Alternatives to Azure Notebooks
Compare Azure Notebooks alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Azure Notebooks in 2024. Compare features, ratings, user reviews, pricing, and more from Azure Notebooks competitors and alternatives in order to make an informed decision for your business.
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TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
2
CoCalc
SageMath
Teaching scientific software online. CoCalc is a virtual online computer lab: it takes away the pain of teaching scientific software. Every student works 100% online – inside their own, isolated workspace. Follow the progress of each student in real-time. At any time you can jump into a file of a student, right where they are working. Use TimeTravel to see each step a student took to get to the solution. Integrated chat rooms allows you to guide students directly where they work or discuss collected files with your teaching assistants. The project's Activity Log records exactly when and by whom a file was accessed. Forget any complicated software setup – everyone is able to start working in seconds! Since everyone works with exactly the same software stack, any inconsistencies between your and your students' environments are eliminated. -
3
Google Colab
Google
Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with - Zero configuration required - Free access to GPUs - Easy sharing Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier. Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX, and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them. -
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Gradient
Gradient
Explore a new library or dataset in a notebook. Automate preprocessing, training, or testing with a 2orkflow. Bring your application to life with a deployment. Use notebooks, workflows, and deployments together or independently. Compatible with everything. Gradient supports all major frameworks and libraries. Gradient is powered by Paperspace's world-class GPU instances. Move faster with source control integration. Connect to GitHub to manage all your work & compute resources with git. Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Use any library or framework. Easily invite collaborators or share a public link. A simple cloud workspace that runs on free GPUs. Get started in seconds with a notebook environment that's easy to use and share. Perfect for ML developers. A powerful no-fuss environment with loads of features that just works. Choose a pre-built template or bring your own. Try a free GPU!Starting Price: $8 per month -
5
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, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface. -
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Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run 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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Deepnote
Deepnote
Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore, and analyze it with real-time collaboration and version control. Users can easily share project links with team collaborators, or with end-users to present polished assets. All of this is done through a powerful, browser-based UI that runs in the cloud. We built Deepnote because data scientists don't work alone. Features: - Sharing notebooks and projects via URL - 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 -
8
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 a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share. -
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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 and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built. -
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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‑premise, IoT networks, or from your Industry 4.0-solution. Deploy on‑premises on your own hardware or at your preferred cloud provider. Hopsworks will provide the same user experience in the cloud or in the most secure of air‑gapped deployments. Learn how to set up customized alerts in Hopsworks for different events that are triggered as part of the ingestion pipeline.Starting Price: $1 per month -
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ClearML
ClearML
ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.Starting Price: $15 -
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Dataiku DSS
Dataiku
Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs. -
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Lambda GPU Cloud
Lambda
Train the most demanding AI, ML, and Deep Learning models. Scale from a single machine to an entire fleet of VMs with a few clicks. Start or scale up your Deep Learning project with Lambda Cloud. Get started quickly, save on compute costs, and easily scale to hundreds of GPUs. Every VM comes preinstalled with the latest version of Lambda Stack, which includes major deep learning frameworks and CUDA® drivers. In seconds, access a dedicated Jupyter Notebook development environment for each machine directly from the cloud dashboard. For direct access, connect via the Web Terminal in the dashboard or use SSH directly with one of your provided SSH keys. By building compute infrastructure at scale for the unique requirements of deep learning researchers, Lambda can pass on significant savings. Benefit from the flexibility of using cloud computing without paying a fortune in on-demand pricing when workloads rapidly increase.Starting Price: $1.25 per hour -
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Zerve AI
Zerve AI
