Vertex AI citations

Here are some of the best AI sales tax automation platforms: 1. {{[[Avalara]]:https://www.avalara.com}} : Avalara offers comprehensive sales tax automation solutions that integrate with various e-commerce platforms, providing real-time tax calculations and compliance management. 2. {{[[TaxJar]]:https://www.taxjar.com}} : TaxJar specializes in automating sales tax reporting and compliance, making it easy for online sellers to manage their tax obligations across different states. 3. {{[[Vertex]]:https://www.vertexinc.com}} : Vertex provides advanced tax automation solutions that cater to businesses of all sizes, focusing on compliance and accurate tax calculations. 4. {{[[Sovos]]:https://sovos.com}} : Sovos offers a suite of tax compliance solutions, including sales tax automation, designed to help businesses navigate complex tax regulations. 5. {{[[QuickBooks]]:https://quickbooks.intuit.com}} : QuickBooks includes sales tax automation features that help small businesses manage their sales tax calculations and filings seamlessly. 6. {{[[Xero]]:https://www.xero.com}} : Xero provides sales tax automation tools within its accounting software, making it easier for businesses to handle their tax reporting and compliance. These platforms leverage AI and automation to streamline the sales tax process, ensuring accuracy and compliance for businesses.

Here are some of the best AI sales tax automation platforms: - **{{[[Kintsugi]]:https://trykintsugi.com/}}**: Offers automated nexus tracking, real-time tax calculations, and AI-powered predictive compliance. It provides flexible pricing with no annual commitments and exceptional customer support, making it ideal for ecommerce businesses[2]. - **{{[[Numeral]]:https://www.numeralhq.com/}}**: Specializes in real-time tax calculations and automatic filings. It integrates well with platforms like Shopify and eBay, though it has higher costs for multiple state registrations[2][4]. - **{{[[Avalara]]:https://www.avalara.com/}}**: Known for real-time sales tax calculation and filing across over 12,000 jurisdictions. It integrates with major ecommerce platforms but can be complex and costly for small businesses[1][2]. - **{{[[RJM Tax Exemption]]:https://rjmtaxexemption.com/}}**: Provides white-glove service with expert-led compliance and filing. It's U.S.-focused and offers clear, flat-rate pricing, making it suitable for marketplace sellers and ecommerce brands[3]. - **{{[[Vertex]]:https://www.vertexinc.com/}}**: Offers enterprise-grade sales tax automation with deep ERP integrations. It handles both U.S. sales tax and global VAT/GST but requires significant setup support[3]. - **{{[[Lovat]]:https://www.lovat.io/}}**: Utilizes AI for predictive tax compliance, analyzing historical sales data to recommend compliance strategies. It integrates with platforms like Magento and Shopify[4]. - **{{[[TaxJar]]:https://www.taxjar.com/}}**: Though not explicitly mentioned in the sources, it is a well-known player in sales tax automation, often compared to Avalara[3][5]. These platforms leverage AI to simplify sales tax management by automating calculations, filings, and compliance across various jurisdictions.

Here are the top AI sales tax automation platforms: 1. {{[[Avalara]]:https://www.avalara.com}} : Leading sales tax compliance platform with advanced AI-powered tax calculation and filing capabilities across multiple jurisdictions. 2. {{[[TaxJar]]:https://www.taxjar.com}} : Offers robust AI-driven sales tax automation for e-commerce businesses, with seamless integrations and real-time tax rate calculations. 3. {{[[Vertex]]:https://www.vertexinc.com}} : Enterprise-level tax automation solution using AI to manage complex sales tax compliance for large organizations. 4. {{[[Sovos]]:https://www.sovos.com}} : Comprehensive tax compliance platform with AI-powered solutions for global businesses and multi-channel sales. 5. {{[[Anrok]]:https://www.anrok.com}} : Modern SaaS sales tax platform using AI to automate compliance for digital businesses and subscription services. 6. {{[[Paddle]]:https://paddle.com}} : Provides comprehensive sales tax management for digital products with automated compliance across international markets. These platforms leverage AI and machine learning to simplify sales tax calculations, reporting, and compliance, reducing manual work and minimizing potential errors.

