#azure power platform

[haley-ai40]

Haley A.I. emerges as a versatile intelligent assistant platform poised to revolutionize how we interact with technology. Unlike singular-purpose assistants, Haley A.I. boasts a broader range of features, making it a valuable tool for individuals and businesses alike. This comprehensive exploration delves into the potential applications, functionalities, and future directions of this innovative AI solution.

Please try this product Haley A.I.

Unveiling the Capabilities of Haley A.I.

Haley A.I. leverages the power of machine learning, natural language processing (NLP), and potentially large language models (LLMs) to deliver a multifaceted experience. Here's a closer look at some of its core functionalities:

Conversational Interface: Haley A.I. facilitates natural language interaction, allowing users to communicate through text or voice commands. This intuitive interface simplifies interactions and eliminates the need for complex navigation or code.

Task Automation: Streamline repetitive tasks by delegating them to Haley A.I. It can schedule meetings, set reminders, manage calendars, and handle basic data entry, freeing up valuable time for users to focus on more strategic endeavors.

Information Retrieval: Harness the power of Haley A.I. to access and process information. Users can ask questions on various topics, and Haley A.I. will utilize its internal knowledge base or external sources to provide relevant and accurate answers.

Decision Support: Haley A.I. can analyze data and generate insights to assist users in making informed decisions. This can involve summarizing complex reports, presenting data visualizations, or identifying potential trends.

Personalized Assistant: Haley A.I. can be customized to cater to individual needs and preferences. By learning user behavior and collecting data, it can offer personalized recommendations, automate frequently performed tasks, and tailor its responses for a more optimal experience.

Integrations: Extend Haley A.I.'s capabilities by integrating it with existing tools and platforms. Users can connect Haley A.I. to their calendars, email clients, CRM systems, or productivity tools, creating a unified workflow hub. 

Harnessing the Power of Haley A.I. in Different Domains

The versatility of Haley A.I. makes it applicable across various domains. Let's explore some potential use cases:

Personal Assistant: Stay organized and manage your daily life with Haley A.I. Utilize it for scheduling appointments, setting reminders, managing grocery lists, or controlling smart home devices.

Customer Service: Businesses can leverage Haley A.I. to provide 24/7 customer support. It can answer frequently asked questions, troubleshoot basic issues, and even direct users to relevant resources.

Employee Productivity: Enhance employee productivity by automating routine tasks and providing real-time information retrieval. Imagine a sales representative being able to access customer data and product information seamlessly through Haley A.I.

Education and Learning: Haley A.I. can become a personalized learning assistant, providing students with explanations, summarizing complex topics, and even offering practice exercises tailored to their needs.

Data Analysis and Decision Making: Businesses can utilize Haley A.I. to analyze large datasets, generate reports, and identify trends. This valuable information can be used to make data-driven decisions and optimize strategies.

These examples showcase the diverse applications of Haley A.I. As the technology evolves and integrates with more platforms, the possibilities will continue to expand.

The Underlying Technology: A Peek Inside the Engine

While the specific details of Haley A.I.'s technology remain undisclosed, we can make some educated guesses based on its functionalities. Here are some potential components:

Machine Learning: Machine learning algorithms likely power Haley A.I.'s ability to learn and adapt to user behavior. This allows it to personalize responses, offer better recommendations, and improve its performance over time.

Natural Language Processing (NLP): The ability to understand and respond to natural language is crucial for a conversational interface. NLP techniques enable Haley A.I. to interpret user queries, translate them into machine-understandable code, and generate human-like responses.

Large Language Models (LLMs): These powerful AI models could play a role in Haley A.I.'s information retrieval and processing capabilities. LLMs can access and analyze vast amounts of data, allowing Haley A.I. to provide comprehensive answers to user inquiries.

The specific implementation of these technologies likely varies depending on Haley A.I.'s specific architecture and the desired functionalities. However, understanding these underlying principles sheds light on how Haley A.I. delivers its intelligent assistant experience.

Conclusion

HaleyA.I. emerges as a versatile and promising intelligent assistant platform. Its ability to automate tasks, access information, and personalize its responses positions it to revolutionize how we interact with technology. As the technology evolves and integrates with more platforms, the possibilities will continue to expand. By harnessing the power of AI responsibly and ethically, Haley A.I. has the potential to transform the way we work, learn, and live.

