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Deci Named Market Leader in AI Software Optimization Solutions by Kisaco Research

Kisaco-Deci AI optimization market leader

According to Kisaco’s assessment, when compared to competing solutions, Deci’s deep learning platform achieved the highest score in the firm’s technical features assessment.

Tel Aviv, Israel, April 28th, 2021 – Deci, the deep learning company building the next generation of AI, was named a market leader in AI software optimization solutions (AISO) for 2021 by Kisaco Research. The study, published in “Kisaco Leadership Chart (KLC) on AI software optimization solutions 2021,” compares the leading players in the AISO market across multiple metrics, including technical features, market execution and strategy, and earning potential. According to Kisaco’s assessment, Deci was positioned firmly as a leader in the KLC, achieving the highest score in the firm’s technical features assessment.

Deci has moved rapidly for a newcomer to the field, with a strong go-to-market strategy and is also the top scoring KLC vendor in our technical assessment, with AISO suitable for both training and inference modes. Deci has advanced innovation in search for optimal neural network architectures. The solution excels in every area of our assessment.

“We are deeply grateful to be acknowledged as a market leader in AI software optimization solutions by Kisaco Research,” said Yonatan Geifman, CEO & co-founder of Deci. “AI developers across industries continue to be bogged down by increasing algorithmic complexity, a barrier for enterprises seeking to productize their solutions. To empower these developers, we took an algorithmic-first approach and created a platform that excels in improving the efficacy of AI algorithms, delivering users with models that multiply the advantages of any other hardware or software optimization solutions. We are confident that our solution frees up developers to focus on what they do best- solve the world’s most complex problems.”

Kisaco further states, “We were also impressed by Deci AI’s inclusion of ML lifecycle management capabilities in its solution (sometimes called MLOps): in our assessment Deci AI scored highest for this function. Enterprises typically struggle to move ML models from the lab to production, and lifecycle functionality helps reduce the friction in deployment, manages change, and monitors performance in production. Deci’s decision to add lifecycle support features we believe was a smart move that ensures users are able to exploit the development of ML models to best effect.”

By harnessing AI to build better AI, Deci is automatically transforming deep learning models to be ready for effective production at scale. The technology that enables this is Deci’s  groundbreaking automated neural architecture construction (AutoNAC), which Kisaco refers to as the company’s “key innovation.” AutoNAC redesigns a user’s deep learning model to squeeze maximum utilization out of the hardware targeted for inference in production. Deci’s AutoNAC engine contains a neural architecture search (NAS) component that revises a given trained model to optimally speed up its runtime by as much as 10x on any hardware, including AI chips for edge & mobile, CPUs, and GPUs, for any task, all while preserving the model’s baseline accuracy.

The recognition follows many notable milestones for Deci. Deci was named to the 2021 CB Insights AI 100 List as a top deep learning accelerator. Deci also recently announced a broad strategic collaboration with Intel Corporation to optimize deep learning inference on Intel Architecture (IA) CPUs. The two are working together to deploy innovative AI technologies to mutual customers. In 2020, Deci raised $9.1M in a seed round to further advance its deep learning platform.

This announcement was originally published on Cision PRWeb.

Download the full report profiling Deci and its status on the Kisaco Leadership Chart for AI software optimization solutions 2021.

DOWNLOAD THE REPORT

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")