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Artificial Intelligence and Machine Learning 2020, Volume 2

  • ID: 5310360
  • Report
  • June 2020
  • Region: Global
  • 190 Pages
  • Evans Data Corp

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.

Artificial intelligence is permeating software development in many ways and many industries, which necessitates a thorough knowledge of how developers are doing this.

This volume includes research and analysis covering topics such as developer demographics and firmographics, artificial intelligence landscape, methods and approaches, resources and services, conversational systems, speech and image recognition, enterprise AI, security, platform adoption, API frameworks, tools and languages, technology adoption, hardware, hardware optimization, parallelism, and high-performance computing, purchasing and influencers, challenges and barriers to success, AI as it relates to IoT, the Cloud, and containerization and more.

 Survey Sample - Artificial Intelligence and Machine Learning Developers  
This survey consists of 406 in-depth interviews conducted in English with qualified AI and machine learning developers worldwide. This provides a margin of error of 4.7%. 

Note: Product cover images may vary from those shown
  • Overview
    • Objectives of the Survey 
    • Survey Methodology  
    • Research Design 
    • Relative Rankings
    • The Sample - Artificial Intelligence and Machine Learning Developers 
  • What’s New
    • The EDC Panel
    • Multi-Client Survey Series 
    • Custom Surveys
    • Targeted Analytics
  • Executive Summary
  • Demographics & Firmographics 
    • Involvement in Software Development 
    • Developer Segment 
    • Industry
    • Job Description
    • Company Size
    • Development Team Size 
    • Presence of Data Scientists on Development Teams 
    • Companies’ Years in Business  
    • AI Development Focus Today  
    • AI Development Focus in Two Years  
    • Development Prior to Working with AI
    • Involvement with Tool Purchasing
    • Technology Decision Making for AI and Machine Learning 
    • Resources Used for AI Familiarity 
  • The Current AI Landscape
    • Tenure in AI or Machine Learning Project 
    • Current Status of AI Projects 
    • Length of Development Lifecycle for AI/ML Projects 
    • Users of Production AI Projects 
    • Use of Formal DevOps Strategies in AI Projects
    • Use of Development Pipeline for AI Projects  
    • Machine Learning - Training vs. Inference  
    • Typical AI Workloads
    • Types of Vertical Apps Augmented by AI and Machine Learning
    • Resources for Expanding AI Capabilities  
    • AI Analytics Methods Used  
    • Types of Applications Used in Reinforcement Learning  
    • Familiarity with Neural Network Architectures  
    • Regularization Techniques in Neural Networks
    • Familiarity with Robotic Process Automation (RPA)
    • Use of RPA in Deployments
  • Motivations and Challenges in AI Projects
    • Industry Demand for AI or Machine Learning Solutions
    • Important Use Cases for Target Industries  
    • Most Important Use Cases for Developers’ Organizations
    • Organization-wide Motivations for AI Adoption 
    • Importance of Factors Relating to AI.
    • Project Benefits from AI Integration  
    • Skill Improvement for AI Development 
    • Barriers to AI and Machine Learning Adoption  
    • Challenges in AI Implementation 
    • Pain Points in Current Development
    • Pain Points by Typical AI Workloads 
    • Top Challenges When Developing AI Apps
    • Top Challenges when Training AI Models 
    • Most Important Vendor Resources for AI or Machine Learning 
    • Rationale for Integrating Vendors’ APIs into your AI Apps
  • Frameworks, Libraries, and Models  
    • Use of AI and Machine Learning Frameworks.
    • Missing Aspects from Machine Learning Libraries 
    • Top Characteristics of Machine Learning Frameworks
    • Use of Frameworks vs. Custom-made Algorithms for Machine Learning  
    • Intermediate Model Storage Format Use.
    • Primary Reason for Using Intermediate Model Storage Formats
    • Determining Factors in Specific Storage Formats 
    • Primary Reason for Not Using Intermediate Model Storage Formats
    • Optimizers for Training Deep Neural Networks
    • Approaches to Accelerating Model Training  
    • Testing Retrained Models
    • Testing Techniques for Embedded Machine Learning Models  
  • Hardware in AI and Machine Learning 
    • Importance of Hardware to Machine Learning Projects  
    • Types of Compilers Used in AI Projects 
    • Preference for Compilers that Optimize for Specific Frameworks 
    • Preference for Compilers Optimized for Frameworks by AI Workload
    • Preference for Compilers that Optimize for Specific Architectures  
    • Preference for Compilers Optimized for Architectures by AI Workload 
    • Top Reasons for Selecting a Hardware Platform 
    • Reasons for Hardware Platform Selection by Typical AI Workloads 
    • Performance vs. Efficiency in Compute Intensive Projects  
