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Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference.
Machine learning is a pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.
In the financial services space, personalization and security are key factors in which companies utilize AI/ML.
Insurance companies can offer better dynamic services; Banks & payment platforms use it for fraud detection; every company can provide faster & better experiencies with automated document processing and chatbots implementation.
AI is being of great help for the healthcare industry through streamlined diagnoses and improved clinical outcomes.
It uses technologies such as computer vision and image recognition to analyze radiology images and help diagnose diseases; provide personalized based on patients medical history and genetic information; identify patterns and trends from large volumes of medical data to treat diseases and prevent them in patients at risk.
AI/ML is key in the manufacturing space, helping optimize process to the fullest.
Demand prediction and supply chain management are probably the most common examples.
Near real-time data gathering and processing to prevent accidents and improve employee’s security; transforming collective data into insights for better quality products, are all solutions that can use already existing data to help business fullfill their mission.
The agroindustry is evolving rapidly with custom-made AI/ML solutions.
Real time crop monitoring; image analysis for pest control; advanced video analysis for livestock recognition, are some of the regular operations getting better, faster and cheaper with AI/ML.
As the use of ML applications continues to grow, so does the demand for managing and optimizing compute, storage, and networking resources, with considerations for usage patterns, cost-effectiveness, and operational efficiency. Selecting the right compute infrastructure becomes crucial to mitigate power consumption, control costs, and streamline the complexities involved in training and deploying ML models in a production environment.
AWS customers have access to virtually unlimited compute, network, and storage so they can scale. You can scale up or down as needed from one GPU or ML accelerator to thousands, and terabytes to petabytes of storage. Using the cloud, you don’t need to invest in all possible infrastructure. Instead, take advantage of elastic compute, storage, and networking.
Support for popular ML frameworks
AWS computing instances support major ML frameworks such as TensorFlow and PyTorch. They also support model libraries and toolkits such as Hugging Face for a broad range of ML use cases. The AWS Deep Learning AMIs (AWS DLAMIs) and AWS Deep Learning Containers (AWS DLCs) come pre-installed with optimizations for ML frameworks and toolkits to accelerate deep learning in the cloud.
With a broad choice of infrastructure services, you can choose the right infrastructure for your budget. AWS Trainium-based Amazon EC2 Trn1 instances deliver 50% savings on training costs and AWS Inferentia2-based Amazon EC2 Inf2 instances deliver up to 40% better price performance than comparable Amazon EC2 instances. You can re-invest these cost-savings to accelerate innovation and grow your business.
Easy to use
Access purpose-built ML accelerators such as AWS Trainium and AWS Inferentia to train and deploy foundation models (FMs) and integrate them into your applications using AWS managed services such as Amazon SageMaker and Amazon Bedrock.
Power your ML application with the highest performing ML infrastructure from AWS. Amazon EC2 P4d and Amazon EC2 Trn1 instances are ideal for high performance ML training. For inference, Amazon EC2 Inf2 instances, powered by second-generation Inferentia2 offer 4x higher throughput and up to 10x lower latency than previous generation Inferentia-based instances.
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