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Showing 16 open source projects for "post"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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NannyML
Detecting silent model failure. NannyML estimates performance
...NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance. -
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Triton Inference Server
The Triton Inference Server provides an optimized cloud
...Triton delivers optimized performance for many query types, including real-time, batched, ensembles, and audio/video streaming. Provides Backend API that allows adding custom backends and pre/post-processing operations. Model pipelines using Ensembling or Business Logic Scripting (BLS). HTTP/REST and GRPC inference protocols based on the community-developed KServe protocol. A C API and Java API allow Triton to link directly into your application for edge and other in-process use cases. -
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pycm
Multi-class confusion matrix library in Python
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. -
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Stable Baselines3
PyTorch version of Stable Baselines
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. ... -
Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
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TensorFlow Model Optimization Toolkit
A toolkit to optimize ML models for deployment for Keras & TensorFlow
...Enable execution on and optimize for existing hardware or new special purpose accelerators. Choose the model and optimization tool depending on your task. In many cases, pre-optimized models can improve the efficiency of your application. Try the post-training tools to optimize an already-trained TensorFlow model. Use training-time optimization tools and learn about the techniques. -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads to scale separately from the serving logic. Adaptive batching dynamically groups inference requests for optimal performance. ... -
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Kaggle Solutions
Collection of Kaggle Solutions and Ideas
...Each competition entry typically includes information about the dataset, evaluation metrics, modeling strategies, and techniques used by high-ranking competitors. The repository also highlights important machine learning concepts such as feature engineering, cross-validation strategies, ensemble modeling, and post-processing methods commonly used in winning solutions. Because the content is organized by competition categories such as computer vision, natural language processing, tabular data, and time-series forecasting, users can explore techniques relevant to specific problem types. -
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AI Explainability 360
Interpretability and explainability of data and machine learning model
...The complete API is also available. There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. -
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sense2vec
Contextually-keyed word vectors
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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Merlion
A Machine Learning Framework for Time Series Intelligence
Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting, anomaly detection, and change point detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
...There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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RoboSat
Semantic segmentation on aerial and satellite imagery
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual... -
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Five video classification methods
Code that accompanies my blog post outlining five video classification
Classifying video presents unique challenges for machine learning models. As I’ve covered in my previous posts, video has the added (and interesting) property of temporal features in addition to the spatial features present in 2D images. While this additional information provides us more to work with, it also requires different network architectures and, often, adds larger memory and computational demands.We won’t use any optical flow images. This reduces model complexity, training time, and... -
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Lip Reading
Cross Audio-Visual Recognition using 3D Architectures
The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the... -
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ExoPlanet
GUI based toolkit for running common Machine Learning algorithms.
...ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model.
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