[trndigital01]

MS Power Platform | TrnDigital

Azure Power Platform - TrnDigital offers Microsoft Power Platform services like Power Apps, Power BI, Power Automate, Power Virtual Agents to Build, Analyse and Automate processes that empower you to drive your business with data. Contact Us !

[atcuality1]

Simplify Transactions and Boost Efficiency with Our Cash Collection Application

Manual cash collection can lead to inefficiencies and increased risks for businesses. Our cash collection application provides a streamlined solution, tailored to support all business sizes in managing cash effortlessly. Key features include automated invoicing, multi-channel payment options, and comprehensive analytics, all of which simplify the payment process and enhance transparency. The application is designed with a focus on usability and security, ensuring that every transaction is traceable and error-free. With real-time insights and customizable settings, you can adapt the application to align with your business needs. Its robust reporting functions give you a bird’s eye view of financial performance, helping you make data-driven decisions. Move beyond traditional, error-prone cash handling methods and step into the future with a digital approach. With our cash collection application, optimize cash flow and enjoy better financial control at every level of your organization.

[jcmarchi]

Ivo Everts, Databricks: Enhancing open-source AI and improving data governance

New Post has been published on https://thedigitalinsider.com/ivo-everts-databricks-enhancing-open-source-ai-and-improving-data-governance/

Ivo Everts, Databricks: Enhancing open-source AI and improving data governance

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Ahead of AI & Big Data Expo Europe, AI News caught up with Ivo Everts, Senior Solutions Architect at Databricks, to discuss several key developments set to shape the future of open-source AI and data governance.

One of Databricks’ notable achievements is the DBRX model, which set a new standard for open large language models (LLMs).

“Upon release, DBRX outperformed all other leading open models on standard benchmarks and has up to 2x faster inference than models like Llama2-70B,” Everts explains. “It was trained more efficiently due to a variety of technological advances.

“From a quality standpoint, we believe that DBRX is one of the best open-source models out there and when we refer to ‘best’ this means a wide range of industry benchmarks, including language understanding (MMLU), Programming (HumanEval), and Math (GSM8K).”

The open-source AI model aims to “democratise the training of custom LLMs beyond a small handful of model providers and show organisations that they can train world-class LLMs on their data in a cost-effective way.”

In line with their commitment to open ecosystems, Databricks has also open-sourced Unity Catalog.

“Open-sourcing Unity Catalog enhances its adoption across cloud platforms (e.g., AWS, Azure) and on-premise infrastructures,” Everts notes. “This flexibility allows organisations to uniformly apply data governance policies regardless of where the data is stored or processed.”

Unity Catalog addresses the challenges of data sprawl and inconsistent access controls through various features:

Centralised data access management: “Unity Catalog centralises the governance of data assets, allowing organisations to manage access controls in a unified manner,” Everts states.

Role-Based Access Control (RBAC): According to Everts, Unity Catalog “implements Role-Based Access Control (RBAC), allowing organisations to assign roles and permissions based on user profiles.”

Data lineage and auditing: This feature “helps organisations monitor data usage and dependencies, making it easier to identify and eliminate redundant or outdated data,” Everts explains. He adds that it also “logs all data access and changes, providing a detailed audit trail to ensure compliance with data security policies.”

Cross-cloud and hybrid support: Everts points out that Unity Catalog “is designed to manage data governance in multi-cloud and hybrid environments” and “ensures that data is governed uniformly, regardless of where it resides.”

The company has introduced Databricks AI/BI, a new business intelligence product that leverages generative AI to enhance data exploration and visualisation. Everts believes that “a truly intelligent BI solution needs to understand the unique semantics and nuances of a business to effectively answer questions for business users.”

The AI/BI system includes two key components:

Dashboards: Everts describes this as “an AI-powered, low-code interface for creating and distributing fast, interactive dashboards.” These include “standard BI features like visualisations, cross-filtering, and periodic reports without needing additional management services.”

Genie: Everts explains this as “a conversational interface for addressing ad-hoc and follow-up questions through natural language.” He adds that it “learns from underlying data to generate adaptive visualisations and suggestions in response to user queries, improving over time through feedback and offering tools for analysts to refine its outputs.”

Everts states that Databricks AI/BI is designed to provide “a deep understanding of your data’s semantics, enabling self-service data analysis for everyone in an organisation.” He notes it’s powered by “a compound AI system that continuously learns from usage across an organisation’s entire data stack, including ETL pipelines, lineage, and other queries.”

