#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]

How Microsoft’s AI Ecosystem Outperforms Salesforce and AWS

New Post has been published on https://thedigitalinsider.com/how-microsofts-ai-ecosystem-outperforms-salesforce-and-aws/

How Microsoft’s AI Ecosystem Outperforms Salesforce and AWS

AI agents are autonomous systems designed to perform tasks that would typically require human involvement. By using advanced algorithms, these agents can handle a wide range of functions, from answering customer inquiries to predicting business trends. This automation not only streamlines repetitive processes but also allows human workers to focus on more strategic and creative activities. Today, AI agents are playing an important role in enterprise automation, delivering benefits such as increased efficiency, lower operational costs, and faster decision-making.

Advancements in generative AI and predictive AI have further enhanced the capabilities of these agents. Generative AI allows agents to create new content, like personalized email responses or actionable insights, while predictive AI helps businesses forecast trends and outcomes based on historical data.

The adoption of AI agents has increased, with over 100,000 organizations now utilizing Microsoft’s AI solutions to automate their processes. According to a recent study commissioned by Microsoft and IDC, businesses are seeing significant returns from their investments in AI. For every dollar spent on generative AI, companies are realizing an average of $3.70 in return. This signifies the immense potential AI has to transform business processes and open new opportunities for growth.

Microsoft’s AI solutions are a key player in the rapidly evolving AI field. Over 85% of Fortune 500 companies are already using Microsoft’s AI capabilities, making the company a leader in AI-driven enterprise transformation. Microsoft helps organizations enhance employee experience, improve customer engagement, transform business processes, and bring innovation and growth across industries.

Microsoft’s AI Agent Ecosystem: A Comprehensive and Scalable Solution

Microsoft’s AI solutions are built on its strong foundation in cloud computing and are designed to address the needs of large organizations. These solutions integrate effectively with Microsoft’s existing products, such as Azure, Office 365, and Dynamics 365, ensuring businesses can use AI without disrupting their current workflows. By incorporating AI into its suite of enterprise tools, Microsoft provides a comprehensive platform that supports various organizational needs.

A key development in Microsoft’s AI efforts is the introduction of Copilot Studio. This platform enables businesses to create and deploy customized AI agents with ease, using a no-code interface that makes it accessible even for those without technical expertise. Leveraging a wide range of large language models, these AI agents can perform complex tasks across multiple domains, such as customer support and sales forecasting.

Microsoft’s AI agents’ flexibility and adaptability make them highly effective across various industries. These agents help automate tasks such as customer service and supply chain management. They can handle large volumes of customer inquiries, predict inventory needs, and improve workflows, ultimately increasing operational efficiency and providing real-time solutions.

Real-World Use Cases of Microsoft AI Agents

Microsoft’s AI agents are becoming critical tools for organizations aiming to improve their operations. One of the primary use cases is in customer service, where AI-powered chatbots and virtual assistants handle routine inquiries. These agents use Natural Language Processing (NLP) to communicate with customers conversationally, offering instant responses and reducing the need for human intervention. This not only reduces costs but also improves customer satisfaction by resolving issues more quickly. For instance, Citibank uses AI-powered virtual assistants for tasks like checking balances and making payments, while Microsoft’s Dynamics 365 helps businesses by analyzing customer interactions and suggesting solutions automatically.

In sales and marketing, Microsoft’s AI agents help automate lead generation and strengthen customer relationships. By analyzing customer behavior, these agents can identify potential leads and suggest personalized marketing strategies to increase sales. They also support predictive analytics, allowing businesses to anticipate market trends, customer preferences, and sales patterns. This helps companies make better, data-driven decisions, improving overall performance.

For example, Dynamics 365 Sales automates lead generation, scores potential leads, and recommends the subsequent best actions for sales teams. Analyzing customer data can identify leads most likely to convert, helping prioritize efforts for higher conversion rates.

Additionally, Dynamics 365 Customer Insights consolidates data from multiple sources to provide a comprehensive view of each customer. It uses AI to predict customer needs, identify upsell opportunities, and suggest personalized engagement strategies, helping businesses optimize marketing efforts and strengthen customer relationships.

