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Artificial Intelligence and Machine Learning Developers Survey 2021: Attitudes, Adoption Patterns and Intentions of Developers Worldwide - ResearchAndMarkets.com

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DUBLIN, October 27, 2021--(BUSINESS WIRE)--The "Artificial Intelligence and Machine Learning Survey Report 2021, Volume 1" report has been added to ResearchAndMarkets.com's offering.

This survey gives a comprehensive view of the attitudes, adoption patterns and intentions of artificial intelligence and machine learning developers worldwide.

This series focuses on tools, methodologies, and concerns related to implementing machine learning, deep learning, image recognition, pattern recognition and other forms of artificial intelligence as well as efficiently storing, handling, and analyzing large datasets and databases from a wide range of sources.

Who should buy this report?

  • Product marketing related Directors

  • AI developer program/Product marketing related Directors

  • Product related Directors

  • Emerging Tech Investors and Partners

  • AI innovation program (eg. AI start up accelerator program)

Key Topics Covered:

Overview

  • Objectives of the Survey

  • Survey Methodology

  • Research Design

  • Relative Rankings

The Sample - Artificial Intelligence and Machine Learning Developers

  • Multi-Client Survey Series

  • Custom Surveys

  • Targeted Analytics

Executive Summary

Demographics and Firmographics

  • Involvement in Software Development

  • Developer Segment

  • Job Description

  • Industry

  • Company Size

  • Company's Time in Business

  • Development Team Size

  • Data Scientists' Presence on Team

  • AI Development Focus Today

  • Nature of AI or Machine Learning Projects

  • Reasons for Working with AI on Personal Projects

  • Initial Driver for Involvement in AI

  • Tenure in AI or Machine Learning Project

Organizational AI Adoption

  • Role of AI and Machine Learning in the Enterprise

  • Barriers to AI and Machine Learning Adoption

  • Organization-wide Motivations for AI Adoption

  • Organizational Challenges to Machine Learning and Analytics

  • Familiarity with AI Project Standards

  • Adherence to AI Project Standards

  • Use of Formal DevOps Strategies in AI Projects

  • Analytics Project Coordination

Evaluation and Purchase Process

  • Involvement with Tool Purchasing

  • Technology Decision Making for AI and Machine Learning

  • Influence on Development Platform Adoption Decisions

  • Influence on Deployment Platform Adoption Decisions

  • Information Sources for Learning about AI and Machine Learning

  • Top Resources for Machine Learning

  • Biggest Barrier to Using a Vendor's API in AI Apps

  • Resources for Expanding AI Capabilities

Perceptions of AI Landscape

  • Importance of Technology Disciplines in AI Projects

  • Project Benefits from AI Integration

  • Industry Demand for AI or Machine Learning Solutions

  • Important Use Cases for Target Industries

  • Most Important Practical Applications for Machine Learning

  • Most Interesting Potential Uses for Machine Learning

  • Fear that AI Will Replace Developers' Roles

AI Practices and Techniques

  • Conceptual Differences between AI and Machine Learning

  • Impact of COVID-19 Pandemic on AI Projects

  • Types of Vertical Apps Augmented by AI and Machine Learning

  • Machine Learning - Training vs. Inference

  • AI Workloads

  • Familiarity with Neural Network Architectures

  • Optimizers for Training Deep Neural Networks

  • Regularization Techniques in Neural Networks

  • Techniques Used in Dimensionality Reduction

  • AI Analytics Methods Used

  • Most Common Uses for AI-Related Data

  • Practices Used for Maintaining Healthy Data

  • Subject Matter Expertise Requirements

  • Challenges of Introducing AI Procedures to Customer Base

  • Use of Model Zoos for Subject Matter Expertise

  • Plans for Content Generation with Generative Models

  • Plans for Content Generation with Generative Models by Company Size

  • Anticipated Content for Generative Models

Conversational Systems and Speech Recognition

  • Conversational Systems by Company Size

  • Types of Apps Targeted with Conversational Systems

  • Use of Text Classification Algorithms in Conversational Systems

  • User Interfaces Used for Conversational Systems

  • Natural Language Targeting for Conversational Systems

  • Primary Function of Conversational Systems

  • Spoken Languages Supported by Conversational Systems

