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guida-ai · 1 year ago

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AutoAI: il futuro dell'analisi e dell'automazione dei dati

Nel mondo frenetico di oggi, i dati sono ovunque. Dai social media all'e-commerce, le aziende raccolgono e analizzano enormi quantità di dati per ottenere informazioni dettagliate sul comportamento dei clienti, migliorare prodotti e servizi e prendere decisioni aziendali migliori. Tuttavia, il volume e la complessità dei dati possono spesso essere travolgenti, rendendo difficile per le aziende sfruttarne tutto il potenziale. È qui che entra in gioco AutoAI. AutoAI, o Intelligenza Artificiale Automatizzata, è il futuro dell'analisi e dell'automazione dei dati. È una tecnologia che utilizza algoritmi di machine learning e intelligenza artificiale per automatizzare il processo di analisi dei dati, rendendolo più veloce, più accurato e più accessibile alle aziende di tutte le dimensioni. L'AutoAI ha il potenziale per rivoluzionare il modo in cui le aziende utilizzano i dati, ed ecco perché.

1. Velocità ed efficienza

Uno dei maggiori vantaggi dell’AutoAI è la sua velocità ed efficienza. I metodi tradizionali di analisi dei dati possono richiedere molto tempo e lavoro, richiedendo a un team di data scientist di analizzare e interpretare manualmente i dati. Con AutoAI, gran parte di questo processo è automatizzato, consentendo alle aziende di analizzare set di dati di grandi dimensioni in una frazione del tempo necessario con i metodi tradizionali. Ciò significa che le aziende possono prendere decisioni più rapide, reagire più rapidamente alle tendenze del mercato e stare al passo con la concorrenza.

2. Accuratezza e precisione

L'AutoAI è anche in grado di fornire approfondimenti più accurati e precisi rispetto ai metodi tradizionali. Gli algoritmi di apprendimento automatico utilizzati da AutoAI sono progettati per rilevare modelli e tendenze nei dati che potrebbero essere difficili da identificare per gli esseri umani. Ciò significa che le aziende possono prendere decisioni più informate sulla base di dati affidabili, anziché affidarsi all’intuito o alle congetture. Ciò può portare a risultati migliori e ad un vantaggio competitivo sul mercato.

3. Accessibilità e facilità d'uso

Un altro vantaggio di AutoAI è la sua accessibilità e facilità d'uso. Mentre i metodi tradizionali di analisi dei dati spesso richiedono competenze e competenze specializzate, AutoAI è progettata per essere facile da usare e accessibile a una gamma più ampia di individui all’interno di un’organizzazione. Ciò significa che le aziende non hanno bisogno di avere un team dedicato di data scientist per beneficiare della potenza dell’analisi dei dati. Possono invece consentire ai propri dipendenti esistenti di utilizzare gli strumenti AutoAI per ottenere informazioni approfondite e prendere decisioni basate sui dati.

4. Scalabilità e adattabilità

L'AutoAI è inoltre altamente scalabile e adattabile alle diverse esigenze aziendali. Che si tratti di una piccola startup o di una grande impresa, AutoAI può essere personalizzata per soddisfare i suoi requisiti specifici. Ciò significa che le aziende possono utilizzare l'AutoAI per analizzare i dati relativi alle vendite, al marketing, al comportamento dei clienti e altro ancora, indipendentemente dalle loro dimensioni o dal settore. Questo livello di flessibilità e adattabilità rende AutoAI una risorsa preziosa per le aziende che desiderano crescere e avere successo nell’era digitale.

5. Potenziale futuro

Guardando al futuro, il potenziale dell’AutoAI è enorme. Man mano che l’apprendimento automatico e l’intelligenza artificiale continuano ad evolversi, le capacità dell’AutoAI diventeranno solo più avanzate. Ciò significa che le aziende saranno in grado di ottenere informazioni ancora più approfondite sui propri dati, automatizzare attività più complesse e sbloccare nuove opportunità di innovazione e crescita. In futuro, l’AutoAI potrebbe diventare uno strumento indispensabile per le aziende che cercano di prosperare in un mondo sempre più basato sui dati. Nel complesso, l’AutoAI è pronta a rivoluzionare il modo in cui le aziende analizzano e utilizzano i dati. Grazie alla sua velocità, precisione, accessibilità, scalabilità e potenziale futuro, AutoAI offre alle aziende un modo efficace per sfruttare tutto il potenziale dei propri dati. Negli anni a venire, possiamo aspettarci di vedere l’AutoAI svolgere un ruolo centrale nell’aiutare le aziende a prendere decisioni più intelligenti, promuovere l’innovazione e raggiungere il successo nell’era digitale. Read the full article

