55 Integrations with ZenML
View a list of ZenML integrations and software that integrates with ZenML below. Compare the best ZenML integrations as well as features, ratings, user reviews, and pricing of software that integrates with ZenML. Here are the current ZenML integrations in 2024:
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1
Google Cloud Platform
Google
Google Cloud is a cloud-based service that allows you to create anything from simple websites to complex applications for businesses of all sizes. New customers get $300 in free credits to run, test, and deploy workloads. All customers can use 25+ products for free, up to monthly usage limits. Use Google's core infrastructure, data analytics & machine learning. Secure and fully featured for all enterprises. Tap into big data to find answers faster and build better products. Grow from prototype to production to planet-scale, without having to think about capacity, reliability or performance. From virtual machines with proven price/performance advantages to a fully managed app development platform. Scalable, resilient, high performance object storage and databases for your applications. State-of-the-art software-defined networking products on Google’s private fiber network. Fully managed data warehousing, batch and stream processing, data exploration, Hadoop/Spark, and messaging.Starting Price: Free ($300 in free credits) -
2
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 -
3
Kubernetes
Kubernetes
Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. Kubernetes builds upon 15 years of experience of running production workloads at Google, combined with best-of-breed ideas and practices from the community. Designed on the same principles that allows Google to run billions of containers a week, Kubernetes can scale without increasing your ops team. Whether testing locally or running a global enterprise, Kubernetes flexibility grows with you to deliver your applications consistently and easily no matter how complex your need is. Kubernetes is open source giving you the freedom to take advantage of on-premises, hybrid, or public cloud infrastructure, letting you effortlessly move workloads to where it matters to you.Starting Price: Free -
4
Slack
Slack
Slack is a cloud-based project collaboration and team interaction software solution specially designed to seamlessly facilitate communication across organizations. Featuring powerful tools and services integrated into a single platform, Slack provides private channels to promote interaction within smaller teams, direct channels to help send messages directly to colleagues, and public channels that enables members across organizations to start conversations. Available on Mac, Windows, Android, and iOS apps, Slack offers a plethora of features that include chat, file sharing, collaborative workspace, real-time notifications, two-way audio and video, screen sharing, document imaging, activity tracking and logging, and more.Starting Price: $6.67 per user per month -
5
Discord
Discord
Discord is a free game communications app designed for both desktop and mobile platforms. Millions of players use the popular game platform every day to chat with friends over voice or text, or even stream gameplay in crystal clear quality for other Discord users. Not only can you organize a voice/text party in seconds, you can also use the service to find other players/teammates, search for certain types of groups/activities, or just talk games during your off time. The best part is that Discord is not designed for any specific genre or type of game; you can use it to coordinate communications for any game imaginable!Starting Price: Free -
6
GitHub
GitHub
GitHub is the world’s most secure, most scalable, and most loved developer platform. Join millions of developers and businesses building the software that powers the world. Build with the world’s most innovative communities, backed by our best tools, support, and services. If you manage multiple contributors , there’s a free option: GitHub Team for Open Source. We also run GitHub Sponsors, where we help fund your work. The Pack is back. We’ve partnered up to give students and teachers free access to the best developer tools—for the school year and beyond. Work for a government-recognized nonprofit, association, or 501(c)(3)? Get a discounted Organization account on us.Starting Price: $7 per month -
7
MongoDB
MongoDB
MongoDB is a general purpose, document-based, distributed database built for modern application developers and for the cloud era. No database is more productive to use. Ship and iterate 3–5x faster with our flexible document data model and a unified query interface for any use case. Whether it’s your first customer or 20 million users around the world, meet your performance SLAs in any environment. Easily ensure high availability, protect data integrity, and meet the security and compliance standards for your mission-critical workloads. An integrated suite of cloud database services that allow you to address a wide variety of use cases, from transactional to analytical, from search to data visualizations. Launch secure mobile apps with native, edge-to-cloud sync and automatic conflict resolution. Run MongoDB anywhere, from your laptop to your data center.Starting Price: Free -
8
GitLab
GitLab
GitLab is a complete DevOps platform. With GitLab, you get a complete CI/CD toolchain out-of-the-box. One interface. One conversation. One permission model. GitLab is a complete DevOps platform, delivered as a single application, fundamentally changing the way Development, Security, and Ops teams collaborate. GitLab helps teams accelerate software delivery from weeks to minutes, reduce development costs, and reduce the risk of application vulnerabilities while increasing developer productivity. Source code management enables coordination, sharing and collaboration across the entire software development team. Track and merge branches, audit changes and enable concurrent work, to accelerate software delivery. Review code, discuss changes, share knowledge, and identify defects in code among distributed teams via asynchronous review and commenting. Automate, track and report code reviews.Starting Price: $29 per user per month -