Merging the best of a notebook and an IDE into one integrated coding environment, experts can explore their data and write stable code at the same time with fully automated cloud infrastructure. Zerve’s data science development environment gives data science and ML teams a unified space to explore, collaborate, build, and deploy data science & AI projects like never before. Zerve offers true language interoperability, meaning that as well as being able to use Python, R, SQL, or Markdown all in the same canvas, users can connect these code blocks to each other. No more long-running code blocks or containers, with Zerve enjoying unlimited parallelization at any stage of the development journey. Analysis artifacts are automatically serialized, versioned, stored, and preserved for later use, meaning easily changing a step in the data flow without needing to rerun any preceding steps. Fine-grained selection of compute resources and extra memory for complex data transformation. -
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Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. 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. -
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IBM Watson Machine Learning is a full-service IBM Cloud offering that makes it easy for developers and data scientists to work together to integrate predictive capabilities with their applications. The Machine Learning service is a set of REST APIs that you can call from any programming language to develop applications that make smarter decisions, solve tough problems, and improve user outcomes. Take advantage of machine learning models management (continuous learning system) and deployment (online, batch, streaming). Select any of widely supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS. Use the command-line interface and Python client to manage your artifacts. Extend your application with artificial intelligence through the Watson Machine Learning REST API.Starting Price: $0.575 per hour
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Anaconda
Anaconda
Empowering the enterprise to do real data science at speed and scale with a full-featured machine learning platform. Spend less time managing tools and infrastructure, so you can focus on building machine learning applications that move your business forward. Anaconda Enterprise takes the headache out of ML operations, puts open-source innovation at your fingertips, and provides the foundation for serious data science and machine learning production without locking you into specific models, templates, or workflows. Software developers and data scientists can work together with AE to build, test, debug, and deploy models using their preferred languages and tools. AE provides access to both notebooks and IDEs so developers and data scientists can work together more efficiently. They can also choose from example projects and preconfigured projects. AE projects are automatically containerized so they can be moved between environments with ease. -
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Altair Knowledge Studio
Altair
Data scientists and business analysts use Altair to generate actionable insight from their data. Knowledge Studio is a market-leading easy to use machine learning and predictive analytics solution that rapidly visualizes data as it quickly generates explainable results - without requiring a single line of code. A recognized analytics leader, Knowledge Studio brings transparency and automation to machine learning with features such as AutoML and explainable AI without restricting how models are configured and tuned, giving you control over model building. Knowledge Studio is designed to enable collaboration across the business. Data scientists and business analysts can complete complex projects in minutes or hours, not weeks or months. Results are easily understood and explained. The ease of use and automation of steps of the modeling process enable data scientists to efficiently develop more machine learning models faster than coding or using other tools. -
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Kaggle
Kaggle
Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 19,000 public datasets and 200,000 public notebooks to conquer any analysis in no time. -
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Valohai
Valohai
Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.Starting Price: $560 per month -
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Striveworks Chariot
Striveworks
Make AI a trusted part of your business. Build better, deploy faster, and audit easily with the flexibility of a cloud-native platform and the power to deploy anywhere. Easily import models and search cataloged models from across your organization. Save time by annotating data rapidly with model-in-the-loop hinting. Understand the full provenance of your data, models, workflows, and inferences. Deploy models where you need them, including for edge and IoT use cases. Getting valuable insights from your data is not just for data scientists. With Chariot’s low-code interface, meaningful collaboration can take place across teams. Train models rapidly using your organization's production data. Deploy models with one click and monitor models in production at scale. -
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SANCARE
SANCARE
SANCARE is a start-up specializing in Machine Learning applied to hospital data. We collaborate with some of the best scientists in the field. SANCARE provides Medical Information Departments with an ergonomic and intuitive interface, promoting rapid adoption. The user has access to all the documents that constitute the computerized patient record. A true production tool, each step of the coding process is traced for external checks. Machine learning makes it possible to develop powerful predictive models from large volumes of data, and to take into account the notion of context, which is not possible for rule engines or semantic analysis engines. It is therefore possible to automate complex decision-making processes or to detect weak signals ignored by humans. The SANCARE software machine learning engine is based on a probabilistic approach. It learns over a large amount of examples to predict the right codes, without any indication. -
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FirstLanguage
FirstLanguage
