Alternatives to TorchMetrics

SportsEngine Motion gives you complete control over your class-based business or swim club. You can do this from a mobile app and dashboard. You can easily view all your business's finances in one dashboard. You can also charge, track and collect payments for events, competitions and class registrations. You can always stay informed by monitoring your finances and payments through the SportsEngine Motion mobile app and website. You can create and run detailed financial reports that are exportable to see almost anything, such as the status of accounts, incoming funds, past-due account, and more. Accept and automatically process credit cards and access reports to view the status of each payment.

Amazon Elastic Inference allows for low-cost GPU-powered acceleration to Amazon EC2 instances and Sagemaker instances, or Amazon ECS tasks. This can reduce the cost of deep learning inference by up 75%. Amazon Elastic Inference supports TensorFlow and Apache MXNet models. Inference is the process by which a trained model makes predictions. Inference can account for as much as 90% of total operational expenses in deep learning applications for two reasons. First, standalone GPU instances are usually used for model training and not inference. Inference jobs typically process one input at a time and use a smaller amount of GPU compute. Training jobs can process hundreds of data samples simultaneously, but inference jobs only process one input in real-time. This makes standalone GPU-based inference expensive. However, standalone CPU instances aren't specialized for matrix operations and are therefore often too slow to perform deep learning inference.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Azure Databricks allows you to unlock insights from all your data, build artificial intelligence (AI), solutions, and autoscale your Apache Spark™. You can also collaborate on shared projects with other people in an interactive workspace. Azure Databricks supports Python and Scala, R and Java, as well data science frameworks such as TensorFlow, PyTorch and scikit-learn. Azure Databricks offers the latest version of Apache Spark and allows seamless integration with open-source libraries. You can quickly spin up clusters and build in an Apache Spark environment that is fully managed and available worldwide. Clusters can be set up, configured, fine-tuned, and monitored to ensure performance and reliability. To reduce total cost of ownership (TCO), take advantage of autoscaling or auto-termination.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

Deep learning frameworks like TensorFlow and PyTorch, Torch and Torch, Theano and MXNet have helped to increase the popularity of deep-learning by reducing the time and skills required to design, train and use deep learning models. Fabric for Deep Learning (pronounced "fiddle") is a consistent way of running these deep-learning frameworks on Kubernetes. FfDL uses microservices architecture to reduce the coupling between components. It isolates component failures and keeps each component as simple and stateless as possible. Each component can be developed, tested and deployed independently. FfDL leverages the power of Kubernetes to provide a resilient, scalable and fault-tolerant deep learning framework. The platform employs a distribution and orchestration layer to allow for learning from large amounts of data in a reasonable time across multiple compute nodes.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

Code for CodeT5, A new pre-trained encoder/decoder model that is code-aware. Identifier-aware pre-trained encoder/decoder models. This is the official PyTorch version of the EMNLP paper 2021 from Salesforce Research. CodeT5-large ntp-py was optimized for Python code generation and used as the foundation model in our CodeRL. This yielded new SOTA results for the APPS Python Competition-Level Program Synthesis benchmark. This repository contains the code to reproduce the experiments in CodeT5. CodeT5 is an encoder-decoder pre-trained model for programming language, which has been pre-trained with 8.35M functions from 8 programming languages: Python, Java, JavaScript PHP, Ruby Go, C#, C, and C#. It achieves the best results in a benchmark for code intelligence - CodeXGLUE. Generate code from the natural language description.

Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

Distributed AI is a computing paradigm which does away with the need to move large amounts of data and allows data to be analyzed at the source. IBM Research has developed a set RESTful web services that provide data and AI algorithms for distributed AI APIs. These APIs are designed to support AI applications in hybrid cloud, distributed, or edge computing environments. Each Distributed AI API addresses the challenges of enabling AI in distributed or edge environments using APIs. The Distributed AI APIs don't focus on the core requirements of creating and deploying AI pipes, such as model training and model servicing. You can use any of your favorite open-source programs such as TensorFlow and PyTorch. You can then containerize your application including the AI pipeline and deploy these containers to the distributed locations. To automate the deployment process, it is often useful to use a container orchestrator like Kubernetes and OpenShift operators.