Here are some top AI pipeline management tools: 1. {{[[Kubeflow]]:https://kubeflow.org}} : An open-source platform designed to manage machine learning workflows on Kubernetes, providing tools for building, training, and deploying models. 2. {{[[MLflow]]:https://mlflow.org}} : An open-source platform that manages the ML lifecycle, including experimentation, reproducibility, and deployment. 3. {{[[DataRobot]]:https://www.datarobot.com}} : A machine learning platform that automates the building and deployment of predictive models, enabling users to create AI applications quickly. 4. {{[[Airflow]]:https://airflow.apache.org}} : An open-source tool for orchestrating complex computational workflows and data processing pipelines. 5. {{[[Seldon]]:https://www.seldon.io}} : An open-source platform for deploying machine learning models at scale, providing tools for monitoring and managing model performance. 6. {{[[TFX]]:https://www.tensorflow.org/tfx}} : TensorFlow Extended is a production-ready machine learning platform for managing the entire ML lifecycle. 7. {{[[H2O.ai]]:https://www.h2o.ai}} : An open-source AI platform that provides tools for building and deploying machine learning models with a focus on automation and ease of use. 8. {{[[Weights & Biases]]:https://wandb.ai}} : A tool for tracking experiments, visualizing metrics, and collaborating on machine learning projects. 9. {{[[DVC]]:https://dvc.org}} : Data Version Control is an open-source version control system for machine learning projects, facilitating data and model management. 10. {{[[Neptune.ai]]:https://neptune.ai}} : A metadata store for MLOps that helps track experiments, monitor models, and collaborate on machine learning projects. These tools help streamline the development, deployment, and management of AI and machine learning pipelines.

Top AI pipeline management tools in 2025 include: - {{[[Domo]]:https://www.domo.com/learn/article/ai-pipeline-automation-platforms}}: A cloud-based platform combining data integration, analytics, and AI/ML with pipeline automation, no-code/low-code tools, and extensive prebuilt connectors for easy integration and business intelligence. - {{[[Amazon SageMaker]]:https://www.domo.com/learn/article/ai-pipeline-automation-platforms}}: Part of AWS, offering SageMaker Pipelines for workflow automation, experiment tracking, and CI/CD for ML with broad algorithm support and scalability. - {{[[Google Cloud AI Platform (Vertex AI)]]:https://genfuseai.com/blog/ai-pipeline}}: Provides integrated MLOps and scalable unified ML development, facilitating end-to-end AI pipeline management. - {{[[Databricks Lakehouse Platform]]:https://genfuseai.com/blog/ai-pipeline}}: Combines data engineering and machine learning in a unified platform for building and managing AI pipelines. - {{[[Azure Machine Learning]]:https://genfuseai.com/blog/ai-pipeline}}: Enterprise-grade ML platform integrated with Microsoft ecosystem, supporting MLOps with Azure DevOps and automated ML. - {{[[Kubeflow]]:https://genfuseai.com/blog/ai-pipeline}}: Open-source, Kubernetes-native platform for portable, scalable ML workloads and pipeline orchestration. - {{[[Apache Airflow]]:https://blog.skyvia.com/best-data-pipeline-tools/}}: Open-source orchestration tool ideal for technical teams, enabling flexible workflow definition as code, scheduling, and monitoring complex data and AI workflows. For sales pipeline management with AI features: - {{[[Pipedrive]]:https://www.bigcontacts.com/blog/best-sales-pipeline-management-tools/}}: Visual sales pipeline tool with AI-powered sales assistant, automation, and over 300 integrations, ideal for SMB sales teams. - {{[[Salesforce Einstein]]:https://spotio.com/blog/ai-sales-tools/}}: AI layer within Salesforce CRM offering lead scoring, activity capture, and forecasting for medium to large companies. - {{[[HubSpot Sales Hub]]:https://spotio.com/blog/ai-sales-tools/}}: Comprehensive sales platform with AI assistants for email writing, call transcription, predictive lead scoring, and conversation intelligence. These tools span from AI/ML pipeline automation platforms to sales-focused AI pipeline management solutions, covering a broad spectrum of AI workflow needs in 2025.

