Except they only came to a few departments, mainly the traditionally IT ones. According to Luke Marsden, CEO and founder of Dotscience, enterprise-grade artificial intelligence (AI) and machine learning is being held back, in part, because the mathematicians and statisticians behind it are stuck in the days of Waterfall -- still emailing code back and forth to each other. Dotscience, a collaboration tool for end-to-end machine learning data and model management, recently ran a survey of enterprises. The resulting State of Development and Operations of AI Applications 2019 included results like how 63.2% of businesses reported they are spending between $500,000 and $10 million on their AI efforts. But 60.6% of respondents continued to experience operational challenges.

Dotscience, the pioneer in DevOps for machine learning (ML), today announced its schedule of presentations at upcoming industry events through the end of 2019. Dotscience will be exhibiting its DevOps for ML platform, which provides collaborative, end-to-end ML data and model management. Dotscience helps simplify, accelerate and control AI and ML models through the entire model development lifecycle. Dotscience makes it possible for ML engineering to be just as simple, fast and safe as modern software engineering when using DevOps techniques. The company sees a parallel between the challenges of software development in the 1990s and the current state of ML model management today.

Despite significant investment in AI, many companies are still struggling to stabilize and scale their AI initiatives, according to Dotscience. While 63.2% of businesses reported they are spending between $500,000 and $10 million on their AI efforts, 60.6% of respondents continue to experience a variety of operational challenges. This is evidenced by the fact that 64.4% of organizations deploying AI said that it is taking between seven to 18 months to get their AI workloads from idea into production, illustrating the slow, unpredictable nature of AI projects today. The State of Development and Operations of AI Applications 2019 report findings are based on a survey of 500 industry professionals. The research examines the AI maturity of businesses based on the practical business applications of machine learning, the tools and processes being used to develop, deploy and monitor machine learning models and the scalability and stability of their AI initiatives.