Bayesforge™ is a Linux image that curates all the best open source software available for data scientists who need advanced analytical tools as well as quantum computing and computational math practitioners who want to work with QC frameworks. The image combines open source software such as D-Wave and Rigetti, IBM Quantum Experience, Google's new quantum computer language Cirq and other advanced QC Frameworks. Qubiter, our quantum compiler and fog modeling framework can be cross-compiled to all major architectures. The Jupyter WebUI makes all software accessible. Its modular architecture allows users to code in Python R and Octave.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

Groq's mission is to set the standard in GenAI inference speeds, enabling real-time AI applications to be developed today. LPU, or Language Processing Unit, inference engines are a new end-to-end system that can provide the fastest inference possible for computationally intensive applications, including AI language applications. The LPU was designed to overcome two bottlenecks in LLMs: compute density and memory bandwidth. In terms of LLMs, an LPU has a greater computing capacity than both a GPU and a CPU. This reduces the time it takes to calculate each word, allowing text sequences to be generated faster. LPU's inference engine can also deliver orders of magnitude higher performance on LLMs than GPUs by eliminating external memory bottlenecks. Groq supports machine learning frameworks like PyTorch TensorFlow and ONNX.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

RoBERTa is based on BERT's language-masking strategy. The system learns to predict hidden sections of text in unannotated language examples. RoBERTa was implemented in PyTorch and modifies key hyperparameters of BERT. This includes removing BERT’s next-sentence-pretraining objective and training with larger mini-batches. This allows RoBERTa improve on the masked-language modeling objective, which is comparable to BERT. It also leads to improved downstream task performance. We are also exploring the possibility of training RoBERTa with a lot more data than BERT and for a longer time. We used both existing unannotated NLP data sets as well as CC-News which was a new set of public news articles.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

Gemini models are a family of light-open, state-of-the art models that was created using the same research and technology as Gemini models. These models include comprehensive security measures, and help to ensure responsible and reliable AI through selected data sets. Gemma models have exceptional comparative results, even surpassing some larger open models, in their 2B and 7B sizes. Keras 3.0 offers seamless compatibility with JAX TensorFlow PyTorch and JAX. Gemma 2 has been redesigned to deliver unmatched performance and efficiency. It is optimized for inference on a variety of hardware. The Gemma models are available in a variety of models that can be customized to meet your specific needs. The Gemma models consist of large text-to text lightweight language models that have a decoder and are trained on a large set of text, code, or mathematical content.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

Amazon SageMaker JumpStart can help you speed up your machine learning (ML). SageMaker JumpStart gives you access to pre-trained foundation models, pre-trained algorithms, and built-in algorithms to help you with tasks like article summarization or image generation. You can also access prebuilt solutions to common problems. You can also share ML artifacts within your organization, including notebooks and ML models, to speed up ML model building. SageMaker JumpStart offers hundreds of pre-trained models from model hubs such as TensorFlow Hub and PyTorch Hub. SageMaker Python SDK allows you to access the built-in algorithms. The built-in algorithms can be used to perform common ML tasks such as data classifications (images, text, tabular), and sentiment analysis.

AWS Deep Learning AMIs are a secure and curated set of frameworks, dependencies and tools that ML practitioners and researchers can use to accelerate deep learning in cloud. Amazon Machine Images (AMIs), designed for Amazon Linux and Ubuntu, come preconfigured to include TensorFlow and PyTorch. To develop advanced ML models at scale, you can validate models with millions supported virtual tests. You can speed up the installation and configuration process of AWS instances and accelerate experimentation and evaluation by using up-to-date frameworks, libraries, and Hugging Face Transformers. Advanced analytics, ML and deep learning capabilities are used to identify trends and make forecasts from disparate health data.

Deep learning, 3D modelling, simulations and distributed analytics take days or even weeks. GPUonCLOUD’s dedicated GPU servers can do it in a matter hours. You may choose pre-configured or pre-built instances that feature GPUs with deep learning frameworks such as TensorFlow and PyTorch. MXNet and TensorRT are also available. OpenCV is a real-time computer-vision library that accelerates AI/ML model building. Some of the GPUs we have are the best for graphics workstations or multi-player accelerated games. Instant jumpstart frameworks improve the speed and agility in the AI/ML environment through effective and efficient management of the environment lifecycle.