9
Amazon Web Services (AWS)
Amazon
Whether you're looking for compute power, database storage, content delivery, or other functionality, AWS has the services to help you build sophisticated applications with increased flexibility, scalability and reliability. Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. AWS has significantly more services, and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. This makes it faster, easier, and more cost effective to move your existing applications to the cloud. -
10
Microsoft Azure
Microsoft
Microsoft's Azure is a cloud computing platform that allows for rapid and secure application development, testing and management. Azure. Invent with purpose. Turn ideas into solutions with more than 100 services to build, deploy, and manage applications—in the cloud, on-premises, and at the edge—using the tools and frameworks of your choice. Continuous innovation from Microsoft supports your development today, and your product visions for tomorrow. With a commitment to open source, and support for all languages and frameworks, build how you want, and deploy where you want to. On-premises, in the cloud, and at the edge—we’ll meet you where you are. Integrate and manage your environments with services designed for hybrid cloud. Get security from the ground up, backed by a team of experts, and proactive compliance trusted by enterprises, governments, and startups. The cloud you can trust, with the numbers to prove it. -
11
Bitbucket
Atlassian
Bitbucket is more than just Git code management. Bitbucket gives teams one place to plan projects, collaborate on code, test, and deploy. Free for small teams under 5 and priced to scale with Standard ($3/user/mo) or Premium ($6/user/mo) plans. Keep your projects organized by creating Bitbucket branches right from Jira issues or Trello cards. Build, test and deploy with integrated CI/CD. Benefit from configuration as code and fast feedback loops. Approve code review more efficiently with pull requests. Create a merge checklist with designated approvers and hold discussions right in the source code with inline comments. Bitbucket Pipelines with Deployments lets you build, test and deploy with integrated CI/CD. Benefit from configuration as code and fast feedback loops. Know your code is secure in the Cloud with IP whitelisting and required 2-step verification. Restrict access to certain users, and control their actions with branch permissions and merge checks for quality code.Starting Price: $15 per month -
12
Amazon S3
Amazon
Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. This means customers of all sizes and industries can use it to store and protect any amount of data for a range of use cases, such as data lakes, websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices, and big data analytics. Amazon S3 provides easy-to-use management features so you can organize your data and configure finely-tuned access controls to meet your specific business, organizational, and compliance requirements. Amazon S3 is designed for 99.999999999% (11 9's) of durability, and stores data for millions of applications for companies all around the world. Scale your storage resources up and down to meet fluctuating demands, without upfront investments or resource procurement cycles. Amazon S3 is designed for 99.999999999% (11 9’s) of data durability. -
13
OpenAI
OpenAI
OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions. -
14
Python
Python
The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.Starting Price: Free -
15
scikit-image
scikit-image
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image provides a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! scikit-image aims to be the reference library for scientific image analysis in Python. We accomplish this by being easy to use and install. We are careful in taking on new dependencies, and sometimes cull existing ones, or make them optional. All functions in our API have thorough docstrings clarifying expected inputs and outputs. Conceptually identical arguments have the same name and position in a function signature. Test coverage is close to 100% and code is reviewed by at least two core developers before being included in the library.Starting Price: Free -
16
PyTorch
PyTorch
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. -
17
Amazon SageMaker
Amazon
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required. -
18
Hugging Face
Hugging Face
A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models. AutoTrain is an automatic way to train and deploy state-of-the-art Machine Learning models, seamlessly integrated with the Hugging Face ecosystem. Your training data stays on our server, and is private to your account. All data transfers are protected with encryption. Available today: text classification, text scoring, entity recognition, summarization, question answering, translation and tabular. CSV, TSV or JSON files, hosted anywhere. We delete your training data after training is done. Hugging Face also hosts an AI content detection tool.Starting Price: $9 per month -
19
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 -
20
Prodigy
Explosion
Radically efficient machine teaching. An annotation tool powered by active learning. Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Today’s transfer learning technologies mean you can train production-quality models with very few examples. With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. You'll move faster, be more independent and ship far more successful projects. Prodigy brings together state-of-the-art insights from machine learning and user experience. With its continuous active learning system, you're only asked to annotate examples the model does not already know the answer to. The web application is powerful, extensible and follows modern UX principles. The secret is very simple: it's designed to help you focus on one decision at a time and keep you clicking – like Tinder for data.Starting Price: $490 one-time fee -