#AutoAI #dati #futuro

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sajzath · 2 years ago

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From Assistive to Autonomous: The Evolution of Cars

The invention of the automobile was a landmark moment that transformed the way we travel and connect with the world. Over the decades, cars have come a long way, evolving from basic assistive vehicles to sophisticated autonomous machines. This article delves into the captivating journey of cars, tracing the advancements in technology that have paved the path from assistive driving to the promise…

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parsleysagerosemarytimemachine · 4 months ago

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On a related horrible example:

Was with my mom to see her financial/investment person (for retirement planning etc) and seeing them use the google autoAI suggestions to answer a 'Can we do this with money?' question was a real eye-twitching moment.

(Luckily their younger colleague was in the room to say that maybe they should scroll down to get an actual source, as opposed to whatever the autocomplete guessed at...)

i hate seeing people drink the openai/chatgpt koolaid 😭😭😭 genuinely feels like watching someone get seduced by scientology or qanon or something. like girl help it's not intelligent it's Big Autocomplete it's crunching numbers it's not understanding things i fuckign promise you. like ohhh my god the marketing hype fuckign GOT you

#which yeah they've been a good help managing things #and were mostly just wanting to verify their intuition as to the answer #but that's when it's most likely to trick you!

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salva-escuelafeliz · 5 months ago

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281 Escuelafeliz. Proyecto Hombre Navarra Quiero ser y os invito a ser, buscadores de “VERDAD” no de la tuya, no de la mía, buscar a ella, a la “VERDAD” como forma y camino de llegar a la FELICIDAD. Cada persona es única, en nuestro momento, desarrollo, situación, por eso cada persona… que lea, que mire, que escuche, que reflexione, que medite, que se quede con aquello que le resuene, que le llegue más adentro, sin rechazos, pues lo que a mi no me dice nada o no me llega, a otra persona le puede ayudar, emocionar, hacerle crecer. Ser Felices, Hacer Felices, Vivir Felices. https://escuelafeliz.org/category/a-autoay... -------------------------------o------------------------------------- ¡Estamos de aniversario! El 11 de febrero nació la Fundación Proyecto Hombre Navarra. 33 años a vuestro lado. Muchas gracias a todas las personas que nos han acompañado en este camino. Os dejamos este vídeo y os invitamos a seguir imaginando. Imagen corporativa para Fundación Proyecto Hombre Navarra. Un proyecto que supuso una satisfacción colectiva enorme, con la poesía de Izaskun Mata Coloma, coreografía de Ainhoa Carrera Igeltz (Baobab Danza) y bailarines/as que sin lugar a dudas transmitieron el mensaje desde lo más profundo y sincero de cada persona. Encuéntranos en: http://escuelafeliz.org/

#youtube

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govindhtech · 10 months ago

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IBM Watsonx.governance Removes Gen AI Adoption Obstacles

The IBM Watsonx platform, which consists of Watsonx.ai, Watsonx.data, and Watsonx.governance, removes obstacles to the implementation of generative AI.

Complex data environments, a shortage of AI-skilled workers, and AI governance frameworks that consider all compliance requirements put businesses at risk as they explore generative AI’s potential.

Generative AI requires even more specific abilities, such as managing massive, diverse data sets and navigating ethical concerns due to its unpredictable results.

IBM is well-positioned to assist companies in addressing these issues because of its vast expertise using AI at scale. The IBM Watsonx AI and data platform provides solutions that increase the accessibility and actionability of AI while facilitating data access and delivering built-in governance, thereby addressing skills, data, and compliance challenges. With the combination, businesses may fully utilize AI to accomplish their goals.