The increased demand for computing power is too much for conventional CPUs. GPU processors process data at speeds 100-200x faster than conventional CPUs. We offer servers that are designed specifically for machine learning or deep learning, and are equipped with unique features. Modern hardware based upon the NVIDIA®, GPU chipset. This has a high operating speed. The latest Tesla® V100 card with its high processing power. Optimized for deep-learning software, TensorFlow™, Caffe2, Torch, Theano, CNTK, MXNet™. Includes development tools for Python 2, Python 3 and C++. We do not charge extra fees for each service. Disk space and traffic are included in the price of the basic service package. Our servers can also be used to perform various tasks such as video processing, rendering etc. LeaderGPU®, customers can now access a graphical user interface via RDP.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

TFlearn, a modular and transparent deep-learning library built on top Tensorflow, is modular and transparent. It is a higher-level API for TensorFlow that allows experimentation to be accelerated and facilitated. However, it is fully compatible and transparent with TensorFlow. It is an easy-to-understand, high-level API to implement deep neural networks. There are tutorials and examples. Rapid prototyping with highly modular built-in neural networks layers, regularizers and optimizers. Tensorflow offers full transparency. All functions can be used without TFLearn and are built over Tensors. You can use these powerful helper functions to train any TensorFlow diagram. They are compatible with multiple inputs, outputs and optimizers. A beautiful graph visualization with details about weights and gradients, activations, and more. The API supports most of the latest deep learning models such as Convolutions and LSTM, BiRNN. BatchNorm, PReLU. Residual networks, Generate networks.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow.

The prompt engineering platform allows you to experiment with different prompt versions. You can also evaluate and compare prompts in a series of tests, optimize prompts by one click, share and more. Optimize your AI development workflow. Key features that help you identify and get the best prompts for production use cases. Evaluation allows for a side-by-side comparison between prompts in test cases. Import test cases from CSV and define custom metrics for evaluation. Automatic template and prompt optimization can improve LLM results. View and manage all versions of the prompt and create OpenAI Functions. You can access all your prompts programmatically. This includes observability and analytics. Calculate the cost, latency and effectiveness of each prompt. Parea can help you improve your prompt engineering workflow. Parea helps developers improve the performance of LLM apps by implementing rigorous testing and versioning.

Selector's software-as-a-service employs machine learning and NLP-driven, self-serve analytics to provide instant access to actionable insights and reduce MTTR by up to 90%. Selector Analytics uses machine learning and artificial intelligence to perform three essential functions and provide actionable insight to network, cloud, or application operators. Selector Analytics can collect any data, including configurations, alerts and metrics, events, logs, and logs, from heterogeneous sources. Selector Analytics can harvest data from router logs or device or network metrics. Selector Analytics collects data and normalizes, filters and clusters it. Selector Analytics also uses pre-built workflows that allow for actionable insights. Selector Analytics then uses machine-learning-based data analytics to analyze metrics and events and detect anomalies.

Tox aims automate and standardize Python testing. It is part of a larger vision to simplify the packaging, testing, and release of Python software. Tox is a generic virtualenv management tool and test command-line tool that you can use to verify that your package works with different Python versions. You can also run your tests in each environment. It also acts as a frontend for continuous integration servers, reducing boilerplate and merging CI. First, install tox using pip install tox. Next, add basic information about your project as well as the test environments that you would like your project to run into a tox.ini.py file. To generate a tox.ini automatically, run tox-quickstart. Answer a few questions. Test your project against Python2.7 or Python3.6.

TensorBoard, TensorFlow’s comprehensive visualization toolkit, is designed to facilitate machine-learning experimentation. It allows users to track and visual metrics such as accuracy and loss, visualize the model graph, view histograms for weights, biases or other tensors over time, display embeddings in a lower-dimensional area, and display images and text. TensorBoard also offers profiling capabilities for optimizing TensorFlow programmes. These features provide a suite to help understand, debug and optimize TensorFlow, improving the machine learning workflow. To improve something in machine learning, you need to be able measure it. TensorBoard provides the measurements and visualisations required during the machine-learning workflow. It allows tracking experiment metrics, visualizing model graphs, and projecting embedded embeddings into a lower-dimensional space.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

Zabbix is the ultimate enterprise software that allows you to monitor millions of metrics from thousands of virtual machines, servers, and network devices. Zabbix is free and open-source. Automatically detect problem states in the incoming metrics flow. You don't have to constantly look at the incoming metrics. The native web interface offers multiple ways to present a visual overview about your IT environment. Zabbix Event correlation mechanism will help you focus on the root cause of a problem and save you thousands of repetitive notifications. Automate monitoring large, dynamic environments. Integrate Zabbix into any part of your IT environment. Access all Zabbix functionality via the Zabbix API.

