IoT AI and ML Services: Deploy, Learn, and Monetize Image

IoT AI and ML Services: Deploy, Learn, and Monetize

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Actionable Benefits

  • Compare the products and services for IoT Machine Learning (ML) and Artificial Intelligence (AI) technology offering of 18 IoT vendors.
  • Identify the opportunities and challenges of the AI &ML market for the IoT and leverage the penetration strategies for the sales/strategy teams.
  • Analyze the strategy, position, differentiation, and competitive outlook of the leading ML/AI vendors for the IoT.
  • Identify current and future trends in IoT advance analytics and machine learning markets, with revenue forecasts from 2018 until 2026 for IoT data-enabled value chain.

Critical Questions Answered

  • How the ML/AI offerings are positioned in the IoT-data enabled value chain?
  • What are the disruptive and future trends in machine learning deployment for the edge, fog, and cloud for the IoT?
  • Who is dominating edge centric & cloud-centric IoT ML and AI vendors?

Research Highlights

  • A detailed breakdown of IoT analytics value chain components.
  • Comprehensive analysis of IoT ML strategies of AWS, Google, IBM, Azure, Huawei, Oracle, Crosser, Ekkono, FogHorn, Swim.ai, Seeq, C3.ai, Uptake, DataRobot, Dataiku, Falkonry, Nokia SpaceTime Insight, Noodle.ai, Fetch.ai, and Pythian and others.
  • Detailed technical and commercial overview of IoT advance analytics and machine learning technologies and evaluation of its commercial growth capabilities comparatively to the other components of the IoT-data enabled value chain.

Who Should Read This?

  • IoT Big Data vendors and software developers for IoT analytics, who needs to understand the market dynamics and identify differentiation point among competitors.
  • Industrial players, who intend/ongoing the IoT digital transformation, to understand advanced data processing offerings, strengths and avoid vendors lock-in.
  • S-Suite and strategic advisors within the data-enabled industry who are responsible for strategy formation, business development, and innovative solutions planning.

Table of Contents

Table of Contents

1. EXECUTIVE SUMMARY

2. INTRODUCTION

2.1. The IoT Perspective: Defining AI and ML

3. VALUE CHAIN OVERVIEW AND DEFINITIONS

3.1. ML/AI at the Edge
3.2. ML/AI in the CLoud

4. MARKET OVERVIEW

4.1. Drivers and Trends
4.2. Challenges

5. VENDOR OVERVIEW AND PROFILES

5.1. ML/AI at the Edge

6. ML/AI IN THE CLOUD AND ON-PREMISES

6.1. End-to-End Platforms
6.2. Specialized PaaS and SaaS

7. FORECASTS

7.1. Methodology