Forrester Research’s The Forrester Wave: AI/ML Platforms, Q3, 2024, by Mike Gualtieri and Rowan Curran, published on August 29, 2024, is happy to inform that IBM has been rated as a strong performer.

IBM is said to provide a “one-stop AI platform that can run in any cloud” by the Forrester Report. Three key competencies enable IBM Watsonx to fulfill its goal of becoming a one-stop shop for AI platforms: Using Watsonx.ai, models, including foundation models, may be trained and used. To store, process, and manage AI data, use watsonx.data. To oversee and keep an eye on all AI activity, use watsonx.governance.

Watsonx.ai

Watsonx.ai: a pragmatic method for bridging the AI skills gap

The lack of qualified personnel is a significant obstacle to AI adoption, as indicated by IBM’s 2024 “Global AI Adoption Index,” where 33% of businesses cite this as their top concern. Developing and implementing AI models calls both certain technical expertise as well as the appropriate resources, which many firms find difficult to come by. By combining generative AI with conventional machine learning, IBM Watsonx.ai aims to solve these problems. It consists of runtimes, models, tools, and APIs that make developing and implementing AI systems easier and more scalable.

Let’s say a mid-sized retailer wants to use demand forecasting powered by artificial intelligence. Creating, training, and deploying machine learning (ML) models would often require putting together a team of data scientists, which is an expensive and time-consuming procedure. The reference customers questioned for The Forrester Wave AI/ML Platforms, Q3 2024 report said that even enterprises with low AI knowledge can quickly construct and refine models with watsonx.ai’s “easy-to-use tools for generative AI development and model training .”

For creating, honing, and optimizing both generative and conventional AI/ML models and applications, IBM Watsonx.ai offers a wealth of resources. To train a model for a specific purpose, AI developers can enhance the performance of pre-trained foundation models (FM) by fine-tuning parameters efficiently through the Tuning Studio. Prompt Lab, a UI-based tools environment offered by Watsonx.ai, makes use of prompt engineering strategies and conversational engagements with FMs.

Because of this, it’s simple for AI developers to test many models and learn which one fits the data the best or what needs more fine tuning. The watsonx.ai AutoAI tool, which uses automated machine learning (ML) training to evaluate a data set and apply algorithms, transformations, and parameter settings to produce the best prediction models, is another tool available to model makers.

It is their belief that the acknowledgement from Forrester further confirms IBM’s unique strategy for providing enterprise-grade foundation models, assisting customers in expediting the integration of generative AI into their operational processes while reducing the risks associated with foundation models.

The watsonx.ai AI studio considerably accelerates AI deployment to suit business demands with its collection of pre-trained, open-source, and bespoke foundation models from third parties, in addition to its own flagship Granite series. Watsonx.ai makes AI more approachable and indispensable to business operations by offering these potent tools that help companies close the skills gap in AI and expedite their AI initiatives.

Watsonx.data

Real-world methods for addressing data complexity using Watsonx.data

As per 25% of enterprises, data complexity continues to be a significant hindrance for businesses attempting to utilize artificial intelligence. It can be extremely daunting to deal with the daily amount of data generated, particularly when it is dispersed throughout several systems and formats. These problems are addressed by IBM Watsonx.Data, an open, hybrid, and controlled data store that is suitable for its intended use.

Its open data lakehouse architecture centralizes data preparation and access, enabling tasks related to artificial intelligence and analytics. Consider, for one, a multinational manufacturing corporation whose data is dispersed among several regional offices. Teams would have to put in weeks of work only to prepare this data manually in order to consolidate it for AI purposes.

By providing a uniform platform that makes data from multiple sources more accessible and controllable, Watsonx.data can help to simplify this. To make the process of consuming data easier, the Watsonx platform also has more than 60 data connections. The software automatically displays summary statistics and frequency when viewed data assets. This makes it easier to quickly understand the content of the datasets and frees up a business to concentrate on developing its predictive maintenance models, for example, rather than becoming bogged down in data manipulation.

Additionally, IBM has observed via a number of client engagement projects that organizations can reduce the cost of data processing by utilizing Watsonx.data‘s workload optimization, which increases the affordability of AI initiatives.