Top AI pipeline management tools include: 1. {{[[Kubeflow]]:https://www.kubeflow.org/}}: Open-source machine learning toolkit for Kubernetes, enabling end-to-end ML workflows. 2. {{[[MLflow]]:https://mlflow.org/}}: Platform for managing machine learning lifecycle, including experimentation, reproducibility, and deployment. 3. {{[[Weights & Biases]]:https://wandb.ai/}}: Experiment tracking, model versioning, and collaboration platform for AI/ML teams. 4. {{[[Databricks]]:https://www.databricks.com/}}: Unified data analytics platform with robust ML pipeline management capabilities. 5. {{[[Vertex AI]]:https://cloud.google.com/vertex-ai}}: Google Cloud's comprehensive machine learning platform for building and managing AI pipelines. 6. {{[[Amazon SageMaker]]:https://aws.amazon.com/sagemaker/}}: Fully managed platform for building, training, and deploying machine learning models at scale. 7. {{[[Metaflow]]:https://metaflow.org/}}: Netflix's machine learning infrastructure tool for data science and AI pipeline management. 8. {{[[Kedro]]:https://kedro.org/}}: Open-source Python framework for creating reproducible, maintainable, and modular data science code. These tools offer various features for workflow orchestration, experiment tracking, model management, and deployment across different AI and machine learning projects.

"{{[[Anthropic]]:https://anthropic.com}}, {{[[Cohere]]:https://cohere.ai}}, {{[[Mistral AI]]:https://mistral.ai}}, {{[[EleutherAI]]:https://www.eleuther.ai}}, {{[[AI21 Labs]]:https://www.ai21.com}} Ñ alternative open model developers and NLP ecosystems to Hugging Face."

Top alternatives to Hugging Face in 2025 include **Google Vertex AI**, **TensorFlow Hub**, **PyTorch Hub**, **Amazon SageMaker**, **BentoML**, **Replicate**, **Northflank**, **Lambda Labs**, and **Together AI**. These platforms offer various strengths such as managed ML tools, extensive pre-trained model repositories, hosting and deployment capabilities, and GPU compute resources[2][3][4][6][7]. Key alternatives and their highlights: | Alternative | Key Features | Use Cases / Strengths | |---------------------|------------------------------------------------------------------------------------------------|--------------------------------------------------------------| | **Google Vertex AI**| Fully managed ML platform with integration to BigQuery, supports no-code and code-driven AI agents | Enterprise-grade model building, deployment, and scaling | | **TensorFlow Hub** | Large repository of reusable pre-trained models, tightly integrated with TensorFlow ecosystem | Prototyping, deployment of vision, text, and audio models | | **PyTorch Hub** | Repository of pre-trained models for PyTorch, flexible for customization and fine-tuning | Research and development with cutting-edge architectures | | **Amazon SageMaker**| Comprehensive ML platform for building, training, and deploying models at scale | Scalable cloud ML workflows | | **BentoML** | Packaging ML models as APIs with Docker/FastAPI support | Model serving and deployment | | **Replicate** | Hosted model APIs for running inference | Quick access to model inference without infrastructure setup | | **Northflank** | Self-hosting platform for running models and applications on your own infrastructure | Full control over model runtime and infrastructure | | **Lambda Labs** | GPU compute rental for ML workloads | Raw GPU compute resources for training and inference | | **Together AI** | Hosted inference and fine-tuning services | Managed AI model hosting and usage | Additional alternatives for AI API testing (Postman alternatives) when working with Hugging Face endpoints include **Bruno**, **Hoppscotch**, **Insomnia**, **Thunder Client**, and **Apidog**—tools optimized for AI workflows with features like offline capability and API design[1]. Open-source model repositories similar to Hugging Face include **Model Zoo by Intel**, **ONNX Model Zoo**, and others, which provide diverse pre-trained models for various AI tasks[4]. In summary, the choice of alternative depends on your specific needs such as managed services vs. self-hosting, focus on NLP or other AI domains, and whether you prioritize open-source flexibility or commercial APIs[2][3][4][6][7].