Databricks also unveiled Mosaic AI, which Everts describes as “a comprehensive platform for building, deploying, and managing machine learning and generative AI applications, integrating enterprise data for enhanced performance and governance.”

Mosaic AI offers several key components, which Everts outlines:

Unified tooling: Provides “tools for building, deploying, evaluating, and governing AI and ML solutions, supporting predictive models and generative AI applications.”

Generative AI patterns: “Supports prompt engineering, retrieval augmented generation (RAG), fine-tuning, and pre-training, offering flexibility as business needs evolve.”

Centralised model management: “Model Serving allows for centralised deployment, governance, and querying of AI models, including custom ML models and foundation models.”

Monitoring and governance: “Lakehouse Monitoring and Unity Catalog ensure comprehensive monitoring, governance, and lineage tracking across the AI lifecycle.”

Cost-effective custom LLMs: “Enables training and serving custom large language models at significantly lower costs, tailored to specific organisational domains.”

Everts highlights that Mosaic AI’s approach to fine-tuning and customising foundation models includes unique features like “fast startup times” by “utilising in-cluster base model caching,” “live prompt evaluation” where users can “track how the model’s responses change throughout the training process,” and support for “custom pre-trained checkpoints.”

At the heart of these innovations lies the Data Intelligence Platform, which Everts says “transforms data management by using AI models to gain deep insights into the semantics of enterprise data.” The platform combines features of data lakes and data warehouses, utilises Delta Lake technology for real-time data processing, and incorporates Delta Sharing for secure data exchange across organisational boundaries.

Everts explains that the Data Intelligence Platform plays a crucial role in supporting new AI and data-sharing initiatives by providing:

A unified data and AI platform that “combines the features of data lakes and data warehouses into a single architecture.”

Delta Lake for real-time data processing, ensuring “reliable data governance, ACID transactions, and real-time data processing.”

Collaboration and data sharing via Delta Sharing, enabling “secure and open data sharing across organisational boundaries.”

Integrated support for machine learning and AI model development with popular libraries like MLflow, PyTorch, and TensorFlow.

Scalability and performance through its cloud-native architecture and the Photon engine, “an optimised query execution engine.”

As a key sponsor of AI & Big Data Expo Europe, Databricks plans to showcase their open-source AI and data governance solutions during the event.

“At our stand, we will also showcase how to create and deploy – with Lakehouse apps – a custom GenAI app from scratch using open-source models from Hugging Face and data from Unity Catalog,” says Everts.

“With our GenAI app you can generate your own cartoon picture, all running on the Data Intelligence Platform.”

Databricks will be sharing more of their expertise at this year’s AI & Big Data Expo Europe. Swing by Databricks’ booth at stand #280 to hear more about open AI and improving data governance.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, ai expo, artificial intelligence, data intelligence platform, databricks, dbrx, ivo everts, large language models, llm, mosaic ai, open source, open-source, unity catalog

[srinathpega]

Microsoft Dynamics 365 API Access token in Postman

Introduction Dynamics 365 Online exposes Web API endpoints, making integration simple. The most difficult part, though, is authenticating since Dynamics 365 Online uses OAuth2.0. Every HTTP request to the Web API requires a valid access bearer token that is issued by Microsoft Azure Active Directory. In this blog, I will talk about how to use Dynamics 365 Application User (Client ID and Secret…

[snakconsultancyservices]

Discover how our team's deep expertise in Microsoft Azure can help you build, deploy, and manage modern web apps, AI solutions, data services, and more

[manageditserviceslosangeles]

(AI) with Azure AI. Microsoft Azure’s comprehensive suite of AI services is paving the way for businesses to compete and thrive. Unlock the potential of AI with Azure AI’s diverse range of tools and services. Enhance decision-making, streamline operations, and discover new opportunities. Let’s not forget about Azure OpenAI, a cutting-edge collaboration between Microsoft and OpenAI, a renowned AI research lab. 

Start here to unravel the potential of Azure OpenAI for the best of both worlds. Harness the incredible language model of Azure Open AI, paired with the unbeatable scalability, security, and user-friendliness of the Azure platform. This dynamic partnership opens up endless business opportunities to revolutionize applications, enhance customer experiences, and ignite innovation. Keep reading to delve into the fundamentals of Azure AI and unlock a new realm of possibilities. 