In supply chain management, AI agents, such as Dynamic 365 Supply Chain Management, help businesses forecast demand, track inventory, and optimize logistics. This enables companies to make proactive adjustments to their supply chains, ensuring timely deliveries and reducing excess stock. Whether managing warehouse operations or optimizing distribution networks, Microsoft’s AI agents provide valuable insights that help businesses lower costs and enhance efficiency.

Comparing Microsoft’s AI Agents with Competitors: Salesforce and AWS

While Microsoft’s AI ecosystem is known for its strong integration, scalability, and focus on enterprise needs, its competitors also offer robust AI solutions, though with different strengths and limitations.

Salesforce, recognized for its CRM and marketing tools, integrates AI into its platform through Einstein GPT and Agentforce. Einstein GPT is a generative AI tool designed to automate customer interactions, personalize content, and enhance service offerings. It works effectively within the Salesforce ecosystem, making it a better choice for companies already using Salesforce for customer relationship management (CRM). However, Salesforce’s AI solutions are more specialized, with a primary focus on customer relationships. They provide a different breadth of features in areas like supply chain management or internal operations.

On the other hand, AWS offers a broad range of AI tools, such as Amazon SageMaker and AWS DeepRacer, which provide businesses the flexibility to build custom AI models. SageMaker, for example, is a robust platform that allows developers and data scientists to create tailored AI models for specific business needs. While AWS excels in offering customizable AI solutions, it lacks the pre-built, ready-to-deploy agents that Microsoft provides. This means businesses may need specialized teams of data scientists or AI experts to get the most out of AWS’s tools.

Both Salesforce and AWS have valuable AI capabilities, but they offer different levels of integrated, enterprise-grade solutions than Microsoft. For businesses looking for a broad, scalable AI ecosystem that easily integrates with existing systems, Microsoft’s offering emerges as the more comprehensive and accessible choice.

Why Microsoft’s AI Agent Ecosystem Outpaces Its Competitors

Microsoft’s AI ecosystem offers distinct advantages that set it apart from its competitors, particularly for large organizations. One key strength is its enterprise focus. With extensive experience supporting the needs of large businesses, Microsoft has designed its AI solutions to integrate with over 1,400 enterprise systems. This ensures that companies can adopt AI without disrupting their existing operations.

Another significant advantage is Microsoft’s commitment to security and governance. The company strongly emphasizes compliance with global regulations, such as GDPR, giving businesses confidence when deploying AI. Microsoft’s robust security features ensure data protection and help ensure that AI systems are used responsibly and ethically.

Microsoft also provides a wide range of pre-built AI agents tailored to common enterprise use cases, such as customer service, sales automation, and marketing. These agents are easy to deploy and integrate, reducing the time required to implement AI solutions and minimizing resource investment.

Finally, scalability is a crucial feature of Microsoft’s AI platform. Whether for a small startup or a large multinational corporation, the ecosystem is designed to grow with the business, offering the flexibility and performance necessary to meet evolving demands. This makes Microsoft’s AI ecosystem a comprehensive and reliable choice for companies looking to integrate AI at scale.

The Bottom Line

Microsoft’s AI agent ecosystem offers a comprehensive, scalable, and integrated solution for businesses looking to enhance their operations through automation and data-driven insights. With its strong focus on enterprise needs, robust security features, and easy integration with existing systems, Microsoft’s AI solutions are helping organizations streamline processes, improve customer experience, and drive growth.

The wide array of pre-built AI agents for tasks like customer service, sales, and supply chain management ensures that businesses can quickly adopt AI with minimal disruption. With the widespread use of AI in enterprise operations, Microsoft stays ahead by providing a reliable and efficient solution for businesses looking to embrace AI and drive digital transformation.

[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…

[atcuality1]

Custom AWS Solutions for Modern Enterprises - Atcuality

Amazon Web Services offer an unparalleled ecosystem of cloud computing tools that cater to businesses of all sizes. At ATCuality, we understand that no two companies are the same, which is why we provide custom Amazon Web Services solutions tailored to your specific goals. From designing scalable architectures to implementing cutting-edge machine learning capabilities, our AWS services ensure that your business stays ahead of the curve. The flexibility of Amazon Web Services allows for easy integration with your existing systems, paving the way for seamless growth and enhanced efficiency. Let us help you harness the power of AWS for your enterprise.

[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

[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.