Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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Google Cloud Timeseries Insights API
Anomaly detection in time series data is essential for the day-to-day operation of many companies. With Timeseries Insights API Preview, you can gather insights in real-time from your time-series datasets. Get everything you need to understand your API query results, such as anomaly events, forecasted range of values, and slices of events that were examined. Stream data in real-time, making it possible to detect anomalies while they are happening. Rely on Google Cloud's end-to-end infrastructure and defense-in-depth approach to security that's been innovated for over 15 years through consumer apps like Gmail and Search. At its core, Timeseries Insights API is fully integrated with other Google Cloud Storage services, providing you with a consistent method of access across storage products. Detect trends and anomalies with multiple event dimensions. Handle datasets consisting of tens of billions of events. Run thousands of queries per second.
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Nixtla
Nixtla is a platform for time-series forecasting and anomaly detection built around its flagship model TimeGPT, described as the first generative AI foundation model for time-series data. It was trained on over 100 billion data points spanning domains such as retail, energy, finance, IoT, healthcare, weather, web traffic, and more, allowing it to make accurate zero-shot predictions across a wide variety of use cases. With just a few lines of code (e.g., via their Python SDK), users can supply historical data and immediately generate forecasts or detect anomalies, even for irregular or sparse time series, and without needing to build or train models from scratch. TimeGPT supports advanced features like handling exogenous variables (e.g., events, prices), forecasting multiple time-series at once, custom loss functions, cross-validation, prediction intervals, and model fine-tuning on bespoke datasets.
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Alibaba Cloud Model Studio
Model Studio is Alibaba Cloud’s one-stop generative AI platform that lets developers build intelligent, business-aware applications using industry-leading foundation models like Qwen-Max, Qwen-Plus, Qwen-Turbo, the Qwen-2/3 series, visual-language models (Qwen-VL/Omni), and the video-focused Wan series. Users can access these powerful GenAI models through familiar OpenAI-compatible APIs or purpose-built SDKs, no infrastructure setup required. It supports a full development workflow, experiment with models in the playground, perform real-time and batch inferences, fine-tune with tools like SFT or LoRA, then evaluate, compress, accelerate deployment, and monitor performance, all within an isolated Virtual Private Cloud (VPC) for enterprise-grade security. Customization is simplified via one-click Retrieval-Augmented Generation (RAG), enabling integration of business data into model outputs. Visual, template-driven interfaces facilitate prompt engineering and application design.
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