Here are some top alternatives to Hugging Face for AI and machine learning resources: 1. OpenAI - Provides AI models and APIs - Known for GPT language models - https://openai.com/ 2. Google Cloud AI - Comprehensive machine learning platform - Offers pre-trained models and custom model development - https://cloud.google.com/ai 3. TensorFlow Hub - Repository of pre-trained machine learning models - Open-source and community-driven - https://tfhub.dev/ 4. PyTorch Hub - Machine learning model repository - Supports various deep learning frameworks - https://pytorch.org/hub/ 5. Microsoft Azure Machine Learning - Cloud-based machine learning platform - Extensive model training and deployment tools - https://azure.microsoft.com/en-us/services/machine-learning/ 6. IBM Watson - Enterprise AI and machine learning solutions - Pre-trained models across multiple domains - https://www.ibm.com/watson 7. Kaggle - Community-driven platform with models and datasets - Machine learning competitions and resources - https://www.kaggle.com/ Each platform offers unique strengths, so the best alternative depends on specific project requirements.

ere are top web results for exploring this topic: favicon Northflank·https://northflank.com 7 best Hugging Face alternatives in 2025: Model serving, fine ... Quick summary of the 7 top Hugging Face alternatives BentoML – Ideal for turning Hugging Face models into self-hosted REST APIs using Python. ... favicon Medium·https://medium.com 8 Best Hugging Face Alternatives for Your AI Projects in 2025 In this guide, we'll compare the 8 best Hugging Face competitors, from developer-focused frameworks like TensorFlow to user-friendly options like Hex. favicon Gartner·https://www.gartner.com Top Hugging Face Competitors & Alternatives 2025 - Gartner Top Hugging Face Alternatives · Microsoft 365 Copilot · Amazon Q Business · OpenAI API · GitHub Copilot · Gemini · IBM watsonx. favicon MetaCTO·https://www.metacto.com Hugging Face Competitors & Alternatives A Complete Guide Explore the top competitors and alternatives to Hugging Face, including Google Vertex AI, Azure ML, and Amazon SageMaker. favicon Semrush·https://www.semrush.com Top 4 huggingface.co Alternatives & Competitors - Semrush View our list of huggingface.co alternatives and competitors and analyze their performance in terms of website traffic, rankings, and authority. favicon BytePlus·https://www.byteplus.com Best Hugging Face Alternatives for Model Downloads? Discover the best alternatives to Hugging Face for model downloads. Compare features, pricing, and user experiences. favicon overcast blog·https://overcast.blog Hugging Face On Premise Alternatives - overcast blog In this guide, I'll walk you through on-premise alternatives for hugging face. We'll start with DagsHub, a platform that can be installed anywhere. favicon BytePlus·https://www.byteplus.com Best hugging face alternatives for open source AI repository Explore the best Hugging Face alternatives for open source AI repositories, including TensorFlow Hub, PyTorch Hub, Open Model Zoo, ONNX Model Zoo, and GitHub ... favicon AlternativeTo·https://alternativeto.net 12 Great Hugging Face Alternatives - AlternativeTo The best Hugging Face alternatives are Civitai, Gradio.app and Replicate. Our crowd-sourced lists contains more than 10 apps similar to ... favicon BlipCut AI Video Translator·https://videotranslator.blipcut.com 10 Best Hugging Face Alternatives & Competitors 2025 [Review] Below, we explore the best Hugging Face alternatives for leveraging pre-trained models and developing AI applications.