HyperConnect is an enterprise-grade, open-source Internet-of-Things framework. It uses the Elastos Peer-to-Peer Carrier Network to route traffic between IoT devices. The modular architecture is a solid foundation for any industry in the Internet-of-Things. The Internet of Things is all about communication, data transfer, and storage. You can use visual contexts to help you understand data, recognize patterns, and obtain relevant metrics. With the built-in compiler, you can create, manage and validate Python scripts for low-level sensors. Real-time data collection from multiple inputs. Automatically generate meaningful information. Monitor and control multiple sensors and devices remotely or locally in a secure and simple way. The Graphical User Interface (GUI), allows maximum flexibility and minimal coding. Secure peer-to-peer communication is possible for the entire Internet-of-Things network. This allows you to own your data.

RetailFlux people-counting software is both affordable and uses the most advanced analytic technology on the market. Our proprietary Artificial Intelligence (AI), FluxVision technology is the basis of RetailFlux people counting solutions. FluxVision's AI core ensures best-in-class data quality and the lowest possible implementation cost. Our AI software platform transforms regular CCTV cameras into the most accurate and versatile counter devices in the world. RetailFlux people counters have unique features such as staff exclusion, occupancy, and shopping time metrics. Conversion reports and shopper footfall are the most important metrics in brick-and-mortar retail management. To evaluate the performance of stores, the most important KPI is conversion rate. This can be combined with visitor counting numbers.

NVIDIA TensorRT provides an ecosystem of APIs to support high-performance deep learning. It includes an inference runtime, model optimizations and a model optimizer that delivers low latency and high performance for production applications. TensorRT, built on the CUDA parallel programing model, optimizes neural networks trained on all major frameworks. It calibrates them for lower precision while maintaining high accuracy and deploys them across hyperscale data centres, workstations and laptops. It uses techniques such as layer and tensor-fusion, kernel tuning, and quantization on all types NVIDIA GPUs from edge devices to data centers. TensorRT is an open-source library that optimizes the inference performance for large language models.

Daft is an ETL, analytics, and ML/AI framework that can be used at scale. Its familiar Python Dataframe API is designed to outperform Spark both in terms of performance and ease-of-use. Daft integrates directly with your ML/AI platform through zero-copy integrations of essential Python libraries, such as Pytorch or Ray. It also allows GPUs to be requested as a resource when running models. Daft is a lightweight, multithreaded local backend. When your local machine becomes insufficient, it can scale seamlessly to run on a distributed cluster. Daft supports User-Defined Functions in columns. This allows you to apply complex operations and expressions to Python objects, with the flexibility required for ML/AI. Daft is a lightweight, multithreaded local backend that runs locally. When your local machine becomes insufficient, it can be scaled to run on a distributed cluster.

Reduce false positives by using machine learning (ML), to detect anomalies in business metrics. Grouping outliers that are similar can help you identify the root cause of any anomalies. Summarize root causes, and rank them according to severity. Integrate AWS databases, storage services and third-party SaaS apps seamlessly to monitor metrics and detect anomalies. Automate the sending of customized alerts and taking appropriate actions when anomalies are detected. Automatically detect anomalies in metrics and identify their root causes. Lookout for Metrics uses ML for diagnosing and detecting anomalies in business and operational data. It is difficult to detect unexpected anomalies using traditional methods that are manual and error-prone. Lookout for Metrics uses ML without the need for any artificial intelligence (AI). You can identify unusual variances in subscriptions and conversion rates so you can keep up with sudden changes.

Get visibility into the performance, availability, health, and health of your infrastructure and applications. Real-time data collection from hybrid and multicloud infrastructure. Allow SRE best practices, which are heavily used by Google based upon SLOs or SLIs. Visualize insights using charts and dashboards, and generate alerts. Integrate with Slack, PagerDuty and other incident management tools to collaborate. Day zero integration for Google Cloud metrics. Cloud Monitoring provides automatic, out-of-the box metric collection dashboards for Google Cloud Services. It can also monitor multicloud and hybrid environments. Rich query language is used to display metrics, events, metadata, and other information. This allows you to identify and uncover patterns and helps you understand the issues. Service-level goals are used to improve user experience and collaboration with developers. One integrated service reduces the time spent navigating between different systems by providing metrics, uptime monitoring and dashboards.