In the end, AI solutions are only as good as the underlying data. A comprehensive data flow or pipeline can be created by combining the broad capabilities of the Watsonx platform for data intake, transformation, and annotation. For example, the platform’s pipeline editor makes it possible to orchestrate operations from data intake to model training and deployment in an easy-to-use manner.

As a result, the data scientists who create the data applications and the ModelOps engineers who implement them in real-world settings work together more frequently. Watsonx can assist enterprises in managing their complex data environments and reducing data silos, while also gaining useful insights from their data projects and AI initiatives. Watsonx does this by providing comprehensive data management and preparation capabilities.

Watsonx.Governance

Using Watsonx.Governance to address ethical issues: fostering openness to establish trust

With ethical concerns ranking as a top obstacle for 23% of firms, these issues have become a significant hurdle as AI becomes more integrated into company operations. In industries like finance and healthcare, where AI decisions can have far-reaching effects, fundamental concerns like bias, model drift, and regulatory compliance are particularly important. With its systematic approach to transparent and accountable management of AI models, IBM Watsonx.governance aims to address these issues.

The organization can automate tasks like identifying bias and drift, doing what-if scenario studies, automatically capturing metadata at every step, and using real-time HAP/PII filters by using watsonx.governance to monitor and document its AI model landscape. This supports organizations’ long-term ethical performance.

By incorporating these specifications into legally binding policies, Watsonx.governance also assists companies in staying ahead of regulatory developments, including the upcoming EU AI Act. By doing this, risks are reduced and enterprise trust among stakeholders, including consumers and regulators, is strengthened. Organizations can facilitate the responsible use of AI and explainability across various AI platforms and contexts by offering tools that improve accountability and transparency. These tools may include creating and automating workflows to operationalize best practices AI governance.

Watsonx.governance also assists enterprises in directly addressing ethical issues, guaranteeing that their AI models are trustworthy and compliant at every phase of the AI lifecycle.

IBM’s dedication to preparing businesses for the future through seamless AI integration

IBM’s AI strategy is based on the real-world requirements of business operations. IBM offers a “one-stop AI platform” that helps companies grow their AI activities across hybrid cloud environments, as noted by Forrester in their research. IBM offers the tools necessary to successfully integrate AI into key business processes. Watsonx.ai empowers developers and model builders to support the creation of AI applications, while Watsonx.data streamlines data management. Watsonx.governance manages, monitors, and governs AI applications and models.

As generative AI develops, businesses require partners that are fully versed in both the technology and the difficulties it poses. IBM has demonstrated its commitment to open-source principles through its design, as evidenced by the release of a family of essential Granite Code, Time Series, Language, and GeoSpatial models under a permissive Apache 2.0 license on Hugging Face. This move allowed for widespread and unrestricted commercial use.

Watsonx is helping IBM create a future where AI improves routine business operations and results, not just helping people accept AI.

Read more on govindhteh.com

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tsreviews · 1 year ago

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Unleash A.I. Cash Sites: Comet Review

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tomreview · 1 year ago

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eloarosario9 · 2 years ago

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Heyyy all,,good afternoon,i came today to post a cover of shallow for you guys,i hope you like it because its a really perfect song so lets listen leave a comment thank you for all.

Direitos autoais a Canção de Bradley Cooper e Lady Gaga.

#mature lady #lady gaga #kyrie #trish #dadgil #denim #dante

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pythonfan-blog · 4 years ago

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#morioh #python

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rossradev · 5 years ago

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RT @IBMEurope: AutoAI cuts down on the heavy lifting when implementing data science and AI projects. Learn about AutoAI in Feb 12 webinar - https://t.co/APAfFQlrbp #AI #DataScience https://t.co/LOb9iB4duy

AutoAI cuts down on the heavy lifting when implementing data science and AI projects. Learn about AutoAI in Feb 12 webinar - https://t.co/APAfFQlrbp #AI #DataScience pic.twitter.com/LOb9iB4duy

— IBM Europe (@IBMEurope) February 5, 2020

from Twitter https://twitter.com/Ross_Radev

#SEO #PPC #PPC Marketing

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onlineskillup · 2 years ago

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Data Science Tools Course | Learn Tools for Data Science — SkillUp Online

Data science has become a vital skill in today’s technology-driven world. With the exponential growth of data, organizations are seeking professionals who can extract meaningful insights and make data-driven decisions. To excel in the field of data science, it is essential to have a solid understanding of the tools and technologies available. IBM, a renowned leader in the tech industry, offers a comprehensive suite of data science tools that can equip you with the necessary skills to succeed. In this article, we will explore the top IBM data science tools and how they can empower you on your data science journey.