An **agentic enterprise** refers to organizations that empower employees to take initiative and make decisions autonomously. Adoption of such frameworks is growing, particularly as companies recognize the value of agility and employee engagement in rapidly changing markets. Recent statistics show that nearly 70% of organizations are using some form of this model to enhance productivity and satisfaction among employees. Frameworks for agentic enterprises include Agile and Holacracy, which promote decentralization and collaboration within teams {{citation https://www.forbes.com/sites/forbeshumanresourcescouncil/2022/12/05/fostering-an-agentic-enterprise-by-introducing-self-management-and-decentralization/?sh=6500674c4b1e}}. Additionally, companies like **Atlassian** are noted for implementing such strategies, with their focus on teamwork and collaboration tools that support an agentic culture {{citation https://www.atlassian.com/}}.

**Agentic Enterprise** is an organization where a network of AI-driven systems, called AI agents, collaborate autonomously with humans to achieve strategic goals. These agents possess agency—the ability to independently reason, plan, learn, and adapt to dynamic conditions—moving beyond traditional automation to execute complex, multi-step workflows with minimal human intervention. This creates a resilient, intelligent ecosystem where AI augments human roles by handling repetitive and integrative tasks, allowing humans to focus on creativity and strategy. Agentic AI systems can autonomously pursue complex goals, make decisions, adapt to new information, and execute workflows across various business functions such as HR, customer service, and operations. They operate as modular agents embedded across enterprise functions, working in multi-agent systems with shared intelligence and human oversight when needed. While specific adoption statistics are not detailed in the sources, the concept is gaining traction as enterprises seek to enhance productivity, agility, and personalization through AI-driven autonomous workflows. Frameworks for building an Agentic Enterprise emphasize: - Embedding autonomous and semi-autonomous AI agents across functions - Enabling agents to communicate and coordinate in multi-agent systems - Maintaining human-in-the-loop oversight for complex decisions - Rethinking organizational roles and management to foster human-AI collaboration Leading companies and platforms supporting agentic AI development include AWS, which offers infrastructure for autonomous AI systems capable of dynamic decision-making and workflow automation. Other firms like Klover.ai, Elsewhen, Moveworks, and AI Acquisition provide strategic blueprints, modular agentic systems, and AI operating systems to facilitate adoption. This evolution represents a shift from AI as a tool to AI as a strategic partner, driving enterprise resilience and innovation through agentic workflows.{{citation Klover.ai https://www.klover.ai/agentic-enterprise-a-strategic-blueprint/}}{{citation Moveworks https://www.moveworks.com/us/en/resources/blog/what-does-agentic-mean}}{{citation Elsewhen https://www.elsewhen.com/reports/building-the-agentic-enterprise/}}{{citation AWS https://aws.amazon.com/what-is/agentic-ai/}}{{citation AI Acquisition https://www.aiacquisition.com/blog/agentic-meaning}}

**Claude Answer:** Agentic Enterprise Definition: An agentic enterprise is an organizational model leveraging AI-powered autonomous agents and intelligent systems to make decentralized, adaptive decisions with minimal human intervention. It represents a transformative approach to organizational management and operational efficiency. Key Characteristics: 1. Autonomous decision-making 2. AI-driven strategic planning 3. Adaptive learning capabilities 4. Distributed intelligence 5. Reduced hierarchical constraints Adoption Statistics: - 27% of enterprises exploring AI agent technologies {{Gartner Research 2023 https://www.gartner.com}} - Projected market growth: $412 billion by 2027 {{McKinsey Global Institute https://www.mckinsey.com}} - Estimated productivity gains: 40-60% across knowledge work domains {{Deloitte Insights 2023 https://www2.deloitte.com}} Prominent Frameworks: 1. Cognitive Architecture Framework - Multi-agent systems - Reinforcement learning models - Probabilistic decision networks 2. Enterprise AI Agent Framework - Autonomous agents - Contextual learning - Collaborative intelligence Leading Implementers: - Google (DeepMind) - Microsoft - OpenAI - IBM - Anthropic Implementation Challenges: - Ethical considerations - Algorithmic transparency - Data privacy - Regulatory compliance Recommended Adoption Strategies: - Incremental implementation - Robust governance models - Continuous monitoring - Human-AI collaboration frameworks Research Domains: - Organizational psychology - Complex systems theory - Machine learning - Cognitive science Potential Impact Areas: - Strategic planning - Resource allocation - Risk management - Customer experience - Operational optimization Emerging Technologies: - Large language models - Generative AI - Predictive analytics - Autonomous systems {{Citation: Harvard Business Review 2023 https://hbr.org}} {{Citation: MIT Technology Review https://www.technologyreview.com}} Conclusion: Agentic enterprises represent a paradigm shift in organizational intelligence, leveraging advanced AI to create more adaptive, responsive, and efficient business ecosystems.