    • Optimizing AI Projects for Hardware Architectures  
    • Types of Chipsets Targeted with Hardware Optimizations
    • Processor Memory Requirements for Datasets  
    • Importance of Computational Performance versus Portability
    • Performance vs. Portability by Typical AI Workloads 
    • AI’s Impact on Demand for Computational Performance 
    • AI’s Impact on Demand for Performance by Typical AI Workloads 
    • AI’s Impact of AI on Demand for Portability 
    • AI’s Impact on Demand for Portability by Typical AI Workloads  
    • Importance of Acceleration and Parallelization Tool Features  
    • Chipset Optimization Preference 
    • Math or Scientific Libraries Used for Machine Learning  
    • Hardware Constraints in AI Development Efforts  
    • Plans for a Heterogeneous Hardware Approach 
    • Use of Specific Heterogeneous Hardware Technologies
  • Conversational Systems and Speech Recognition 
    • Types of Apps Targeted with Conversational Systems 
    • Use of Text Classification Algorithms in Conversational Systems.
    • User Interface Used for Conversational Systems 
    • Types of Inputs for Conversational Systems 
    • Natural Language Targeting for Conversational Systems 
    • Addressing Multiple Natural Languages  
    • Primary Function of Conversational Systems
    • Spoken Languages Supported by Conversational Systems 
    • SDKs Adopted for Intelligent Virtual Assistants
    • Speech Recognition in Applications with Conversational Systems  
    • Primary Purpose of Speech Recognition Project 
  • Image Recognition and Machine Vision
    • Use of Image Recognition 
    • Industrial Use of Image Recognition
    • Workloads for Industrial Machine Learning Projects 
    • Image Recognition Domain Focus
    • Text Formats in Text Recognition 
    • Priority for Still vs. Motion Images
    • Types of Images Captured 
    • Origination of Image Data 
    • Most Promising Use Case for Image Recognition
    • Most Promising Use Case for Image Recognition by Use of Image Recognition
    • Experience with Image Recognition Tooling
  • AI and the Cloud
    • Cloud Hosting for AI or Machine Learning Tools 
    • Endpoints of AI or Machine Learning Apps
    • Common Environments for Inference 
    • Environments for Inference by Typical AI Workloads 
    • Use of a Cloud-based Backend for AI Needs. 
    • Top Reasons for Selecting a Cloud Platform 
  • Containerization and Container Orchestration
    • Use of Containers to Deploy AI or Machine Learning Models 
    • Hosting of AI or Machine Learning Projects that Use Containers
    • Adoption of Deep-learning Containers 
    • Container Orchestration for AI or Machine Learning Workloads  
    • Container Orchestration Tools Used
  • AI and the Internet of Things
    • Intersection of AI and IoT
    • Types of IoT Development Intersecting with Developers’ Projects  
    • Machine Learning in Connected-device Projects. 
    • IoT Project Benefits from Machine Learning 
    • Additional Requirements for IoT Eventing  
    • Nature of IoT Eventing Requirements 
  • Security 
    • Traditional Security Mechanism and Unstructured Data
    • Primary Determinants of Security Standards  
    • Use of AI or Machine Learning for Security
    • Data Privacy Measures Built into AI Projects 
    • Regulatory Issues Impacting AI Projects  
  • Data Management and Data Science  
    • Awareness of End-to-end Cloud Platforms for Data Science
    • Appeal of End-to-end Data Science Platforms 
    • Data Scientists’ Freedom to Work on Modeling 
    • Techniques Used in Dimensionality Reduction  
    • Use of DataOps to Manage Healthy Data 
    • Greatest Challenge in DataOps 
    • Practices Used for Maintaining Healthy Data
  • Development Platforms and Platform Targets
    • Primary Host Operating System Today 
    • Native vs. Virtual Primary Development Host 
    • Additional Development Hosts Today  
    • Additional Development Hosts Next Year 
    • Operating Systems Targeted Today  
    • Impact of Machine Learning on Tool and Platform Selection 
    • Machine Learning’s Impact by Machine Learning Developers 
    • Machine Learning’s Impact by Deep Learning Developers 
    • Machine Learning’s Impact by Algorithmic AI Developers
  • Technology Adoption
    • Typical Development Environment
    • Typical Development Environments by Typical AI Workloads  
    • Technologies Used in AI Projects 
    • Languages Used for AI or Machine Learning  
    • JVM Framework Use 
    • Satisfaction with Tools for Specific Language 
    • Satisfaction with Library Availability for Specific Languages
    • Satisfaction with Library Quality for Specific Languages
  • Subject Matter Expertise
    • Need for Subject Matter Expertise Outside of Development
    • Entities Responsible for Subject Matter Expertise
    • Subject Matter Expertise and Consumer-focused Projects
    • Development Support for Additional Subject Matter Expertise 
    • Use of Model Zoos for Subject Matter Expertise 
Note: Product cover images may vary from those shown

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