AI and machine learning developers usually count on language and speech-based Application Programmer Interfaces (APIs).
Up to 50% of them are saying that gathering or generating data is the most challenging aspect of regular train and tune AI versions.
They are also saying that the difficulty of managing operations is the biggest challenge they encounter when working on AI applications.
Evans Data Corporation’s newest study of AI and machine learning development is giving us an insight into the challenges developers encounter when building enterprise-level, high-quality AI apps. The study, Artificial Intelligence and Machine Learning 2019, Volume 2 is based on interviews with 500 AI and machine learning developers from around the world. Paying attention in particular on the attitudes, adoption patterns and intentions of AI and machine learning developers worldwide, the 187-page research is considered one of the most inclusive and complete studies of its kind. What makes the study so impressive is the depth of research about AI and machine learning developer’s challenges today.
Key aspects from the study include the next things:
55.9% of AI and machine learning developers use language APIs, followed by speech (51.1%). Developers worked on multiple types of APIs for AI and machine learning improvement. What caught our interest in this survey’s results is the increasing popularity of conversation and data discovery APIs, voice-activated assistants are now an important part of mainstream AI and machine learning software development.
Not having quality tools is slowing down AI and Machine Learning app development today. The most significant obstacle AI and machine learning developers are facing in improving AI app development also includes the prices of materials and the absence of necessary skills or training. Just 10% have to confront the challenges of working on this field and integrating into the legacy systems, a number that indicates that AI and machine learning app development is a relatively new business field.
38% of AI developers state that the complexity of managing operations is the biggest challenge when working with AI applications. The second most notable challenge is developing applications that are portable across deployment environments. Choosing the right AI framework is the third greatest challenge they encounter in creating high-quality AI apps. AI and machine learning frameworks consist of libraries of mathematical expressions and functions for different machine learning and deep learning functions. They usually have a broad base of APIs and other development tools that are made to assist developers in integrating into previous code and capitalizing on enterprise systems for the data they need to train models and produce the app.
A lot of developers (54.9%) are relying on private cloud infrastructure for hosting their AI, machine learning, and deep learning development. 46% are using the public cloud infrastructure, with just over 51% using their organizations’ on-premise infrastructure. Many cloud service providers have developed their cloud-based environments that incorporate an array of usual AI tools, including machine learning or deep learning frameworks, data-science-specific IDEs, and machine learning notebooks.