The integrated platform for Learning and Development leaders, allowing them to manage, measure, and deliver employee growth at scale. Torch's flexible platform brings together technology and humans to provide digital learning and leadership development in an integrated way. Data-driven personalization, top-tier coaching and the most engaged mentor network in the world. You can create personalized learning paths that include collaboration and facilitation tools at any scale. High-touch, virtual human development delivered by skilled coaches professionals. Experienced operating leaders deliver virtual, high-touch human education. You will have a central dashboard and tools that can be used to manage, measure, and build learning and development in your organization. To report on learning effectiveness, ROI and satisfaction, you can use data from global engagement, satisfaction rates, individual goal tracking, and team opportunities areas.

Data Version Control (DVC), an open-source version control system, is tailored for data science and ML projects. It provides a Git-like interface for organizing data, models, experiments, and allowing users to manage and version audio, video, text, and image files in storage. Users can also structure their machine learning modelling process into a reproducible work flow. DVC integrates seamlessly into existing software engineering tools. Teams can define any aspect of machine learning projects in metafiles that are readable by humans. This approach reduces the gap between software engineering and data science by allowing the use of established engineering toolsets and best practices. DVC leverages Git to enable versioning and sharing for entire machine learning projects. This includes source code, configurations and parameters, metrics and data assets.

VictoriaMetrics Enterprise, a commercial product designed by the creators VictoriaMetrics, is a solution for monitoring and observability in complex environments. It's perfect for organizations with large or rapidly scaling monitoring environments. The Enterprise edition includes all of the features in the Community Edition plus additional enhancements like Downsampling Automated backups / Backup manager Data Retention Per Label/Tenant Multi Tenant Statistic & Anomaly detection. It provides stable releases and long-term support to ensure critical bug fixes, security patches, and other enhancements. The package also includes enterprise security compliance and prioritised feature requests. We can help you reduce storage costs while improving performance of historical data queries. Multiple retentions allow different storage durations for various datasets. Automatic discovery of storage updates the list without restarting services at insert and vmselect.

You can add logs, metrics, and traces to production or staging directly from your IDE/CLI, in real time and on-demand. Lightrun can help you increase productivity and ensure 100% code-level observability. Lightrun allows you to insert logs and metrics even when the service is in progress. You can debug monolith microservices like Kubernetes and Docker Swarm, ECS and Big Data workers, as well as serverless. Quickly add a logline, instrument a measurement, or place a snapshot that can be taken on-demand. There is no need to recreate the production environment or redeploy. Once instrumentation has been invoked, data is printed to your log analysis tool, your editor, or an APM of choice. To analyze code behavior and find bottlenecks or errors, you can stop the running process. You can easily add large numbers of logs and snapshots, counters or timers to your program. The system won't be stopped or broken. Spend less time debugging, and more time programming. Debugging is done without the need to restart, redeploying, or reproduce.

HD Camera is a fully-featured camera app. Take amazing photos with amazing filters! Enhance images taken in low-light or backlit scenes. Before you take pictures or shoot videos, preview the filter effect.

Extended analysis is essential for web administrators to help develop the project. Finteza's distributed network of servers does not slow down websites. Acceptance of payments and management of advertising areas. Are you tired of the same old metrics? Don't be limited by the standard reports. This is especially important for those who wish to increase sales or decrease their expenses. The bot detector can help you identify dirty traffic and determine its source. You will also be able to identify the presence of spammers, hackers, and scammers. Automated funnels for pages, events, and sources. All the things marketers love, and more. Operator, company, IP, geographical location, devices. Without revealing any personal information, you will be able access all the details about users. Compare pages based upon their effectiveness, optimize them for conversions, and monitor traffic. Real-time automatic calculation of conversions based on source.

Bugsnag monitors your application stability, allowing you to make data-driven decisions about whether you should be building new features or fixing bugs. We provide a full-stack stability monitoring solution that offers best-in-class functionality to mobile applications. Rich diagnostics that help you reproduce any error. All your apps can be accessed from one dashboard. It's a simple, thoughtful user experience. The most important metric for app health -- the common language between product and engineering teams. Not all bugs are worth fixing. You should only fix the ones that are important to your business. You have many customization options and extensive libraries with opinionated defaults. Experts who care deeply about the health and error reduction of your apps.