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Low code and no code for AI Business benefits of platforms About us: At viso.ai, we power Viso Suite , the leading no-code/low-code computervision platform. Our technology is used by leaders worldwide to rapidly develop, deploy and scale real-time computervision systems. Get a demo for your organization.
These models range from lightweight tree-based models to deep learning computervision models, which need to run on GPUs to achieve low latency and improve the user experience. This approach was initially used for all company services, including microservices that run expensive computervision ML models.
In this series, we walk you through the process of architecting and building an integrated end-to-end MLOps pipeline for a computervision use case at the edge using SageMaker, AWS IoT Greengrass, and the AWS Cloud Development Kit (AWS CDK). So if you have a DevOps challenge or want to go for a run: let him know.
She has a diverse background, having worked in many technical disciplines, including softwaredevelopment, agile leadership, and DevOps, and is an advocate for women in tech. He holds an MSEE from the University of Michigan, where he worked on computervision for autonomous vehicles.
James’s work covers a wide range of ML use cases, with a primary interest in computervision, deep learning, and scaling ML across the enterprise. Prior to joining AWS, James was an architect, developer, and technology leader for over 10 years, including 6 years in engineering and 4 years in marketing & advertising industries.
The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Amazon Rekognition offers pre-trained and customizable computervision (CV) capabilities to extract information and insights from your images and videos.
He has touched on most aspects of these projects, from infrastructure and DevOps to softwaredevelopment and AI/ML. After earning his bachelors degree in software engineering and a masters in computervision and machine learning from Polytechnique Montreal, Philippe joined AWS to put his expertise to work for customers.
The softwaredevelopment landscape is constantly evolving, driven by technological advancements and the ever-growing demands of the digital age. Over the years, we’ve witnessed significant milestones in programming languages, each bringing about transformative changes in how we write code and build software systems.
It has intuitive helpers and utilities for modalities like computervision, natural language processing, audio, time series, and tabular data. About the authors Fred Wu is a Senior Data Engineer at Sportradar, where he leads infrastructure, DevOps, and data engineering efforts for various NBA and NFL products.
The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.
AI for DevOps to infuse AI/ML into the entire softwaredevelopment lifecycle to achieve high productivity. Libraries Collecting, labeling, and cleaning data for computervision is a pain. There is a good number of companies using Dall-E to create various products such as Mixtiles , Cala.
Version control for code is common in softwaredevelopment, and the problem is mostly solved. ” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. My Story DevOps Engineers Who they are?
Qing Lan is a SoftwareDevelopment Engineer in AWS. He specializes in machine learning, AI, and computervision domains, and holds a master’s degree in computer science from UT Dallas. In his spare time, he enjoys seeking out new cultures, new experiences, and staying up to date with the latest technology trends.
About the Authors Tzahi Mizrahi is a Solutions Architect at Amazon Web Services with experience in cloud architecture and softwaredevelopment. His expertise includes designing scalable systems, implementing DevOps best practices, and optimizing cloud infrastructure for enterprise applications.
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