Google Vertex AI
Step-by-Step Guide for Google Vertex AI Additional Resources for Google Vertex AI: Custom Model Training:https://cloud.google.com/vertex-ai/docs/training
Azure AI Studio
Azure AI Studio – Step-by-Step Guide Open your browser and navigate to the Azure AI Studio URL https://portal.azure.com/#create/Microsoft.AI.Log in using your Azure account credentials (you can sign up for a free account if you don’t have one). Create a New AI Service: Select the AI Service Type: Define Project Details: Connect Data Sources: Test and
Azure Machine Learning Studio
Step-by-Step Guide Open your browser and navigate to the Azure Machine Learning Studio URL https://ml.azure.com/.Log in using your Azure account credentials (or create a free account if needed). Create a New Machine Learning Workspace: Upload a Dataset: Train a Model: Evaluate and Deploy the Model: Azure Cognitive Services is a comprehensive suite of artificial intelligence
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Azure Cognitive Services
Key Features: Key Use Cases: Benefits: Conclusion: With Azure Cognitive Services, businesses of all sizes can unlock the potential of artificial intelligence, enabling their applications to see, hear, speak, and understand the world around them. Whether enhancing customer interactions, automating manual processes, or providing new insights from data, these AI-powered APIs offer a seamless way
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Google Cloud AI Services
Google Cloud AI Services provides a comprehensive suite of APIs and tools designed to bring powerful AI capabilities to applications without requiring deep knowledge of machine learning. These services, much like Azure Cognitive Services, enable businesses to implement vision, speech, natural language processing, and translation capabilities into their applications with ease. Pre-Built AI APIs (Similar
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Microsoft Copilot Studio
Step-by-Step Guide for Microsoft Copilot Studio Open your browser and navigate to the Microsoft Copilot Studio URL https://copilotstudio.microsoft.com/. Log in using your Microsoft account credentials (company or organization). Create a New Project: On the home screen, click on the «Create» button located on the left sidebar. You will see various templates such as «Safe Travels,» «Store Operations,» «Sustainability Insights,» «Voice,» «Weather,» and «Team Navigator.» Choose a template
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Amazon SageMaker
Amazon SageMaker is a fully managed service introduced by Amazon Web Services (AWS) in November 2017. Its purpose is to make it easier for developers and data scientists to build, train, and deploy machine learning (ML) models at scale. Before SageMaker, creating and deploying ML models required significant expertise and manual effort in setting up infrastructure,