Search Results for "ml"

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.

Cloud Storage FUSE is an open-source user-space file system adapter that allows Google Cloud Storage buckets to be mounted and accessed as if they were local file systems on a machine. This approach enables applications to interact with cloud storage using standard file system semantics, eliminating the need to rewrite code to use object storage APIs directly. The tool is particularly valuable in data-intensive workflows such as machine learning, where large datasets can be accessed on...

Unlock the power of big data with OpenDataDiscovery Platform. Experience seamless end-to-end insights, powered by unprecedented observability and trust - from ingestion to production - while building your ideal tech stack! Democratize data and accelerate insights. Find data that fits your use case and discover hints left by your peers to leverage existing knowledge. Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase....

Standard IT tools applied to SCADA/IoT (MongoDB, PostgreSQL/TimescaleDB,Node.js, C#, Golang, Grafana, etc.). MongoDB as the real-time core database, persistence layer, config store, SOE historian. Portability and interoperability over Linux, Windows, x86/64, ARM. Horizontal scalability, from a single computer to big clusters (MongoDB-sharding), Bare Metal, Docker containers, VM, cloud, or hybrid deployments. Unlimited tags, servers, and users. HTML5 Web interface. UTF-8/I18N. Protocols:...

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:

Create JIT common virtual machine into which famous language like java,microsoft ml,python etc can be translate.

The MyNOS project aims at developing a new and different operating system, based on high-level languages. MyNOS will be based on the OCaml bytecode interpreter. We'd like to explore microkernel architecture and new language definition too.

XML Universal Packet Archiver (PKT) is a platform independant universal file archiving system with pluggable options for encryption, encoding, compression, parity/RAID protection and more.

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

Bibgrep indexes and searches BibTex files for entries matching a given query. Its usage is similar to the command ``grep'' and the queries uses a Google-like syntax.

Command line client to test broadband connections and report results to the site http://www.grenouille.com/

Amylase is a set of tools/libraries written in Java, which converts various structured text files such computer programs/DTD files into HTML or XML documents. The name, Amylase, comes from a word ML-ize, where ML stands for Markup Language as in XML.

Dirfreak is a file organizational tool which catagorizes files using various methods, including file name, size, and date. Methods of sorting can be specified on either the command line or in a configuration file, allowing automation of the sorting task.

shadowk kernel aim to be a clean portable object micro-kernel. Clean and extensible would be most too important directions for this development.

Infect is a network traffic classification tool, that classifies TCP protocols. The classification is based on ML techniques. Scen: Train/Recognize arbitrary protocols (SSH, HTTPS..), Detect policy violations (e.g. tunnels)