Our Natural Language Processing(NLP) APIs provide best-in-class accuracy at an affordable rate and cover all aspects of NLP under a single roof. Save weeks of time training and creating language models. Take advantage of our best-in-class APIs to kickstart your app development. We provide the building blocks to create your own apps effectively like chatbots, sentiment analysis, etc. Text classification on multiple domains and 100+ languages. Perform effective sentiment analysis. We grow when your business does. So we have put together simple pricing that allows you to easily scale your business when it needs to evolve. Perfect for individual developers who are creating apps or building proof of concepts. Head to the Dashboard and get your API Key. Place this in the header of all your API calls. Use our SDK in your preferred language to start coding. Or you can refer to the auto-generated code blocks provided in 18 programming languages.Starting Price: $150 per month -
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ZenML
ZenML
Simplify your MLOps pipelines. Manage, deploy, and scale on any infrastructure with ZenML. ZenML is completely free and open-source. See the magic with just two simple commands. Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.Starting Price: Free -
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Clarifai
Clarifai
Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for developing better, faster and stronger AI. We help our customers create innovative solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. The platform comes with the broadest repository of pre-trained, out-of-the-box AI models built with millions of inputs and context. Our models give you a head start; extending your own custom AI models. Clarifai Community builds upon this and offers 1000s of pre-trained models and workflows from Clarifai and other leading AI builders. Users can build and share models with other community members. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been recognized by leading analysts, IDC, Forrester and Gartner, as a leading computer vision AI platform. Visit clarifai.comStarting Price: $0 -
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The single development environment for the entire data science workflow. Natively analyze your data with a reduction in context switching between services. Data to training at scale. Build and train models 5X faster, compared to traditional notebooks. Scale-up model development with simple connectivity to Vertex AI services. Simplified access to data and in-notebook access to machine learning with BigQuery, Dataproc, Spark, and Vertex AI integration. Take advantage of the power of infinite computing with Vertex AI training for experimentation and prototyping, to go from data to training at scale. Using Vertex AI Workbench you can implement your training, and deployment workflows on Vertex AI from one place. A Jupyter-based fully managed, scalable, enterprise-ready compute infrastructure with security controls and user management capabilities. Explore data and train ML models with easy connections to Google Cloud's big data solutions.Starting Price: $10 per GB
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Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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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 Python and R code and exchange SAS language datasets, Pandas, and R data frames. The software runs on IBM mainframes, in the cloud, and on servers and workstations running a variety of operating systems. It supports both remote job submission and the ability to exchange data between mainframe, cloud, and on-premises installations. -
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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 models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. Use fine-grained access controls to share your work. Allow others have read, edit, and run access as well as enable collaboration and distribution. All notebooks are auto-saved and versioned. You can name, manage and roll back all versions through an easy-to-use interface, and export seamlessly into Github. -
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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) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.Starting Price: Free -
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LUIS
Microsoft
Language Understanding (LUIS): A machine learning-based service to build natural language into apps, bots, and IoT devices. Quickly create enterprise-ready, custom models that continuously improve. Add natural language to your apps. Designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. LUIS integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot. Powerful developer tools are combined with customizable pre-built apps and entity dictionaries, such as Calendar, Music, and Devices, so you can build and deploy a solution more quickly. Dictionaries are mined from the collective knowledge of the web and supply billions of entries, helping your model to correctly identify valuable information from user conversations. Active learning is used to continuously improve the quality of the models. -
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Amazon SageMaker JumpStart
Amazon
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building 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. -
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Launchable
Launchable
You can have the best developers in the world, but every test is making them slower. 80% of your software tests are pointless. The problem is you don't know which 80%. We find the right 20% using your data so that you can ship faster. We have shrink-wrapped predictive test selection, a machine learning-based approach being used at companies like Facebook so that it can be used by any company. We support multiple languages, test runners, and CI systems. Just bring Git to the table. 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. -
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AI Squared
AI Squared