21
Seldon
Seldon Technologies
Deploy machine learning models at scale with more accuracy. Turn R&D into ROI with more models into production at scale, faster, with increased accuracy. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Deploy reduces the time to production by providing production grade inference servers optimized for popular ML framework or custom language wrappers to fit your use cases. Seldon Core Enterprise provides access to cutting-edge, globally tested and trusted open source MLOps software with the reassurance of enterprise-level support. Seldon Core Enterprise is for organizations requiring: - Coverage across any number of ML models deployed plus unlimited users - Additional assurances for models in staging and production - Confidence that their ML model deployments are supported and protected. -
22
KServe
KServe
Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.Starting Price: Free -
23
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
24
Pillow
Pillow
The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool. Pillow for enterprise is available via the Tidelift subscription. The Python Imaging Library is ideal for image archival and batch processing applications. You can use the library to create thumbnails, convert between file formats, print images, etc. The current version identifies and reads a large number of formats. Write support is intentionally restricted to the most commonly used interchange and presentation formats. The library contains basic image processing functionality, including point operations, filtering with a set of built-in convolution kernels, and color space conversions.Starting Price: Free -
25
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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26
Azure OpenAI Service
Microsoft
Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.Starting Price: $0.0004 per 1000 tokens -
27
Tekton
Tekton
Tekton is a cloud-native solution for building CI/CD systems. It consists of Tekton Pipelines, which provides the building blocks, and of supporting components, such as Tekton CLI and Tekton Catalog, that make Tekton a complete ecosystem. Tekton standardizes CI/CD tooling and processes across vendors, languages, and deployment environments. It works well with Jenkins, Jenkins X, Skaffold, Knative, and many other popular CI/CD tools. Tekton abstracts the underlying implementation so that you can choose the build, test, and deploy workflow based on your team’s requirements. Tekton lets you create CI/CD systems quickly, giving you scalable, serverless, cloud native execution out of the box.Starting Price: Free -
28
Evidently AI
Evidently AI
The open-source ML observability platform. Evaluate, test, and monitor ML models from validation to production. From tabular data to NLP and LLM. Built for data scientists and ML engineers. All you need to reliably run ML systems in production. Start with simple ad hoc checks. Scale to the complete monitoring platform. All within one tool, with consistent API and metrics. Useful, beautiful, and shareable. Get a comprehensive view of data and ML model quality to explore and debug. Takes a minute to start. Test before you ship, validate in production and run checks at every model update. Skip the manual setup by generating test conditions from a reference dataset. Monitor every aspect of your data, models, and test results. Proactively catch and resolve production model issues, ensure optimal performance, and continuously improve it.Starting Price: $500 per month -
29
BudgetML
ebhy
BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production quickly and cheaply. Cloud functions are limited in memory and cost a lot at scale. Kubernetes clusters are overkill for one single model. Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST, Uvicorn/Gunicorn, backend servers, etc., that are simply not within the scope of a typical data scientist. BudgetML is our answer to this challenge. It is supposed to be fast, easy, and developer-friendly. It is by no means meant to be used in a full-fledged production-ready setup. It is simply a means to get a server up and running as fast as possible with the lowest costs possible.Starting Price: Free -
30
Llama 3.1
Meta
The open source AI model you can fine-tune, distill and deploy anywhere. Our latest instruction-tuned model is available in 8B, 70B and 405B versions. Using our open ecosystem, build faster with a selection of differentiated product offerings to support your use cases. Choose from real-time inference or batch inference services. Download model weights to further optimize cost per token. Adapt for your application, improve with synthetic data and deploy on-prem or in the cloud. Use Llama system components and extend the model using zero shot tool use and RAG to build agentic behaviors. Leverage 405B high quality data to improve specialized models for specific use cases.Starting Price: Free -
31
Deepchecks
Deepchecks
Release high-quality LLM apps quickly without compromising on testing. Never be held back by the complex and subjective nature of LLM interactions. Generative AI produces subjective results. Knowing whether a generated text is good usually requires manual labor by a subject matter expert. If you’re working on an LLM app, you probably know that you can’t release it without addressing countless constraints and edge-cases. Hallucinations, incorrect answers, bias, deviation from policy, harmful content, and more need to be detected, explored, and mitigated before and after your app is live. Deepchecks’ solution enables you to automate the evaluation process, getting “estimated annotations” that you only override when you have to. Used by 1000+ companies, and integrated into 300+ open source projects, the core behind our LLM product is widely tested and robust. Validate machine learning models and data with minimal effort, in both the research and the production phases.Starting Price: $1,000 per month -
