The text discusses the rapid adoption of large language models (LLMs), such as GPT NeoX and Pythia, on AWS Trainium for training and fine-tuning. It highlights their performance, training steps, cost analysis, and comparisons to Nvidia A100 GPU. The authors’ expertise and roles are also outlined, showcasing their contributions to AI and deep learning.
Vodafone is transitioning to a technology company by 2025, aiming to have 50% of its workforce involved in software development. They are partnering with Accenture and AWS to build a cloud platform and develop ML skills through the AWS DeepRacer challenge, with the goal of improving customer satisfaction and digital services. The initiative has seen…
The text discusses integrating Amazon Comprehend and Amazon Kendra to enrich enterprise search capabilities. Structured and unstructured data are rapidly growing, and using custom metadata helps categorize information. Amazon Comprehend can identify document types and entities, which Amazon Kendra then uses to filter search results, including facets for better searching. The solution is particularly applied…
Protopia AI and AWS have partnered to provide a tool called Stained Glass Transform (SGT), enabling businesses to deploy large language models (LLMs) securely without compromising data privacy. SGT protects sensitive information in prompts and fine-tuning data by converting them into randomized re-representations, preserving usability and accuracy. This facilitates responsible AI implementation and competitive advantage…
Getir, established in 2015, is a leading ultrafast grocery delivery company with a multinational presence. Utilizing Amazon SageMaker and AWS Batch, they reduced model training time by 90% and improved operational efficiency. Their data science team developed a product category prediction pipeline with an 80% accuracy rate, aiding commercial teams in inventory management and competitive…
AWS’s suite of low-code and no-code ML tools, such as Amazon SageMaker Canvas, enables rapid, cost-effective machine learning model development without requiring coding expertise. Deloitte uses these tools to expedite project delivery and take on more clients, increasing accessibility and standardization while reducing time and costs, resulting in roughly 30-40% productivity gains in ML development…
Generative AI is rapidly transforming customer experiences, with many companies launching applications on AWS, including major brands and startups. AWS is democratizing advanced generative AI technology, making it more accessible and secure across three layers of infrastructure, model building, and applications, such as Amazon CodeWhisperer and the newly introduced Amazon Q for professional assistance. Upcoming…
Amazon SageMaker is a fully managed service that simplifies building, training, and deploying ML models. It offers API deployment, containerization, and various deployment options including AWS SDKs and AWS CLI. New Python SDK improvements and SageMaker Studio interactive experiences streamline model packaging and deployment. Features include multi-model endpoints, price-performance optimization, and deployment without prior SageMaker…
Amazon SageMaker has launched two new features to streamline ML model deployment: the ModelBuilder in the SageMaker Python SDK and an interactive deployment experience in SageMaker Studio. These features automate deployment steps, simplify the process across different frameworks, and enhance productivity. Additional customization options include staging models, extending pre-built containers, and custom inference specification.
Digital publishers use machine learning for faster content creation, ensuring relevant images match articles. Amazon’s Titan Multimodal Embeddings model generates image and text embeddings for semantic search. This streamlines finding appropriate images, without keywords, by comparing metadata similarity—enhancing media workflows while maintaining quality. Amazon Bedrock simplifies AI application development for various modalities.
Amazon SageMaker Canvas now features extensive data preparation tools from SageMaker Data Wrangler, offering an intuitive no-code solution for data professionals to prepare data, build, and deploy machine learning models without coding. Users can import from 50+ sources, use 300+ built-in analyses, and balance datasets using natural language commands. This integration streamlines the journey from…
Large Language Models (LLMs) are influential tools in various applications such as conversational agents and content generation. Responsible and robust evaluation of these models is essential to prevent misinformation and bias. Amazon SageMaker Clarify simplifies LLM evaluation by integrating with SageMaker Pipelines, enabling scalable and efficient model assessments using structure configurations. Users, including model providers,…
SageMaker’s new ‘smart sifting’ feature filters less informative data during training, potentially reducing deep learning model training costs by up to 35%. This online data sifting process requires no changes to existing training pipelines and aims to maintain model accuracy while improving cost-efficiency.
Amazon SageMaker Studio offers a managed environment for developing, training, and deploying ML models, with the ability to run notebooks as scheduled jobs. SageMaker Pipelines now includes notebook jobs as a step, enabling data scientists to create complex, multi-step ML workflows. With the Python SDK, these workflows can be programmed and managed via SageMaker Studio,…
AWS is focused on responsibly developing generative AI, prioritizing safety, fairness, and security through innovations like Amazon CodeWhisperer with security scanning, Amazon Titan for content management, and privacy with Amazon Bedrock. Collaborations, customer engagement, and new tools like Guardrails and Model Evaluation on Amazon Bedrock enable safe scaling of AI, embedding safeguards against disinformation and…
The AWS Generative AI Innovation Center, launched in June 2023, has assisted numerous clients in creating custom AI solutions. Starting Q1 2024, the new Custom Model Program will enable customers to fine-tune Anthropic Claude models with their own data through Amazon Bedrock. The program offers specialized support from AI experts for tailored model optimization.
Artificial intelligence (AI) has the potential to improve society, and the adoption of AI technologies has accelerated. Amazon has launched generative AI services like Amazon Bedrock and CodeWhisperer to unlock the capabilities of generative AI. Assessing and managing the risks associated with AI systems is crucial. Risk management frameworks can benefit organizations by improving decision-making,…
Amazon announces the expansion of its EC2 accelerated computing portfolio with three new instances powered by NVIDIA GPUs: P5e instances with H200 GPUs, G6 instances with L4 GPUs, and G6e instances with L40S GPUs. These instances provide powerful infrastructure for AI/ML, graphics, and HPC workloads, along with managed services like Amazon Bedrock, SageMaker, and Elastic…
Amazon SageMaker has released a new version (0.25.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs) with support for NVIDIA’s TensorRT-LLM Library. This upgrade provides improved performance and efficiency for large language models (LLMs) on SageMaker. The new LMI DLCs offer features such as continuous batching support, efficient inference collective operations, and quantization techniques.…
AWS has announced updates to its AI services, including language support and summarization capabilities. Amazon Transcribe now supports over 100 languages, improving accuracy and adding features like automatic punctuation and speaker diarization. Amazon Transcribe Call Analytics offers generative AI-powered call summarization, saving time for agents and managers. Amazon Personalize introduces the Content Generator, allowing companies…