ML models driving high-stakes decisions need interpretability. See how the Amazon QSL and Fidelity FCAT developed interpretable models based on Boolean logic. Read the Post on the AWS Blog Channel
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Welcome to the world of dynamically linked libraries and all the fun involved in predicting - and then manipulating - which libraries your binaries see and use … all so you can get the results and performance you expect. Dr Stephen …
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Welcome to the world of dynamically linked libraries and all the fun involved in predicting - and then manipulating - which libraries your binaries see and use … all so you can get the results and performance you expect. Dr Stephen …
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Delving into the important but unheralded optimizations that enable HPC systems to truly fly, Stephen Sachs walks us through some of the nitty gritty details that can throttle - or unleash - performance. From ancient Fortran codes pushing …
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Companies from different industries use data to help their Artificial Intelligence (AI) and Machine Learning (ML) systems make intelligent decisions. For ML systems to work well, it is crucial to make sure that the massive datasets used for …
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Nextflow is popular workflow framework for genomics pipelines, but did you know you can also use it for machine-learning? ML is already being used for medical imaging, protein folding, drug discovery, and gene editing. In this post, we …
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In this second part of using Nextflow for machine learning for life science workloads, we provide a step-by-step guide, explaining how you can easily deploy a Seqera environment on AWS to run ML and other pipelines. Read the Post on the AWS …
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Fargate is almost like having a magical compute engine. You simply provide the container image and Fargate launches it for you in seconds. No servers to manage, no infrastructure to configure. Fargate auto-scales and load balances your …
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