GET STARTED WITH MICROSOFT AZURE AI

Table of Contents  hide 

1 What is Azure AI? What Services come under this?

1.1 What Is the Difference Between Azure and OpenAI?

1.2 What are the latest Azure AI features launched?

1.3 Addressing Challenges in Azure AI

1.3.1 What is there for IT Leaders?

1.4 How Can Azure AI Help Protecting and Building Data Insight for Your Business?

1.5 How can Industries Benefit from Azure AI?

1.5.1 Ready to Maximize Microsoft Azure?

1.5.2 How can you begin your journey with Azure AI?

1.5.3 Why choose ECF Data for the next-generation AI project?

[generateawareness]

[ibarrau]

[Fabric] ¿Por donde comienzo? OneLake intro

Microsoft viene causando gran revuelo desde sus lanzamientos en el evento MSBuild 2023. Las demos, videos, artículos y pruebas de concepto estan volando para conocer más y más en profundidad la plataforma.

Cada contenido que vamos encontrando nos cuenta sobre algun servicio o alguna feature, pero muchos me preguntaron "¿Por donde empiezo?" hay tantos nombres de servicios y tecnologías grandiosas que aturden un poco.

En este artículo vamos a introducirnos en el primer concepto para poder iniciar el camino para comprender a Fabric. Nos vamos a introducir en OneLake.

Si aún no conoces nada de Fabric te invito a pasar por mi post introductorio así te empapas un poco antes de comenzar.

Introducción

Para introducirnos en este nuevo mundo me gustaría comenzar aclarando que es necesaria una capacidad dedicada para usar Fabric. Hoy esto no es un problema para pruebas puesto que Microsoft liberó Fabric Trials que podemos activar en la configuración de inquilinos (tenant settings) de nuestro portal de administración.

Fabric se organiza separando contenido que podemos crear según servicios nombrados como focos de disciplinas o herramientas como PowerBi, Data Factory, Data Science, Data Engineering, etc. Estos son formas de organizar el contenido para visualizar lo que nos pertine en la diaria. Sin embargo, al final del día el proyecto que trabajamos esta en un workspace que tiene contenidos varios como: informes, conjuntos de datos, lakehouse, sql endpoints, notebooks, pipelines, etc.

Para poder comenzar a trabajar necesitaremos entender LakeHouse y OneLake.

Podemos pensar en OneLake como un storage único por organización. Esta única fuente de datos puede tener proyectos organizados por Workspaces. Los proyectos permiten crear sub lagos del único llamado LakeHouse. El contenido LakeHouse no es más que una porción de gran OneLake. Los LakeHouses combinan las funcionalidades analíticas basadas en SQL de un almacenamiento de datos relacional y la flexibilidad y escalabilidad de un Data Lake. La herramienta permite almacenar todos los formatos de archivos de datos conocidos y provee herramientas analíticas para leerlos. Veamos una imagen como referencia estructural:

Beneficios

Usan motores Spark y SQL para procesar datos a gran escala y admitir el aprendizaje automático o el análisis de modelado predictivo.

Los datos se organizan en schema-on-read format, lo que significa que se define el esquema según sea necesario en lugar de tener un esquema predefinido.

Admiten transacciones ACID (Atomicidad, Coherencia, Aislamiento, Durabilidad) a través de tablas con formato de Delta Lake para conseguir coherencia e integridad en los datos.

Crear un LakeHouse

Lo primero a utilizar para aprovechar Fabric es su OneLake. Sus ventajas y capacidades será aprovechadas si alojamos datos en LakeHouses. Al crear el componente nos encontramos con que tres componentes fueron creados en lugar de uno:

Lakehouse contiene los metadatos y la porción el almacenamiento storage del OneLake. Ahi encontraremos un esquema de archivos carpetas y datos de tabla para pre visualizar.

Dataset (default) es un modelo de datos que crea automáticamente y apunta a todas las tablas del LakeHouse. Se pueden crear informes de PowerBi a partir de este conjunto. La conexión establecida es DirectLake. Click aqui para conocer más de direct lake.

SQL Endpoint como su nombre lo indica es un punto para conectarnos con SQL. Podemos entrar por plataforma web o copiar sus datos para conectarnos con una herramienta externa. Corre Transact-SQL y las consultas a ejecutar son únicamente de lectura.

Lakehouse

Dentro de este contenido creado, vamos a visualizar dos separaciones principales.

