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Machine Learning - Thematic Intelligence

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    Report

  • 76 Pages
  • December 2022
  • Region: Global
  • GlobalData
  • ID: 5706969
Machine learning is a subset of artificial intelligence (AI) that allows computer systems to learn and improve from data without being explicitly programmed. Machine learning is the most practical application of AI currently available for enterprise adoption.

Key Highlights

  • GlobalData forecasts the global AI market will be worth $136 billion in 2026. Specialist AI applications will account for the largest proportion of 2026 revenue at 37%, followed by AI consulting and support services at 30%. AI platforms will record the fastest revenue growth between 2021 and 2026 (a CAGR of 18%)
  • Instead of companies employing programmers to design machine learning tools from scratch, nocode/low-code and machine learning as a service (MLaaS) platforms allow those without extensive programming ability to design systems tailored to their needs. This has also led to the popularity of machine learning operations (MLOps) to ensure systems are implemented and monitored to a high standard
  • AI is increasingly involved in life-changing decisions like welfare payments, mortgage approvals, and medical diagnoses. Consequently, transparency and explainability have become essential. Some key AI frameworks driving transparency in the sector include IBM's open-source AI 360 tool kit and Rulex's Logic Leaning Machine (LLM)
  • The main areas driving AI M&A deals are NLP, automated driving, MLaaS, and enterprise predictive analytics
  • US-based machine learning companies have raised a total of $57,960 million through 3,038 venture financing deals in the last 10 years

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

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 the Publisher
  • Contact the Publisher

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • 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