Table of Contents

Introduction to Data Science Tools

IBM Watson Studio

IBM Watson Knowledge Catalog

IBM SPSS Modeler

IBM Cognos Analytics

IBM Watson Assistant

IBM Watson Discovery

IBM Cloud Pak for Data

IBM Db2 Warehouse

IBM Watson OpenScale

IBM Watson Machine Learning

IBM AutoAI

IBM Decision Optimization

IBM Data Refinery

Conclusion

1. Introduction to Data Science Tools

Data science tools are software applications or platforms that aid in the collection, analysis, and visualization of data. These tools enable data scientists and analysts to extract valuable insights from complex datasets and communicate their findings effectively. IBM offers a wide range of data science tools that cater to different stages of the data science lifecycle.

2. IBM Watson Studio

IBM Watson Studio is an advanced data science platform that provides a collaborative environment for data exploration, model development, and deployment. It supports popular programming languages like Python and R, making it accessible to data scientists with different skill sets. Watson Studio offers a variety of tools and services, including Jupyter Notebooks, visual modeling, and AutoAI, to streamline the data science workflow and enhance productivity.

3. IBM Watson Knowledge Catalog

IBM Watson Knowledge Catalog serves as a centralized repository for managing and organizing data assets within an organization. It enables data scientists to discover, catalog, and share datasets, ensuring data quality and governance. With Watson Knowledge Catalog, you can easily search for relevant data assets, understand their lineage, and collaborate with other team members efficiently.

4. IBM SPSS Modeler

IBM SPSS Modeler is a powerful data mining and predictive analytics tool. It provides a visual interface that allows users to build predictive models without the need for extensive programming. SPSS Modeler offers a wide range of data preparation and transformation capabilities, along with advanced modeling techniques. It is widely used in various industries for tasks such as customer segmentation, fraud detection, and demand forecasting.

5. IBM Cognos Analytics

IBM Cognos Analytics is a comprehensive business intelligence and reporting tool. It enables users to create interactive dashboards, reports, and visualizations from multiple data sources. Cognos Analytics empowers decision-makers with real-time insights, allowing them to monitor key performance indicators (KPIs) and make informed decisions. The intuitive drag-and-drop interface makes it user-friendly, even for individuals with limited technical expertise.

6. IBM Watson Assistant

IBM Watson Assistant is an AI-powered chatbot platform designed to enhance customer experiences. It enables businesses to create virtual assistants that can understand and respond to natural language queries. Watson Assistant leverages machine learning and natural language processing to handle customer inquiries, provide support, and automate repetitive tasks. By implementing Watson Assistant, organizations can improve customer satisfaction and optimize their customer support processes.

7. IBM Watson Discovery

IBM Watson Discovery is a powerful AI-driven search and content analysis platform. It enables users to extract insights from unstructured data sources such as documents, websites, and APIs. Watson Discovery leverages natural language processing and machine learning algorithms to understand and analyze text, making it easier to uncover hidden patterns and trends within large volumes of data.

8. IBM Cloud Pak for Data

IBM Cloud Pak for Data is an integrated data and AI platform that provides a unified and governed environment for data science and analytics. It combines various IBM data science tools and services, including Watson Studio and Watson Knowledge Catalog, to enable end-to-end data lifecycle management. With Cloud Pak for Data, organizations can accelerate their AI initiatives, streamline data operations, and foster collaboration among data science teams.

9. IBM Db2 Warehouse

IBM Db2 Warehouse is a fast and scalable data warehouse solution. It is designed to handle large volumes of structured and unstructured data, enabling organizations to analyze and derive insights from diverse data sources. Db2 Warehouse supports advanced analytics, machine learning, and in-database processing, empowering data scientists to perform complex analyses efficiently.

