Google CloudHej Sverige! Google Cloud launches new region in Sweden
Google Cloud launches new region in Sweden, empowering businesses with cloud innovations and AI capabilities
Snorkel AIWhat is specialized GenAI evaluation, and why is it so critical to enterprise AI?
Specialized GenAI evaluation and its critical role in enterprise AI explored in detail.
SalesforceHow AWS SageMaker Inference Components Save AI Inference Costs By Up to 8X
Discover how AWS SageMaker Inference Components optimize GPU usage and save up to 8X on AI inference costs for Salesforce AI Platform.
Snorkel AIWhat is specialized GenAI evaluation, and why is it so critical to enterprise AI?
Exploring the importance of specialized GenAI evaluation for enterprise AI applications.
Apple MLDoes Spatial Cognition Emerge in Frontier Models?
Exploring Spatial Cognition Evaluation in Frontier Models with SPACE Benchmark
Apple MLM2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference
Introducing M2R2: Mixture of Multi-Rate Residuals for enhancing inference efficiency in Transformers.
AWS MLGround truth generation and review best practices for evaluating generative AI question-answering with FMEval
Guidance on best practices for generating and reviewing ground truth for evaluating generative AI question-answering using FMEval
AWS MLTime series forecasting with LLM-based foundation models and scalable AIOps on AWS
Elevate time series forecasting with Chronos and SageMaker pipelines for efficient AIOps deployment on AWS
AWS MLInnovating at speed: BMW’s generative AI solution for cloud incident analysis
BMW utilizes generative AI technology on AWS for efficient cloud incident analysis
DatabricksAnnouncing Automatic Liquid Clustering
Introducing Automatic Liquid Clustering for improved data management and query performance on Unity Catalog tables.
Apple MLTowards Automatic Assessment of Self-Supervised Speech Models Using Rank
Exploring the use of embedding rank as an unsupervised evaluation metric for self-supervised speech models.
Apple MLSpeaker-IPL: Unsupervised Learning of Speaker Characteristics with i-Vector Based Pseudo-Labels
Enhancing speaker representation quality through unsupervised learning using i-vector based pseudo-labels.