Empower data scientists and application developers to collaborate on ML projects. Build, load, optimize and test models and integrations before publishing to end-users for integration into live applications. Reduce data science workload and improve decision-making by storing and sharing ML models across the organization. Publish updates to automatically push changes to models in production. Drive efficiency by instantly providing ML-powered insights within any web-based business application. Our self-service, drag-and-drop browser extension enables analysts and business users to integrate models into any web-based application with zero code. -
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Vaex
Vaex
At Vaex.io we aim to democratize big data and make it available to anyone, on any machine, at any scale. Cut development time by 80%, your prototype is your solution. Create automatic pipelines for any model. Empower your data scientists. Turn any laptop into a big data powerhouse, no clusters, no engineers. We provide reliable and fast data driven solutions. With our state-of-the-art technology we build and deploy machine learning models faster than anyone on the market. Turn your data scientist into big data engineers. We provide comprehensive training of your employees, enabling you to take full advantage of our technology. Combines memory mapping, a sophisticated expression system, and fast out-of-core algorithms. Efficiently visualize and explore big datasets, and build machine learning models on a single machine. -
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Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find 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. -
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TruEra
TruEra
A machine learning monitoring solution that helps you easily oversee and troubleshoot high model volumes. With explainability accuracy that’s unparalleled and unique analyses that are not available anywhere else, data scientists avoid false alarms and dead ends, addressing critical problems quickly and effectively. Your machine learning models stay optimized, so that your business is optimized. TruEra’s solution is based on an explainability engine that, due to years of dedicated research and development, is significantly more accurate than current tools. TruEra’s enterprise-class AI explainability technology is without peer. The core diagnostic engine is based on six years of research at Carnegie Mellon University and dramatically outperforms competitors. The platform quickly performs sophisticated sensitivity analysis that enables data scientists, business users, and risk and compliance teams to understand exactly how and why a model makes predictions. -
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Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. 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. -
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Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
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Saturn Cloud
Saturn Cloud
Saturn Cloud is an award-winning ML platform for any cloud with 100,000+ users, including NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. It is an all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Users can spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, build large language models, and more in a completely hosted environment. Data professionals can use your preferred languages, IDEs, and machine-learning libraries in Saturn Cloud. We offer full Git integration, shared custom images, and secure credential storage, making scaling and building your team in the cloud easy. We support the entire machine learning lifecycle from experimentation to production with features like jobs and deployments. These features and built-in tools are easily shareable within teams, so time is saved and work is reproducible.Starting Price: $0.005 per GB per hour -
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Kraken
Big Squid
Kraken is for everyone from analysts to data scientists. Built to be the easiest-to-use, no-code automated machine learning platform. The Kraken no-code automated machine learning (AutoML) platform simplifies and automates data science tasks like data prep, data cleaning, algorithm selection, model training, and model deployment. Kraken was built with analysts and engineers in mind. If you've done data analysis before, you're ready! Kraken's no-code, easy-to-use interface and integrated SONAR© training make it easy to become a citizen data scientist. Advanced features allow data scientists to work faster and more efficiently. Whether you use Excel or flat files for day-to-day reporting or just ad-hoc analysis and exports, drag-and-drop CSV upload and the Amazon S3 connector in Kraken make it easy to start building models with a few clicks. Data Connectors in Kraken allow you to connect to your favorite data warehouse, business intelligence tools, and cloud storage.Starting Price: $100 per month -
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Wallaroo.AI
Wallaroo.AI
Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale. -
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Amazon SageMaker Canvas
Amazon
Amazon SageMaker Canvas expands access to machine learning (ML) by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own, without requiring any ML experience or having to write a single line of code. Visual point-and-click interface to connect, prepare, analyze, and explore data for building ML models and generating accurate predictions. Automatically build ML models to run what-if analysis and generate single or bulk predictions with a few clicks. Boost collaboration between business analysts and data scientists by sharing, reviewing, and updating ML models across tools. Import ML models from anywhere and generate predictions directly in Amazon SageMaker Canvas. With Amazon SageMaker Canvas, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and quickly and more easily build ML models. You can then analyze models and generate accurate predictions. -
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OctoAI
OctoML