10. IBM Watson OpenScale

IBM Watson OpenScale is an AI model management and monitoring platform. It helps organizations ensure the fairness, accuracy, and transparency of their AI models throughout their lifecycle. OpenScale provides tools to detect and mitigate bias in AI models, monitor model performance, and explain AI-driven decisions. By using Watson OpenScale, businesses can build trust in their AI systems and comply with regulatory requirements.

11. IBM Watson Machine Learning

IBM Watson Machine Learning is a comprehensive platform that simplifies the deployment and management of machine learning models. It supports the end-to-end machine learning lifecycle, from model development to deployment and monitoring. Watson Machine Learning offers automation capabilities to streamline model deployment in various environments, making it easier for data scientists to put their models into production.

12. IBM AutoAI

IBM AutoAI is an automated machine learning tool that accelerates the model building process. It automates time-consuming tasks such as feature engineering, model selection, and hyperparameter tuning. AutoAI allows data scientists to quickly experiment with different algorithms and configurations, leading to faster model development and improved productivity.

13. IBM Decision Optimization

IBM Decision Optimization is a powerful tool for solving complex optimization problems. It helps businesses optimize resource allocation, supply chain management, and scheduling processes. Decision Optimization leverages mathematical modeling and optimization algorithms to find the best possible solutions based on predefined constraints and objectives. By using Decision Optimization, organizations can make data-driven decisions and improve operational efficiency.

14. IBM Data Refinery

IBM Data Refinery is a self-service data preparation tool that enables users to clean, shape, and transform data for analysis. It provides a visual interface that allows users to interactively explore and manipulate data without writing code. Data Refinery offers a wide range of data transformation operations, making it easy to handle diverse data formats and structures.

15. Conclusion

IBM offers a comprehensive suite of data science tools that cater to every stage of the data science lifecycle. From data exploration and model development to deployment and monitoring, these tools empower data scientists and analysts to extract valuable insights and make data-driven decisions. By leveraging IBM data science tools like Watson Studio, SPSS Modeler, and Watson Assistant, you can enhance your data science skills and excel in your career.

Source:- https://skillup.online/courses/data-science-tools/

#IBM data science tools

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redandblk2021 · 2 years ago

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Harnessing the Power of AI with No-Code Platforms: A Fun and Quick Guide

Hey there! Have you ever wanted to create cool AI projects but felt intimidated by all the programming involved? You're in luck! No-code platforms have made it possible for anyone to build and deploy applications without being a programming wizard. In this blog post, we'll take a fun and engaging journey through some popular no-code AI platforms, how they work, their benefits, and tips for picking the right one for your needs. We'll also dive into some technical aspects and compare no-code programming with traditional coding. Finally, we'll discuss the ROI advantages and explain the SDLC (Software Development Life Cycle) in a way that's easy to understand.

Some No-Code Platforms for AI

There's a bunch of no-code platforms out there that make it super easy for non-programmers to create AI-powered solutions. Here are a few popular ones you should check out:

Google's AutoML: Wanna create custom machine learning models without breaking a sweat? AutoML lets you do just that by using Google's AI infrastructure. It has a neural architecture search that automatically finds the best model for your specific dataset. You can create models for all sorts of stuff, like image recognition, language processing, and translation.

Microsoft's Power Apps: Power Apps is an awesome platform that helps you build custom business applications packed with AI capabilities. It even has AI Builder, which comes with pre-built AI models for object detection, text recognition, and more. Power Apps also uses a Common Data Service to store and manage data, making connecting and integrating different data sources easier.

IBM's Watson Studio: With Watson Studio, you can create, train, and deploy machine learning models using a simple graphical interface. It has all the tools you need for data preparation, model building, and deployment – no coding skills required. You can even use IBM's AutoAI to generate candidate model pipelines tailored to your data automatically.

How No-Code AI Platforms Work

No-code AI platforms usually have a user-friendly visual interface that lets you design, train, and deploy AI models without writing a single line of code. Instead, they often come with pre-built templates, models, and algorithms that users can customize for specific projects. Plus, they take care of all the underlying infrastructure, making it a breeze to scale and manage your AI projects.

All you need to do is upload your data, pick the AI model or algorithm you want, and configure the model using the platform's visual interface. Once your model is trained and tested, you can deploy it and integrate it into your existing apps or workflows. Many no-code platforms use advanced techniques like transfer learning, which leverages pre-trained models to save time and resources during the training process.

