Dynatrace achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category.
This designation reflects AWS’s recognition that Dynatrace has demonstrated deep experience and proven customer success building AI-powered solutions on AWS to help some of the world’s largest organizations accelerate digital transformation.
Dynatrace’s AI and automation in AWS and hybrid-cloud environments delivers speed and efficiency, enabling IT, DevOps, and SRE teams to innovate faster and optimize customer experiences.
Mike Maciag, CMO at Dynatrace, said: “The Dynatrace platform delivers out-of-the-box automatic and intelligent observability, which dramatically reduces manual and repetitive tasks and accelerates results, whether that is speed and quality of innovation for development, automation and efficacy for operations, or optimization and consistency of user experiences and business outcomes.”
“Many companies are reinventing themselves using AWS ML and AI. We are delighted to welcome Dynatrace as an inaugural AWS Partner in our newly expanded AWS Machine Learning Competency Program,” said Julien Simon, Global AI & ML Evangelist, AWS. “Dynatrace’s innovation-focused solutions, powered and vetted by AWS, and its proven track record of helping customers, will undoubtedly help many other customers transform their business.”
AI and ML-driven applications are maturing rapidly and creating new demands for enterprises. AWS is keeping pace and continuously evolving AWS Competency Programs to allow customers to engage enhanced AWS Partner technology and consulting offerings. AWS launched two new Categories within the AWS Machine Learning Competency to help customers easily and confidently identify and engage highly specialized AWS Partners with Applied AI and/or ML Ops capabilities. With this program expansion, customers will be able to go beyond the current data processing and data science platform capabilities and find experienced AWS Partners who will help productionize successful models (ML Ops) and find off-the-shelf packages for their business problems (Applied AI).
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