32
Google Cloud Tekton
Google
Tekton is a powerful yet flexible Kubernetes-native open-source framework for creating continuous integration and delivery (CI/CD) systems. It lets you build, test, and deploy across multiple cloud providers or on-premises systems by abstracting away the underlying implementation details. Standardize your CI/CD tooling, Built-in best practices for Kubernetes, Run on hybrid or multi-cloud, Get maximum flexibility. -
33
HashiCorp Vault
HashiCorp
Secure, store and tightly control access to tokens, passwords, certificates, encryption keys for protecting secrets and other sensitive data using a UI, CLI, or HTTP API. Secure applications and systems with machine identity and automate credential issuance, rotation, and more. Enable attestation of application and workload identity, using Vault as the trusted authority. Many organizations have credentials hard coded in source code, littered throughout configuration files and configuration management tools, and stored in plaintext in version control, wikis, and shared volumes. Safeguarding and ensuring that a credentials isn’t leaked, or in the likelihood it is, that the organization can quickly revoke access and remediate, is a complex problem to solve. -
34
Feast
Tecton
Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution. -
35
Amazon SageMaker Ground Truth
Amazon Web Services
Amazon SageMaker allows you to identify raw data such as images, text files, and videos; add informative labels and generate labeled synthetic data to create high-quality training data sets for your machine learning (ML) models. SageMaker offers two options, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which give you the flexibility to use an expert workforce to create and manage data labeling workflows on your behalf or manage your own data labeling workflows. data labeling. If you want the flexibility to create and manage your own personal and data labeling workflows, you can use SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes data labeling easy and gives you the option of using human annotators via Amazon Mechanical Turk, third-party providers, or your own private staff.Starting Price: $0.08 per month -
36
AWS AI Services
Amazon
AWS pre-trained AI Services provide ready-made intelligence for your applications and workflows. AI Services easily integrate with your applications to address common use cases such as personalized recommendations, modernizing your contact center, improving safety and security, and increasing customer engagement. Because we use the same deep learning technology that powers Amazon.com and our ML Services, you get quality and accuracy from continuously-learning APIs. And best of all, AI Services on AWS doesn't require machine learning experience. Catalog assets, automate workflows, and extract meaning from your media and applications. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control. Improve operations with automated monitoring to find bottlenecks and assess manufacturing quality and safety. Pull valuable information from millions of documents at speed. -
37
Label Studio
Label Studio
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input. -
38
LangSmith
LangChain
Unexpected results happen all the time. With full visibility into the entire chain sequence of calls, you can spot the source of errors and surprises in real time with surgical precision. Software engineering relies on unit testing to build performant, production-ready applications. LangSmith provides that same functionality for LLM applications. Spin up test datasets, run your applications over them, and inspect results without having to leave LangSmith. LangSmith enables mission-critical observability with only a few lines of code. LangSmith is designed to help developers harness the power–and wrangle the complexity–of LLMs. We’re not only building tools. We’re establishing best practices you can rely on. Build and deploy LLM applications with confidence. Application-level usage stats. Feedback collection. Filter traces, cost and performance measurement. Dataset curation, compare chain performance, AI-assisted evaluation, and embrace best practices. -
39
DBRX
Databricks
Today, we are excited to introduce DBRX, an open, general-purpose LLM created by Databricks. Across a range of standard benchmarks, DBRX sets a new state-of-the-art for established open LLMs. Moreover, it provides the open community and enterprises building their own LLMs with capabilities that were previously limited to closed model APIs; according to our measurements, it surpasses GPT-3.5, and it is competitive with Gemini 1.0 Pro. It is an especially capable code model, surpassing specialized models like CodeLLaMA-70B in programming, in addition to its strength as a general-purpose LLM. This state-of-the-art quality comes with marked improvements in training and inference performance. DBRX advances the state-of-the-art in efficiency among open models thanks to its fine-grained mixture-of-experts (MoE) architecture. Inference is up to 2x faster than LLaMA2-70B, and DBRX is about 40% of the size of Grok-1 in terms of both total and active parameter counts. -
40
ARGO
ARGO