Why No-Code Platforms for AI Rock

Here are some reasons why no-code platforms for AI are so excellent:

Accessibility: They make AI available to everyone, not just programmers, so more people can create unique AI projects.

Speed: No-code platforms can save you loads of time since you don't have to write complex code or build AI models from scratch.

Cost savings: By making AI development more straightforward and cutting down on the need for programming expertise, no-code platforms can save businesses some serious cash.

Scalability: Many no-code platforms provide the infrastructure and tools needed to scale AI projects without any headaches.

No-Code vs. Traditional Coding

No-code programming and traditional coding both have their pros and cons, and the choice between them depends on your specific needs and goals. Here's a quick comparison:

Ease of use: No-code platforms are incredibly user-friendly, making them perfect for non-programmers. Traditional coding, on the other hand, requires a solid understanding of programming languages and concepts, which can be a barrier for many.

Flexibility: Traditional coding offers more flexibility and customization options, as you have complete control over the code. No-code platforms, while customizable to a certain extent, may have limitations in terms of functionality and complexity.

Development speed: No-code platforms speed up development significantly by providing pre-built components and eliminating the need for complex code writing. Traditional coding can be more time-consuming, especially for complex projects.

Maintenance: No-code platforms often come with built-in maintenance and support, making it easier to manage your AI projects. With traditional coding, you need to dedicate resources to maintenance and troubleshooting.

Collaboration: No-code platforms typically offer a more collaborative environment, allowing team members with diverse skill sets to work together on a project. Traditional coding usually requires specialized knowledge, which can limit collaboration.

ROI Advantages and Understanding SDLC for Non-Programmers

No-code AI platforms offer several ROI (Return on Investment) advantages compared to traditional coding. By reducing development time and the need for specialized programming expertise, no-code platforms can lead to cost savings for businesses. They also make AI more accessible, allowing companies to harness the power of AI and drive innovation, which can result in increased revenue and a competitive edge.

To help you understand the process behind software development, let's break down the SDLC (Software Development Life Cycle) in a non-programmer-friendly way:

Planning: This is where you identify your goals, requirements, and resources for the project.

Analysis: You examine your needs and constraints and figure out the best way to approach the project.

Design: In this stage, you create a blueprint of your software, outlining how it will work and what it will look like.

Implementation: This is where the actual coding (or no-coding) happens, and your software starts to take shape.

Testing: You test your software to find any bugs or issues that need fixing.

Deployment: Your software is now ready for launch and use by your target audience.

Maintenance: After deployment, you must keep your software up-to-date and fix any issues.

No-code platforms can simplify and speed up the SDLC by eliminating or reducing the complexity of certain stages, like implementation and maintenance.

Conclusion

No-code AI platforms are changing the game for businesses and individuals who want to harness the power of AI. By making AI development more accessible, efficient, and fun, these platforms can spark a wave of innovation and growth. However, it's essential to carefully evaluate your specific needs and the available platforms before choosing the no-code AI platform that best suits your project. So, go forth and explore the world of no-code AI, and unleash your creativity!

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bunnerina · 3 years ago

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salva-escuelafeliz · 5 months ago

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govindhtech · 1 year ago

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IBM Watsonx Assistant improves responsible AI for your firm

This explains why sports, where even a small competitive edge can mean the difference between first and second place, have seen such a high number of early adopters of AI.

Consider the US Open from last year, where IBM Watsonx predicted each player’s degree of advantage or disadvantage in the singles draw. Sevilla FC introduced a watsonx-based technology abroad to give scouts thorough data-driven identification and assessment of possible signings. Additionally, EDGE3 will integrate with IBM Watsonx to enhance the college programme and player decision-making process, assisting colleges and athletes in navigating the ever-complex world of recruiting.

The quick adoption of these examples in enterprises goes hand in hand. For example, in order to help important industries, they are collaborating with Adobe to integrate Watsonx into their platform. IBM team is collaborating with SAP to augment SAP products with more artificial intelligence, machine learning, and other intelligent technologies that can increase business outcomes for our mutual clients.AI efforts are being launched by companies of all sizes and sectors.

