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UML Diagram Tools Software
UML (Unified Modeling Language) diagram tools are software applications used to create visual representations of a system's architecture and design. These tools allow users to draw various UML diagram types, such as class, sequence, activity, and use case diagrams, which aid in documenting and understanding software systems. UML diagram tools typically feature drag-and-drop interfaces, templates, and code generation capabilities for both forward and reverse engineering. Some tools also enable team collaboration, allowing multiple users to work on the same project at the same time.
Data Science Software
Data science software is a collection of tools and platforms designed to facilitate the analysis, interpretation, and visualization of large datasets, helping data scientists derive insights and build predictive models. These tools support various data science processes, including data cleaning, statistical analysis, machine learning, deep learning, and data visualization. Common features of data science software include data manipulation, algorithm libraries, model training environments, and integration with big data solutions. Data science software is widely used across industries like finance, healthcare, marketing, and technology to improve decision-making, optimize processes, and predict trends.
Artificial Intelligence Software
Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics.
View more categories (7) for "uml python"
35 Products for "uml python" with 2 filters applied:
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1
MLJAR Studio
MLJAR
It's a desktop app with Jupyter Notebook and Python built in, installed with just one click. It includes interactive code snippets and an AI assistant to make coding faster and easier, perfect for data science projects. We manually hand crafted over 100 interactive code recipes that you can use in your Data Science projects. Code recipes detect packages available in the current environment. Install needed modules with 1-click, literally. You can create and interact with all variables available...Starting Price: $20 per month -
2
Ray
Anyscale
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch...Starting Price: Free -
3
Pathway
Pathway
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing... -
4
Azure Notebooks
Microsoft
Develop and run code from anywhere with Jupyter notebooks on Azure. Get started for free. Get a better experience with a free Azure Subscription. Perfect for data scientists, developers, students, or anyone. Develop and run code in your browser regardless of industry or skillset. Supporting more languages than any other platform including Python 2, Python 3, R, and F#. Created by Microsoft Azure: Always accessible, always available from any browser, anywhere in the world. -
5
Towhee
Towhee
You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. Towhee provides out-of-the-box integration with your...Starting Price: Free -
6
MLBox
Axel ARONIO DE ROMBLAY
MLBox is a powerful Automated Machine Learning python library. It provides the following features fast reading and distributed data preprocessing/cleaning/formatting, highly robust feature selection and leak detection, accurate hyper-parameter optimization in high-dimensional space, state-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM), and prediction with models interpretation. MLBox main package contains 3 sub-packages: preprocessing... -
7
Indigo DRS Data Reporting Systems
Indigo Scape DRS Data Reporting Systems
Indigo Scape DRS is an advanced Data Reporting and Document Generation System for Rapid Report Development (RRD) using HTML, XML, XSLT, XQuery and Python to generate highly compatible and content rich business reports and documents with HTML. Representing the ultimate in reporting software our advanced technology and reusable reporting system is a powerhouse in data reporting. Indigo DRS is totally unique in its ability to query in XQuery, Python and SQL and use data from multiple...Starting Price: $500 per month / user -
8
Amazon SageMaker Pipelines
Amazon
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows... -
9
Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs... -
10
Altair SLC
Altair
Many organizations have developed SAS language programs over the past 20 years that are vital to their operations. Altair SLC runs programs written in SAS language syntax without translation and without needing to license third-party products. Altair SLC reduces users’ capital costs and operating expenses thanks to its superb ability to handle high levels of throughput. Altair SLC's built-in SAS language compiler runs SAS language and SQL code, and utilizes Python and R compilers to run... -
11
scikit-learn
scikit-learn
Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine...Starting Price: Free -
12
Keepsake
Replicate
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves...Starting Price: Free -
13
MLlib
Apache Software Foundation
Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional Map... -
14
Azure Machine Learning
Microsoft
.... Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
15
Hopsworks
Logical Clocks
Hopsworks is an open-source Enterprise platform for the development and operation of Machine Learning (ML) pipelines at scale, based around the industry’s first Feature Store for ML. You can easily progress from data exploration and model development in Python using Jupyter notebooks and conda to running production quality end-to-end ML pipelines, without having to learn how to manage a Kubernetes cluster. Hopsworks can ingest data from the datasources you use. Whether they are in the cloud, on...Starting Price: $1 per month -
16
Google Colab
Google
Google Colab is a free, hosted Jupyter Notebook service that provides cloud-based environments for machine learning, data science, and educational purposes. It offers no-setup, easy access to computational resources such as GPUs and TPUs, making it ideal for users working with data-intensive projects. Colab allows users to run Python code in an interactive, notebook-style environment, share and collaborate on projects, and access extensive pre-built resources for efficient experimentation... -
17
Google Cloud Datalab
Google
... knowledge base. Cloud Datalab enables analysis of your data on BigQuery, AI Platform, Compute Engine, and Cloud Storage using Python, SQL, and JavaScript (for BigQuery user-defined functions). Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data, and run training jobs on terabytes of data in AI Platform seamlessly. -
18
Weights & Biases
Weights & Biases
.... Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
19
... models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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20
MLflow
MLflow
... and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
21
Deepnote
Deepnote
... - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connectionStarting Price: Free -
22
Oracle Machine Learning
Oracle
Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment. Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python... -
23
Xilinx
Xilinx
... the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications. -
24
NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU...Starting Price: Free
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25
Baseten
Baseten
A frustratingly slow process requiring development resources or know-how, resulting in most models never seeing the light of day. Ship full-stack apps in minutes. Deploy models instantly, automatically generate API endpoints, and quickly build UI with drag-and-drop components. You shouldn’t need to become a DevOps engineer to get models into production. With Baseten, you can instantly serve, manage, and monitor models with a few lines of Python. Assemble business logic around your model... -
26
Launchable
Launchable
... uses machine learning to analyze your test failures and source code. It doesn't rely on code syntax analysis. This means it's trivial for Launchable to add support for almost any file-based programming language. It also means we can scale across teams and projects with different languages and tools. Out of the box, we currently support Python, Ruby, Java, JavaScript, Go, C, and C++, and we regularly add support for new languages. -
27
Hex
Hex
Hex brings together the best of notebooks, BI, and docs into a seamless, collaborative UI. Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories. Your default landing page in Hex is the Projects page. You can quickly find projects you created, as well as those shared with you and your workspace. The outline provides an easy-to-browse overview of all the cells...Starting Price: $24 per user per month -
28
Zepl
Zepl
Sync, search and manage all the work across your data science team. Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your... -
29
Amazon SageMaker JumpStart
Amazon
... and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis. -
30
Gradio
Gradio
Build & Share Delightful Machine Learning Apps. Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate...