OctoAI is world-class compute infrastructure for tuning and running models that wow your users. Fast, efficient model endpoints and the freedom to run any model. Leverage OctoAI’s accelerated models or bring your own from anywhere. Create ergonomic model endpoints in minutes, with only a few lines of code. Customize your model to fit any use case that serves your users. Go from zero to millions of users, never worrying about hardware, speed, or cost overruns. Tap into our curated list of best-in-class open-source foundation models that we’ve made faster and cheaper to run using our deep experience in machine learning compilation, acceleration techniques, and proprietary model-hardware performance technology. OctoAI automatically selects the optimal hardware target, applies the latest optimization technologies, and always keeps your running models in an optimal manner. -
45
Datatron
Datatron
Datatron offers tools and features built from scratch, specifically to make machine learning in production work for you. Most teams discover that there’s more to just deploying models, which is already a very manual and time-consuming task. Datatron offers single model governance and management platform for all of your ML, AI, and Data Science models in production. We help you automate, optimize, and accelerate your ML models to ensure that they are running smoothly and efficiently in production. Data Scientists use a variety of frameworks to build the best models. We support anything you’d build a model with ( e.g. TensorFlow, H2O, Scikit-Learn, and SAS ). Explore models built and uploaded by your data science team, all from one centralized repository. Create a scalable model deployment in just a few clicks. Deploy models built using any language or framework. Make better decisions based on your model performance. -
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Amazon SageMaker Studio Lab
Amazon
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately. -
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Alpa
Alpa
Alpa aims to automate large-scale distributed training and serving with just a few lines of code. Alpa was initially developed by folks in the Sky Lab, UC Berkeley. Some advanced techniques used in Alpa have been written in a paper published in OSDI'2022. Alpa community is growing with new contributors from Google. A language model is a probability distribution over sequences of words. It predicts the next word based on all the previous words. It is useful for a variety of AI applications, such the auto-completion in your email or chatbot service. For more information, check out the language model wikipedia page. GPT-3 is very large language model, with 175 billion parameters, that uses deep learning to produce human-like text. Many researchers and news articles described GPT-3 as "one of the most interesting and important AI systems ever produced". GPT-3 is gradually being used as a backbone in the latest NLP research and applications.Starting Price: Free -
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Dext
Dext
Automate bookkeeping and streamline expense management with Dext. Capture receipts, process invoices, and integrate seamlessly with major accounting software, 11,500+ apps, banks, and platforms. Start your free trial today! 🆕 The Evolution of Dext: From receipt capture to e-commerce integrations—All in one place. - Capture receipts and invoices via our mobile app, browser, email, direct integrations, and more. - Streamline expense management with the Dext mobile app. - Handle expense claims and approvals with greater efficiency. - Integrate with over 11,500 applications, banks, and institutions: Seamlessly connect with e-commerce platforms like PayPal, Shopify, and Stripe. Trusted by 700K+ businesses worldwide. Start your free trial today! **2024 Awards 🏆🏆 2024 Winner - ‘Small Business App Partner of the Year’ (Xero Awards United Kingdom) 🏆 🏆 2024 Winner - ‘Small Business App Partner of the Year’ (Xero Awards United States)Starting Price: $24 per month -
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Wekinator
Wekinator
The Wekinator is free, open source software. Wekinator 1.0 was originally created in 2009 by Rebecca Fiebrink. In 2015, Rebecca released Wekinator 2.0, an entirely new version with redesigned interactions, new algorithms, and ability to connect easily to dozens of other creative coding tools and sensors. Wekinator 2.0 continues to be gently updated with bug fixes and feature requests. It allows anyone to use machine learning to build new musical instruments, gestural game controllers, computer vision or computer listening systems, and more. The Wekinator allows users to build new interactive systems by demonstrating human actions and computer responses, instead of writing programming code. Create mappings between gesture and computer sounds. Control a drum machine using your webcam! Play Ableton using a Kinect! Control interactive visual environments created in Processing, OpenFrameworks, or Quartz Composer, or game engines like Unity, using gestures sensed from webcam, Kinect, etc. -
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Obviously AI
Obviously AI
The entire process of building machine learning algorithms and predicting outcomes, packed in one single click. Not all data is built to be ready for ML, use the Data Dialog to seamlessly shape your dataset without wrangling your files. Share your prediction reports with your team or make them public. Allow anyone to start making predictions on your model. Bring dynamic ML predictions into your own app using our low-code API. Predict willingness to pay, score leads and much more in real-time. Obviously AI puts the world’s most cutting-edge algorithms in your hands, without compromising on performance. Forecast revenue, optimize supply chain, personalize marketing. You can now know what happens next. Add a CSV file OR integrate with your favorite data sources in minutes. Pick your prediction column from a dropdown, we'll auto build the AI. Beautifully visualize predicted results, top drivers and simulate "what-if" scenarios.Starting Price: $75 per month