Are your fraud losses higher than expected? Is your fraud prevention effectiveness below 95%? Are you losing money at the teller line and through ATMs? Are your check verification thresholds above $500? Are you spending more than 0.01% of bank assets on systems and analysts to review suspects and prevent fraud? Are you reviewing more than 250 checks for each item worth returning? Stop wasting time and money, and let us reduce your false positives, false negatives, manual reviews, and labor resources. All-In-One Check, ACH, ATM, Wire, and Cash Fraud Security Solution. An all-in-one fraud solution with compliance reporting, case management options, and increased levels of fraud prevention for financial transactions. Connecting financial services and healthcare customers with innovative technology. -
41
Azure Kubernetes Service (AKS)
Microsoft
The fully managed Azure Kubernetes Service (AKS) makes deploying and managing containerized applications easy. It offers serverless Kubernetes, an integrated continuous integration and continuous delivery (CI/CD) experience, and enterprise-grade security and governance. Unite your development and operations teams on a single platform to rapidly build, deliver, and scale applications with confidence. Elastic provisioning of additional capacity without the need to manage the infrastructure. Add event-driven autoscaling and triggers through KEDA. Faster end-to-end development experience with Azure Dev Spaces including integration with Visual Studio Code Kubernetes tools, Azure DevOps, and Azure Monitor. Advanced identity and access management using Azure Active Directory, and dynamic rules enforcement across multiple clusters with Azure Policy. Available in more regions than any other cloud providers. -
42
PostgreSQL
PostgreSQL Global Development Group
PostgreSQL is a powerful, open-source object-relational database system with over 30 years of active development that has earned it a strong reputation for reliability, feature robustness, and performance. There is a wealth of information to be found describing how to install and use PostgreSQL through the official documentation. The open-source community provides many helpful places to become familiar with PostgreSQL, discover how it works, and find career opportunities. Learm more on how to engage with the community. The PostgreSQL Global Development Group has released an update to all supported versions of PostgreSQL, including 15.1, 14.6, 13.9, 12.13, 11.18, and 10.23. This release fixes 25 bugs reported over the last several months. This is the final release of PostgreSQL 10. PostgreSQL 10 will no longer receive security and bug fixes. If you are running PostgreSQL 10 in a production environment, we suggest that you make plans to upgrade. -
43
Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
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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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MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API 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. -
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Neptune OS
Neptune
Neptune is a GNU/Linux Distribution for desktops based fully upon Debian Stable ('Buster'), except for a newer kernel and some drivers. It ships with a modern KDE Plasma Desktop with its main view on a good looking multimedia system which allows for getting work done. It also is a system which is flexible and very useful on usb sticks. Therefore we developed easy to use applications like USB Installer as well as a Persistent Creator that allows you to store changes to your system on your live usb stick. The Debian repository is the major base for getting updates and new software. Furthermore Neptune ships with its own software repository to update our own applications. Neptune tries to get the BeOS message of a fully supported multimedia OS to a next generation of users. Neptunes focuses on providing an elegant out of the box experience for the users. Therefore we ship a nice and simple overall look and feel as well as a whole bunch of multimedia tools, like codecs, flash player, etc. -
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Azure Databricks
Microsoft
Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO). -
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Great Expectations
Great Expectations
Great Expectations is a shared, open standard for data quality. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. We recommend deploying within a virtual environment. If you’re not familiar with pip, virtual environments, notebooks, or git, you may want to check out the Supporting. There are many amazing companies using great expectations these days. Check out some of our case studies with companies that we've worked closely with to understand how they are using great expectations in their data stack. Great expectations cloud is a fully managed SaaS offering. We're taking on new private alpha members for great expectations cloud, a fully managed SaaS offering. Alpha members get first access to new features and input to the roadmap. -
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Kubeflow
Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready. -
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Apache Beam
Apache Software Foundation
The easiest way to do batch and streaming data processing. Write once, run anywhere data processing for mission-critical production workloads. Beam reads your data from a diverse set of supported sources, no matter if it’s on-prem or in the cloud. Beam executes your business logic for both batch and streaming use cases. Beam writes the results of your data processing logic to the most popular data sinks in the industry. A simplified, single programming model for both batch and streaming use cases for every member of your data and application teams. Apache Beam is extensible, with projects such as TensorFlow Extended and Apache Hop built on top of Apache Beam. Execute pipelines on multiple execution environments (runners), providing flexibility and avoiding lock-in. Open, community-based development and support to help evolve your application and meet the needs of your specific use cases.
