Category: Amazon Machine Learning

In this blog, you’ll learn how Neurons Lab, an AWS Advanced Tier Services Partner, collaborated with Peak Defence to automate compliance processes using Amazon Bedrock with Anthropic Claude 3 model and Amazon Sagemaker. The generative AI solution streamlines cybersecurity audits and RFP responses, reducing time and resources required. It covers architectural considerations, operationalization with AWS services, LLM evaluation, and continuous improvement.

By Vanitha Jayasuriya, Full Stride Cloud Solution Lead, Germany – Wipro By Yedu Kuruvath, Alliances and Partner Development Lead, AI Practice – Wipro By Shaban Saddique, AWS Business Group Director, Europe – Wipro By Benson Philip, Sr Partner Development Manager, EMEA – AWS By Bindhu Chinnadurai, Sr Partner Solutions Architect, EMEA – AWS Wipro Introduction […]

In this exclusive interview for our blog, we sat down with Joaquin Lippincott, the CEO of Metal Toad, to delve into the innovative Securitoad Machine Learning Security SaaS solution. Joaquin generously shared his expertise on how they strategically built their latest offering on the AWS Marketplace. His insights offer invaluable lessons for software providers aiming to embrace this modern delivery mode.

Snowflake By Daniel Wirjo, Solutions Architect – AWS By Benny Chun, Solutions Architect – AWS By Bosco Albuquerque, Sr. Partner Solutions Architect – AWS By Hans Siebrand, Cloud Data Architect – Snowflake By Matt Marzillo, Sr. Partner Engineer – Snowflake With recent advancements, building a data platform to provide a data foundation for generative AI […]

HCLSoftware’s automation product, HCL Workload Automation (HWA), now integrates with AWS Step Functions. This integration offers a comprehensive automation solution, streamlining complex workflows across cloud and on-premises environments. It enables organizations to automate more use cases with increased efficiency, scalability, and reliability, utilizing the robust AWS ecosystem of services. This strategic partnership empowers customers to transform their IT landscape through centralized, cloud-native automation.

In April, Amazon Web Services (AWS) unveiled a suite of groundbreaking features for Amazon Bedrock and Amazon Q, ushering in a new era of generative AI capabilities. Learn how AWS Partners are leveraging the latest Amazon Bedrock and Amazon Q features to transform how they build, scale, and deploy intelligent applications—unlocking unprecedented opportunities for innovation and efficiency.

Arcanum AI migrated its generative AI workloads from OpenAI to AWS using a two-phase model evaluation process. Open-source LLMs were tested out-of-the-box and with customized prompts, scored by experts, and evaluated against existing use cases. Amazon Bedrock provided a private network and access control for handling sensitive client data. AWS’s AI services enabled Arcanum to deploy top-performing LLMs securely in clients’ VPCs, outperforming OpenAI models while meeting security needs.

AWS HealthImaging is a new HIPAA-eligible service for storing, analyzing, and sharing medical imaging data securely in the cloud. CitiusTech developed a solution leveraging AWS services like HealthImaging to automate ingesting DICOM data. It scans for malware, validates DICOM files, copies clean images to HealthImaging for storage, and notifies users. Healthcare providers can easily migrate imaging workloads to realize improved accessibility and cost-efficiency.

Large language models like Amazon Bedrock can generate MongoDB queries from natural language questions, transforming how users access NoSQL databases. By leveraging AI and language models, this solution allows business users to query MongoDB data through conversational English instead of code. It connects to MongoDB with PyMongo, generates queries with LangChain and Bedrock, retrieves and formats results into natural language answers.

MLOps applies DevOps principles to machine learning, enabling organizations to efficiently develop, deploy, and manage models at scale. Eviden’s 10-step MLOps assessment examines existing models, establishes governance, creates self-service access, scales data analysis, registers models, enables feature re-use, provides data access, tests models at scale, deploys models, and enables API access. This end-to-end approach streamlines model creation and deployment while ensuring governance and consistency.