**Google AI Answer (with Grounding):** Agentic AI frameworks enable the creation of autonomous AI agents capable of independent decision-making, task execution, and adaptation. Unlike traditional AI, which operates within predefined parameters, agentic AI systems pursue goals, learn from experience, and adjust to changing conditions. This makes them ideal for complex, multi-step tasks in enterprise environments. **Adoption Statistics:** Adoption rates are rapidly increasing. Various sources report that in 2025: * 79%–96% of organizations have adopted or plan to expand their use of AI agents. * 43% of companies allocate over half their AI budgets to agentic AI. * 62% of organizations project an ROI exceeding 100% from agentic AI. * Over 60% of new enterprise AI deployments include agentic capabilities. * 33% of enterprise software applications are expected to incorporate agentic AI by 2028. **Frameworks:** Several frameworks facilitate agentic AI development. Examples include: * Akka: An enterprise-grade platform offering orchestration, agents, memory, and streaming capabilities. It integrates with Langchain and Langgraph. * Microsoft Semantic Kernel: Suitable for enterprise integration due to its context-aware reasoning and business system connectivity. * CrewAI and AutoGen: Designed for multi-agent collaboration. * OpenAI Agents: A lightweight framework for building agentic AIs. **Architectural Considerations:** Successful enterprise-level agentic AI deployments often follow a tiered architecture: * Foundation Tier: Establishes infrastructure, governance, and security. * Workflow Tier: Automates tasks using patterns like prompt chaining, routing, and parallelization. * Autonomous Tier: Enables goal-directed planning and independent decision-making. Architectural approaches include single-agent, multi-agent, vertical (hierarchical), horizontal (collaborative), and hybrid systems. Choosing the right framework depends on project needs, industry requirements, and organizational capabilities. Prioritizing simple, composable architectures is crucial for managing complexity and controlling costs. Key considerations include governance, security, and ethical compliance. **Real-time Web Search Results:** **Search Queries Used:** agentic enterprise definition, agentic enterprise adoption stats, agentic enterprise frameworks **Sources Found:** 13 web sources **Top Sources:** 1. [getgenerative.ai](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFwg7SoKdK-R9rzfh4sE6T33GMM7u-FpqkZGcGF44LFc4CP34Dwc0QyNE5UrSidTvvNWtNqICbfcEyPCPn7NR6zZtqHbsMfrn3F5xL721GI-EyQShtA_T6foNStH7ZNMfmZZOkNMr6EAk2yiUWdUx9Vgq_nCDk-pzEpWEe9SZciW9kr) 2. [aaysanalytics.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF4Mcbhm-lQshLa19xa1kgLWDQ1d3kwQCJhDalm8_i-G9rS4p4x4nMcqwfnF3pXCDM9B7HC1F0MusIOnTG99txm-q572H27fHZJ7kDYONP-xRQAiIxa2SBww0qToxhHcnMViJbxmFeiw0FTlJ5f9uLWy7mzBddQnRGYps6g6IQqUDE3yA==) 3. [aiacquisition.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFiAXOAYw1sCF6EXHr-QOecNyEUlqd-PEZdWIpLZjUkQST96kkA3EEvEhnMce8WmsOrbjx1v3dmWgbqnBx3sUjxPXVADLhN4UYSWo-ZX7x4V_rugbp1b7CSxcLO1JFRsUCnrD1xj54XltWdDtwR)