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Welcome to CloudData Science 5. There were not as many announcements as last week in CloudData Science 4 , but quantity is not what is important. Thank you for reading the weekly news, and you can find previous editions on the CloudData Science News page. The first announcement is big!
GTC—Amazon Web Services (AWS), an Amazon.com company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced that the new NVIDIA Blackwell GPU platform—unveiled by NVIDIA at GTC 2024—is coming to AWS.
Sign Up for the CloudData Science Newsletter. Amazon Athena and Aurora add support for ML in SQL Queries You can now invoke Machine Learning models right from your SQL Queries. It is based upon this article: Preparing and curating your data for machine learning. We will have to wait and see. Announcements.
Lots of announcements this week, so without delay, let’s get right to CloudData Science 9. Google Announces Cloud SQL for Microsoft SQL Server Google’s Cloud SQL now supports SQL Server in addition to PostgreSQL and MySQL Google Opens a new Cloud Region Located in Salt Lake City, Utah, it is named us-west3.
Amazon Web Services (AWS) announced the general availability of Amazon DataZone, a data management service that enables customers to catalog, discover, govern, share, and analyze data at scale across organizational boundaries.
AWSDeepLearning Containers now support Tensorflow 2.0 AWSDeepLearning Containers are docker images which are preconfigured for deeplearning tasks. An intro to Azure FarmBeats An innovative idea to bring data science to farmers. Here are the few bits of information I could find.
In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. We first highlight how we use AWS Glue for highly parallel data processing. Data processing and model inference need to scale as our data grows. Additionally, 10.4%
OpenAI chooses PyTorch OpenAI, an organization aimed at helping artificial intelligence benefit all of humanity, has chosen to use PyTorch as its standard deeplearning framework. The post CloudData Science 6 appeared first on Data Science 101.
SageMaker has developed the distributed data parallel library , which splits data per node and optimizes the communication between the nodes. You can use the SageMaker Python SDK to trigger a job with data parallelism with minimal modifications to the training script.
Data scientists and ML engineers require capable tooling and sufficient compute for their work. Therefore, BMW established a centralized ML/deeplearning infrastructure on premises several years ago and continuously upgraded it. This results in faster experimentation and shorter idea validation cycles.
Gamma AI is a great tool for those who are looking for an AI-powered cloudData Loss Prevention (DLP) tool to protect Software-as-a-Service (SaaS) applications. The business’s solution makes use of AI to continually monitor personnel and deliver event-driven security awareness training in order to prevent data theft.
This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care.
As LiDAR sensors become more accessible and cost-effective, customers are increasingly using point clouddata in new spaces like robotics, signal mapping, and augmented reality. In this series, we show you how to train an object detection model that runs on point clouddata to predict the location of vehicles in a 3D scene.
Amazon Redshift is the most popular clouddata warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. It provides a single web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models.
Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deeplearning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.
He is responsible for defining and leading the business that extends the company’s semantic layer platform to address the rapidly expanding set of Enterprise AI and machine learning applications. Alex Watson | Co-Founder | Gretel AI Alex has been a trailblazer in the technology sector, focusing on data security and innovation.
AWS can play a key role in enabling fast implementation of these decentralized clinical trials. By exploring these AWS powered alternatives, we aim to demonstrate how organizations can drive progress towards more environmentally friendly clinical research practices.
phData’s Approach phData implemented Optical Character Recognition (OCR), which was performed using the open-source tool Paddle (Parallel Distributed DeepLearning). Migrations from legacy on-prem systems to clouddata platforms like Snowflake and Redshift. Explore phData's AI Services Today!
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