Search Results for "ml-so1v"

Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve.

...Productionizing language models, including LLMs, comes with a range of risks due to the infinite amount of input combinations, which can elicit an infinite amount of outputs. The unstructured nature of text poses a challenge in the ML observability space - a challenge worth solving, since the lack of visibility on the model's behavior can have serious consequences.

...Ability to capture the output of SQL queries as Pandas dataframes to interact with other Python libraries (e.g. matplotlib). Send local files or dataframes to a remote cluster (e.g. sending pretrained local ML model straight to the Spark cluster) Authenticate to Livy via Basic Access authentication or via Kerberos.

A cloud backup solution that employs the "ChaCha20 + Serpent-256 CBC + HMAC-SHA3-512" authenticated encryption scheme for data encryption and ML-KEM-1024 for quantum-resistant key exchange. Check it out at https://plum-cave.netlify.app/ GitHub page: https://github.com/Northstrix/plum-cave

An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.

Experiment tracking in SageMaker Training Jobs, Processing Jobs, and Notebooks. SageMaker Experiments is an AWS service for tracking machine learning Experiments. The SageMaker Experiments Python SDK is a high-level interface to this service that helps you track Experiment information using Python. Experiment tracking powers the machine learning integrated development environment Amazon SageMaker Studio. Experiment: A collection of related Trials. Add Trials to an Experiment that you wish to...

...It comes with a large number of modules, encompassing the whole modeling cycle: creating computational models, simulating and analyzing them, visualizing the information, in order to improve the models. All using community standards, such as SED-ML, SBML and MIRIAM.

IPTV/Video cloud Features: Cross-platform (Linux, MacOSX, FreeBSD, Raspbian/Armbian) GPU/CPU Encode/Decode/Post Processing Stream statistics CCTV Adaptive hls streams Load balancing Temporary urls HLS push EPG scanning Subtitles to text conversions AD insertion Logo overlay Video effects Relays Timeshifts Catchups Playlists Restream/Transcode from online streaming services like Youtube, Twitch Mozaic Many Outputs Physical Inputs Streaming Protocols File Formats Presets Vods/Series server-side support Pay per view channels Channels on demand HTTP Live Streaming (HLS) server-side support Public API, client server communication via JSON RPC Protocol gzip compression Deep learning video analysis Supported deep learning frameworks: Tensorflow NCSDK Caffe ML Hardware:

If you want to rename your dataset samples for ML and you might have a lot of them (you should btw) or maybe you need to set different enumeration for every .extension you have or you just want to rename some personal stuff I am glad you are here. This is how you can rename your files: - For every .extension in the target folder reNamer sets unique enumeration. - Randomly - With your set of parameters.

Framework for software component integration, interoperability and adoptability through a XML based vocabulary: Software Component Integration Mark-up Language (SCIML)