1 Fashion Designers

2 Fashion Stores (Online and Offline Brand Stores)

Worldwide Technology Industry News:

Amazon Web Services (AWS) (April 24, 2020) - AWS Announces General Availability of Amazon Augmented Artificial Intelligence (A2I) - Today, Amazon Web Services (AWS), an Amazon.com company, announced the general availability of Amazon Augmented Artificial Intelligence (A2I), a fully managed service that makes it easy to add human review to machine learning predictions to improve model and application accuracy by continuously identifying and improving low confidence predictions. Amazon A2I helps developers add human review for model predictions to new or existing applications using reviewers from Mechanical Turk, third party vendors, or their own employees. Amazon A2I makes it easier for developers to build the human review system, structure the review process, and manage the human review workforce. For example, developers could use Amazon A2I to quickly spin up and manage a workforce of humans to review and validate the accuracy of machine learning predictions for an application that extracts financial information from scanned mortgage documents or an application that uses image recognition to identify counterfeit items online, so that the quality of results improve over time. There are no upfront commitments to use Amazon A2I, and users pay only for each review needed.

Today, machine learning provides highly accurate predictions (known as "inferences") for a variety of use cases, including identifying objects in images, extracting text from scanned documents, or transcribing and understanding spoken language. In each case, machine learning models provide an inference and a confidence score that expresses how certain the model is in its prediction. The higher the confidence number, the more the result can be trusted. Typically, when developers receive a high confidence result they can trust that the prediction is accurate, and, depending on the use case, they can use it to fully automate a process. For example, developers of a social media application that matches a user's photos to celebrity faces might rely on an 80% confidence score to generate and return a lot of entertaining matches. However, there are other times when it is strongly recommended to have both high confidence (up to 99%) and human review, such as public safety use cases involving law enforcement. In situations where the confidence score is lower than desired and/or human judgment is required, reviews can be used to validate the prediction. This interplay between machine learning and human reviewers is critical to the success of machine learning systems, but human reviews are challenging and expensive to build and operate at scale, often involving multiple workflow steps, operating custom software to manage human review tasks and results, and recruiting and managing large groups of reviewers. As a result, developers sometimes spend more time managing the human review process than building the intended application, or they have to forego having human reviews, which leads to less confidence in deploying applications that utilize machine learning.

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Major Points in Table of Contents:

Global AI in Fashion Market Report 2020

1 AI in Fashion Product Definition

2 Global AI in Fashion Market Manufacturer Share and Market Overview

2.1 Global Manufacturer AI in Fashion Shipments

2.2 Global Manufacturer AI in Fashion Business Revenue

2.3 Global AI in Fashion Market Overview

2.4 COVID-19 Impact on AI in Fashion Industry

3 Manufacturer AI in Fashion Business Introduction

3.1 Microsoft (US) AI in Fashion Business Introduction

3.2 IBM (US) AI in Fashion Business Introduction

3.3 Google (US) AI in Fashion Business Introduction

3.4 AWS (US) AI in Fashion Business Introduction

3.5 SAP (Germany) AI in Fashion Business Introduction

3.6 Facebook (US) AI in Fashion Business Introduction

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