Remove Data Processing Remove Deep Learning Remove Publishing Remove Testing
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Deep learning for improved breast cancer monitoring using a portable ultrasound scanner

Insight

Segmentation Since a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%). The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. The box plot below shows a summary of the testing results.

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Will enterprises soon keep their best gen AI use cases under wraps?

CIO Business Intelligence

Helping software developers write and test code Similarly in tech, companies are currently open about some of their use cases, but protective of others. They now use what they learn about a program to help build unit tests. And unit tests are too tedious for humans to build reliably.

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10 Things AWS Can Do for Your SaaS Company

Smart Data Collective

With these tools, your SaaS can: Merge and improve the application code constantly Automate the development, testing, and release of software Integrate operations and developer workflows And much more. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Easy to use.

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Where Programming, Ops, AI, and the Cloud are Headed in 2021

O'Reilly on Data

This study is based on title usage on O’Reilly online learning. The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. It’s particularly difficult if testing includes issues like fairness and bias.

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Building a scalable online product recommender with Keras, Docker, GCP, and GKE

Insight

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). In this project, I was curious to see if deep learning approaches?—?specifically

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On-Demand Spark clusters with GPU acceleration

Domino Data Lab

Maintaining the cluster and the underlying infrastructure configuration can be a complex and time-consuming task Lack of GPU acceleration – Complex machine workloads, especially the ones involving Deep Learning, benefit from GPU architectures that are well adapted for vector and matrix operations. GPU) and use bitnami/spark:2.4.6

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. Managing Machine Learning Projects” (AWS).