  • Speech Recognition in Applications with Conversational Systems

  • Primary Purpose of Speech Recognition Project

Image Recognition and Machine Vision

  • Use of Image Recognition

  • Use of Image Recognition by Company Size

  • Industrial Use of Image Recognition

  • Workloads for Industrial Machine Vision Projects

  • Image Recognition Domain Focus

  • Text Formats in Text Recognition

  • Priority for Still vs. Motion Images

  • Origination of Image Data

Hardware and AI Development

  • Importance of Hardware to Machine Learning Projects

  • Importance of Hardware to Machine Learning Projects by Company Size

  • Top Reasons for Selecting a Hardware Platform

  • Optimizing AI Projects for Hardware Architectures

  • Chipsets Targeted for AI Optimization

  • Reason for Optimizing for CPUs

  • Reason for Optimizing for GPUs

  • Reason for Optimizing for FPGAs

  • Importance of Acceleration and Parallelization Tool Features

  • Math or Scientific Libraries Used for Machine Learning

  • Hardware Constraints in AI Development Efforts

  • Plans for a Heterogeneous Hardware Approach

  • Familiarity with Quantum Computing

  • Use of Quantum Computing Frameworks for AI Projects

  • First Important Use Case for Quantum Computing

  • First Important Use Case for Quantum Computing by Familiarity

  • Expectation for Quantum Tooling Utility

  • Expectation for Quantum Tooling Utility by Familiarity

  • Use of Parallelism for AI or Machine Learning

  • Issues with Parallel Programming

  • Resources for Implementing Parallelism

  • Task or Data Parallelism

AI and the Cloud

  • Cloud Hosting for AI or Machine Learning Tools

  • Environments Running a Typical AI Project

  • User Interaction with AI Projects

  • Endpoints of AI or Machine Learning Apps

  • Common Environments for ML Inference

  • Use of Cloud-based Backend for AI Needs

  • Top Reasons for Selecting a Cloud Platform

AI and Containerization

  • Use of Containers to Deploy AI or Machine Learning Models

  • Hosting of AI or Machine Learning Projects that Use Containers

  • Factors Determining Adoption of Containers for Deep Learning

  • Container Orchestration for AI or Machine Learning Workloads

  • Container Orchestration Tools Used

  • Kubernetes: Cloud vs. On-Prem Environments

  • Kubernetes' Services Used

Platform and Technology Adoption

  • Impact of Machine Learning on Tool and Platform Selection

  • Primary Host Operating System Today

  • Additional Development Hosts Today

  • Operating Systems Targeted Today

  • Languages Used for AI or Machine Learning

  • Most Helpful Tooling Improvements for Machine Learning

  • Use of AI and Machine Learning Frameworks

  • Top Characteristics of Machine Learning Frameworks

  • Glaring Needs in Machine Learning Libraries

Machine Learning Model Lifecycle

  • Team Members Engaged in Model Creation

  • Team Members Engaged in Training

  • Top Challenges when Training AI Models

  • Reliance on Publicly-available Datasets

  • Most Important Factors in Selecting Datasets

  • Testing Techniques for Embedded Machine Learning Models

  • Automation of Model Management

  • Measurements Used in Monitoring Models

  • Approaches to Accelerating Model Training

  • Typical Method of Deploying Models

  • Responsibility for Deploying Models to Production

  • Responsibility for Monitoring Production Models

  • Testing Retrained Models

  • Operationalization of Models

  • Methods of Storing and Sharing Models

  • Greatest Challenges to Storing and Sharing Models

  • Intersection of Data Models and DevOps Practices

  • Use of Tools to Monitor AI Models in Production

Security

  • Most Important Types of Data to Analyze for Data Security

  • Anticipated Prevalence of Attacks Targeting AI

  • Traditional Security Mechanisms and AI

  • Traditional Security Mechanisms and AI by Company Size

  • Most Helpful Vendor Resources for Securing Data

  • Primary of Posture Management vs. Threat Protection

  • Posture Management vs. Threat Protection: Primacy by Company Size

  • Most Important Aspect of AI Threat Protection

  • Most Important Aspect of AI Threat Protection by Primacy

  • Most Important Aspect of Security Posture Management

  • Most Important Aspect of Posture Management by Primacy

For more information about this report visit https://www.researchandmarkets.com/r/fi1n9x

View source version on businesswire.com: https://www.businesswire.com/news/home/20211027005551/en/

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