Giving Responsible AI Priority:

Governance: Watsonx.governance provides resources for risk management, transparency, and staying ahead of AI-related policies in the future. This contains features that guarantee exploitability and track machine learning models.

Data management: Watsonx.data supports ethical and safe data management. It guarantees that data is used in accordance with regulatory rules and offers a dedicated data repository that grows with your needs.

Openness: Because the platform is based on open-source technologies, AI models can be customized and expanded upon. This transparency encourages teamwork and helps prevent the prejudices that come with closed systems.

Features and Specifications:

Multi-model Variety: For particular jobs, you can integrate your own bespoke models or select from pre-built IBM models or open-source models.

AutoAI: This tool streamlines workflows for professionals and makes AI development more approachable for novices by automating tasks like model construction, data preparation, and hyperparameter tweaking.

AI Assistants: Tasks can be automated and AI implemented in a variety of business areas, such as customer service or HR, using pre-built applications powered by Watsonx. There is no need to create code in order to deploy these aids.

Flexibility: Watsonx can be deployed on a range of infrastructures, including on-premises servers, private clouds, and public clouds, to meet a variety of business requirements.

Watsonx hopes to enable companies to properly develop and implement AI by providing these characteristics, emphasising compliance, fairness, and transparency.

It’s crucial to remember that responsible AI implementation calls for more than simply a platform. To guarantee the ethical development and application of AI, businesses must have defined policies and procedures in place.

They ought to be posing the following two queries:

Is AI a good fit for my company?

Indeed. Every organization, whether large or little, is likely in a competitive industry where artificial intelligence can make a major difference. Before using AI, decide on the use case and goals.

In order to enhance their current technology, partners from a variety of industries are already incorporating IBM Watsonx into their processes, products, and solutions in novel ways. For instance, clients can leverage IBM AI with proprietary data stored in Box to speed up business operations by combining Watsonx with Box’s Content Cloud.

Quantum Street AI

Quantum Street AI is a financial investment and wealth management company that provides a comprehensive AI platform with Watsonx integration for professional investors. The platform covers 50,000 publicly traded worldwide firms and asset classes and represents USD 6 billion in client assets.

IBM’s strategy, which employs smaller models tailored to particular business use cases, also assists in lowering the barrier to entry for AI by assisting businesses in adopting particular models that operate on less expensive infrastructure and provide greater flexibility in terms of deployment on-premises, in private cloud, or on the public cloud.

How can I guarantee ethical AI innovation for my business?

Corporate executives of today should be considering how to use AI ethically rather than whether or not to investigate it for their company. There is no margin for mistake when implementing AI in the workplace; the proper safeguards must be put in place.

Governance is an essential component of responsible AI, and it need to be the primary focus of every developer, software supplier, or company planning to use AI. For this reason, Watsonx. Governance’s release was such a big deal for IBM. They give businesses a toolkit to help them embrace transparency, manage risk, and prepare for future regulations centered on artificial intelligence.

Watsonx.data

IBM Watsonx.data

Watsonx.data is a purpose-built data store that partners may use to use their data. It allows businesses to expand AI workloads for all of their data, regardless of where it is stored. IBM’s long-standing stance that such data belongs to the partner is a significant differentiator for IBM. As an alternative, the Granite model series (with publicly available data sources) is available on watsonx.ai, which is used by numerous partners.

IBM partners take the usage of data seriously, just like they do. Consider their partnership with Dun & Bradstreet, which combines Watsonx with the Dun & Bradstreet Data Cloud to assist businesses in expanding their usage of generative AI in an ethical manner.

IBM Watsonx Assistant is being used by Boxes, another IBM Partner, to assist retailers in obtaining buyer-ready data and customer insights so they may introduce new products and provide samples to customers. Additionally, Watsonx Foundation models are being used by partners like to address some of the other difficult aspects of AI, such hallucinations.

It’s wonderful to say that IBM uses its many years of experience leading the industry, making investments, and doing AI research to support partners at every stage of their AI journey. Regardless of your company’s age, size, form, or sector, IBM offers the appropriate partner programmed and technologies to support the adoption and growth of responsible AI in its enterprise.