Archivos: esta carpeta es lo más parecido a un Data Lake tradicional. Podemos crear subcarpetas y almacenar cualquier tipo de archivos. Podemos pensarlo como un filesystem para organizar todo tipo de archivos que querramos analizar. Aquellos archivos que sean de formato datos como parquet o csv, podrán ser visualizados con un simple click para ver una vista previa del contenido. Como muestra la imagen, aquí mismo podemos trabajar una arquitectura tradicional de medallón (Bronze, Silver, Gold). Aquí podemos validar que existe un único lakehouse analizando las propiedades de un archivo, si las abrimos nos encontraremos con un ABFS path como en otra tecnología Data Lake.

Tablas: este espacio vendría a representar un Spark Catalog, es decir un metastore de objetos de data relacionales como son las tablas o vistas de un motor de base de datos. Esta basado en formato de tablas DeltaLake que es open source. Delta nos permite definir un schema de tablas en nuestro lakehouse que podrá ser consultado con SQL. Aquí no hay subcarpetas. Aqui solo hay un Meta store tipo base de datos. De momento, es uno solo por LakeHouse.

Ahora que conocemos más sobre OneLake podemos iniciar nuestra expedición por Fabric. El siguiente paso sería la ingesta de datos. Podes continuar leyendo por varios lugares o esperar nuestro próximo post sobre eso :)

[biglisbonnews]

The future of business is here: How industries are unlocking AI innovation and greater value with the Microsoft Cloud Over the past six months, I have witnessed the staggering speed and scale of generative AI technology adoption, and how it has opened doors for organizations to imagine new ways to solve business, societal, and sustainability challenges. For many with modernized data estates fortified with the Microsoft Cloud, advanced AI technology is already unlocking innovation... The post The future of business is here: How industries are unlocking AI innovation and greater value with the Microsoft Cloud appeared first on The Official Microsoft Blog. https://blogs.microsoft.com/blog/2023/07/24/the-future-of-business-is-here-how-industries-are-unlocking-ai-innovation-and-greater-value-with-the-microsoft-cloud/

[code-life]

What is Microsoft Power Automate

In this article, I will know what is power automate, And Microsoft Power Automate, formerly known as Microsoft Flow, is a cloud-based service that allows users to create automated workflows across a wide range of applications and services. It is part of t

In this article, I will know what is power automate, And Microsoft Power Automate, formerly known as Microsoft Flow, is a cloud-based service that allows users to create automated workflows across a wide range of applications and services. It is part of the Microsoft Power Platform suite of business applications and is designed to help organizations automate routine tasks and processes. For more…

[sainacloudsoftwaresolutions]

"Part-1: Introduction to Integrating External Applications with Dynamics 365 for Operations: Step-by-Step Guide"

Welcome to our latest tutorial, where we'll guide you through the process of creating a new service method in Dynamics 365 for Operations and exposing it for external application usage. In this comprehensive tutorial, we'll demonstrate how to seamlessly integrate external applications with your Dynamics 365 environment, ensuring smooth data exchange and enhanced functionality.

[jcmarchi]

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

New Post has been published on https://thedigitalinsider.com/datasets-matter-the-battle-between-open-and-closed-generative-ai-is-not-only-about-models-anymore/

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

Two major open source datasets were released this week.

Created Using DALL-E

Next Week in The Sequence:

Edge 403: Our series about autonomous agents continues covering memory-based planning methods. The research behind the TravelPlanner benchmark for planning in LLMs and the impressive MemGPT framework for autonomous agents.

The Sequence Chat: A super cool interview with one of the engineers behind Azure OpenAI Service and Microsoft CoPilot.

Edge 404: We dive into Meta AI’s amazing research for predicting multiple tokens at the same time in LLMs.

You can subscribe to The Sequence below:

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

📝 Editorial: Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

The battle between open and closed generative AI has been at the center of industry developments. From the very beginning, the focus has been on open vs. closed models, such as Mistral and Llama vs. GPT-4 and Claude. Less attention has been paid to other foundational aspects of the model lifecycle, such as the datasets used for training and fine-tuning. In fact, one of the limitations of the so-called open weight models is that they don’t disclose the training datasets and pipeline. What if we had high-quality open source datasets that rival those used to pretrain massive foundation models?

Open source datasets are one of the key aspects to unlocking innovation in generative AI. The costs required to build multi-trillion token datasets are completely prohibitive to most organizations. Leading AI labs, such as the Allen AI Institute, have been at the forefront of this idea, regularly open sourcing high-quality datasets such as the ones used for the Olmo model. Now it seems that they are getting some help.

