Machine Learning – Thematic Intelligence

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Machine Learning Thematic Intelligence Report Overview

Machine learning is a subset of artificial intelligence (AI) that allows computer systems to learn and improve from data without being explicitly programmed. It is the most practical application of AI currently available for enterprise adoption.

The machine learning thematic intelligence report highlights key technology trends, macroeconomic trends, and regulatory trends impacting the machine learning theme. Furthermore, it discusses machine learning value chains, mergers & acquisitions activities, and major milestones in the journey of the machine learning theme.

Report Pages 76
Regions Covered Global
Market Size (2021) $68 billion
CAGR (2019-2026) >14%
Key Trends Technology Trends, Macroeconomic Trends, and Regulatory Trends
Value Chains Hardware, Software, Services, And Use Cases
Leading Public Companies Alibaba, Alphabet (parent company of Google), Amazon, Arm, Baidu, C3.ai, IBM, Intel, Microsoft, and Nvidia
Leading Private Companies Cerebras, Cloudera, Databricks, Groq, Horizon Robotics, MakeML, Nuro, Quadric, and SparkCognition
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Machine learning: Key Trends

The key trends that are associated with the machine learning theme can be classified into three categories: technology trends, macroeconomic trends, and regulatory trends.

Technology trends: The key technological trends that will shape the machine learning theme are explainable AI (XAI), facial recognition (FR), federated learning, natural language processing (NLP), AI chips, cloud, quantum computing, cybersecurity, 5G, black box algorithms, MLOps, and artificial general intelligence (AGI).

Macroeconomic trends: The key macroeconomic trends that will shape the machine learning theme are ESG and its complex relationship with ML, China’s rise as a global AI superpower, and COVID-19 treatment methods with the use of ML.

Regulatory trends: The key regulatory trends that will shape the machine learning theme are data privacy, algorithmic bias regulation, lack of global AI regulation, antitrust, Intellectual property (IP), regulation stifling innovation, and regulation of autonomous weapons.

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Machine Learning- Value Chain Analysis

GlobalData’s machine learning value chain consists of four segments: hardware, software, services, and use cases.

Hardware: Machine learning models run on hardware components. These are essential enablers as they define the parameters within which a machine learning system can operate. The hardware dictates the speed and capacity in which data can be transferred and, therefore, the speed and latency of the machine learning system.

Machine Learning Value Chain Analysis

Machine Learning Value Chain Analysis

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Machine Learning – Competitive Landscape

Leading Public Companies Associated with the Machine Learning Theme: A few of the leading public companies that are making their mark within the machine learning theme are Alibaba, Alphabet (parent company of Google), Amazon, Arm, Baidu, C3.ai, IBM, Intel, Microsoft, and Nvidia.

Leading Private Companies Associated with the Machine Learning Theme: The leading private companies that are making their mark within the machine learning theme are Cerebras, Cloudera, Databricks, Groq, Horizon Robotics, MakeML, Nuro, Quadric, and SparkCognition.

Machine Learning Players

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Application Software Sector Scorecard

At GlobalData, we use a scorecard approach to predict tomorrow’s leading companies within each sector. Our sector scorecards have three screens: a thematic screen, a valuation screen, and a risk screen.

The enterprise security software scorecard has three screens:

  • Our thematic screen ranks companies based on overall leadership in the 10 themes that matter most to their industry, generating a leading indicator of future performance.
  • Our valuation screen ranks our universe of companies within a sector based on selected valuation metrics
  • Our risk screen ranks companies within a particular sector based on overall investment risk

Application Software Sector Scorecard – Thematic Screen

Application Software Sector Scorecard – Thematic Screen

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The machine learning thematic research report also covers the sector scorecard for:

  • Cloud Services Sector Scorecard

Scope

  • This report provides an overview of the machine learning theme.
  • It identifies the key trends impacting growth of the theme over the next 12 to 24 months.
  • It includes a comprehensive industry analysis, including market size and growth forecasts for AI hardware, AI platforms, AI consulting and support services, and specialized AI applications.
  • The detailed value chain breaks down machine learning into three areas: hardware, software (big data management and machine learning techniques), and services (platforms, MLaaS, and libraries).

Reasons to Buy

Machine learning will benefit all enterprises in some capacity, with potential advantages including automation, trend and pattern recognition, process improvement, and forecasting. This report will help readers make sense of the machine learning theme, understand training techniques and leading algorithms, the business benefits, identify the leading vendors and startups, and understand MLaaS, MLOps, and machine learning libraries.

Accton
Adtran
Alibaba
Alphabet
Alteryx
Amazon
AMD
AMS
Arista
Armadillo
Armis
Baidu
Baidu
BigML
Blaize
Brain Corp
BrainChip
Broadcom
Broadcom
C3.ai
Cambricon
Cavium
Centec Networks
Cerebras
Cerebras Systems
Chainer
Check Point Software
Ciena
Cisco
Cisco
Cloud Software Group
Cloudera
CrowdStrike
Darktrace
Dataiku
DataRobot
Dell
Ericsson
Esperanto
Eta Compute
Extreme Networks
FANN
FireEye
First Sensor
Flux
Flybits
Fortinet
Fritz AI
Fujitsu
Google
Google (TensorFlow)
GrAI Matter Labs
Graphcore
Groq
GyrFalcon
H2O.ai
Honeywell
Horizon Robotics
HPE
Huawei
IBM
iFlytek
Infineon
Informatica
Innatera Nanosystems
Inspur
Intel
Juniper Networks
Keras
Keras
Lenovo
Lumen Technologies
MakeML
Marvell
Matplotlib
McAfee
MediaTek
Megvii
Meta (PyTorch)
Micron
Microsoft
Microsoft
Mythic
NLTK
Nokia
NoviFlow
Numpy
Nvidia
NXP
Okta
OpenNN
Oracle
Palantir
Palo Alto Networks
Pandas
Pluribus
Pure Storage
QCT
Qualcomm
Qualcomm
Quanta
Rockwell Automation
RunwayML
SambaNova
Samsung Electronics
Samsung Electronics
SAP
Scikit-learn
Seagate
Securonix
SenseTime
Senseye
SK Hynix
Sony
SparkCognition
SparkCognition
Splunk
Statsmodels
STMicroelecontrics
Sugon
Supermicro
SynSense
TDK
TE Connectivity
TensTorrent
Texas Instruments
Torch
Toshiba
Trend Micro
TSMC
University of Montreal (Theano)
Vdoo
Vodafone
Webflow
Western Digital
Xiaomi
ZTE

Table of Contents

Executive Summary

Players

Technology Briefing

Trends

• Technology trends

• Macroeconomic trends

• Regulatory trends

Industry Analysis

• Market size and growth forecasts

• Mergers and acquisitions

• Venture financing

• Patent trends

• Company filing trends

• Hiring trends

• Use cases

• Timeline

Value Chain

• Hardware

• Software

• Services

Companies

Sector Scorecards

• Application software sector scorecard

• Cloud services sector scorecard

Glossary

Further Reading

Our Thematic Research Methodology

About GlobalData

Contact Us

Frequently asked questions

Machine Learning – Thematic Intelligence thematic reports
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