This week, we saw two major efforts related to open source generative AI datasets. Hugging Face open-sourced FineWeb, a 44TB dataset of 15 trillion tokens derived from 96 CommonCrawl snapshots. Hugging Face also released FineWeb-Edu, a subset of FineWeb focused on educational value. But Hugging Face was not the only company actively releasing open source datasets. Complementing the FineWeb release, AI startup Zyphra released Zyda, a 1.3 trillion token dataset for language modeling. The construction of Zyda seems to have focused on a very meticulous filtering and deduplication process and shows remarkable performance compared to other datasets such as Dolma or RedefinedWeb.

High-quality open source datasets are paramount to enabling innovation in open generative models. Researchers using these datasets can now focus on pretraining pipelines and optimizations, while teams using those models for fine-tuning or inference can have a clearer way to explain outputs based on the composition of the dataset. The battle between open and closed generative AI is not just about models anymore.

🔎 ML Research

Extracting Concepts from GPT-4

OpenAI published a paper proposing an interpretability technique to understanding neural activity within LLMs. Specifically, the method uses k-sparse autoencoders to control sparsity which leads to more interpretable models —> Read more.

Transformer are SSMs

Researchers from Princeton University and Carnegie Mellon University published a paper outlining theoretical connections between transformers and SSMs. The paper also proposes a framework called state space duality and a new architecture called Mamba-2 which improves the performance over its predecessors by 2-8x —> Read more.

Believe or Not Believe LLMs

Google DeepMind published a paper proposing a technique to quantify uncertainty in LLM responses. The paper explores different sources of uncertainty such as lack of knowledge and randomness in order to quantify the reliability of an LLM output —> Read more.

CodecLM

Google Research published a paper introducing CodecLM, a framework for using synthetic data for LLM alignment in downstream tasks. CodecLM leverages LLMs like Gemini to encode seed intrstructions into the metadata and then decodes it into synthetic intstructions —> Read more.

TinyAgent

Researchers from UC Berkeley published a detailed blog post about TinyAgent, a function calling tuning method for small language models. TinyAgent aims to enable function calling LLMs that can run on mobile or IoT devices —> Read more.

Parrot

Researchers from Shanghai Jiao Tong University and Microsoft Research published a paper introducing Parrot, a framework for correlating multiple LLM requests. Parrot uses the concept of a Semantic Variable to annotate input/output variables in LLMs to enable the creation of a data pipeline with LLMs —> Read more.

🤖 Cool AI Tech Releases

FineWeb

HuggingFace open sourced FineWeb, a 15 trillion token dataset for LLM training —> Read more.

Stable Audion Open

Stability AI open source Stable Audio Open, its new generative audio model —> Read more.

Mistral Fine-Tune

Mistral open sourced mistral-finetune SDK and services for fine-tuning models programmatically —> Read more.

Zyda

Zyphra Technologies open sourced Zyda, a 1.3 trillion token dataset that powers the version of its Zamba models —> Read more.

🛠 Real World AI

Salesforce discusses their use of Amazon SageMaker in their Einstein platform —> Read more.

��AI Radar

Cisco announced a $1B AI investment fund with some major positions in companies like Cohere, Mistral and Scale AI.

Cloudera acquired AI startup Verta.

Databricks acquired data management company Tabular.

Tektonic, raised $10 million to build generative agents for business operations —> Read more.

AI task management startup Hoop raised $5 million.

Galileo announced Luna, a family of evaluation foundation models.

Browserbase raised $6.5 million for its LLM browser-based automation platform.

AI artwork platform Exactly.ai raised $4.3 million.

Sirion acquired AI document management platform Eigen Technologies.

Asana added AI teammates to complement task management capabilities.

Eyebot raised $6 million for its AI-powered vision exams.

AI code base platform Greptile raised a $4 million seed round.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

[srinathpega]

Power Platform User Permanently deletion from Azure Active Directory

Power Platform User Permanently deletion from Azure Active Directory

Introduction The methods of user deletion in Microsoft Power Platform from Microsoft 365 admin center, Azure Active Directory (Azure AD), and Power Platform admin center. In this article will learn the step-by-step process of the deletion of the user, This is in preview, the time of writing this blog. Delete users in Microsoft 365 admin center (Soft Delete) Go to Microsoft 365 admin center.…