💡AI doesn’t see raw sentences—it sees tokens as numbers. Why Tokens? Unlike the traditional way of storing and retrieving data, the AI Model shall build relationships between the text and generate text with the right context, so it requires a special data structure (Vectors) to store it in
Why are Vectors a big deal for AI? Understand what we have been doing so far vs what we can get via AI Vectors as a perfect data type and format. What are we doing to retrieve the data? 1. RDBMS options 2. NoSQL and other database options 3. Search
Understand high-level Microsoft Azure AI capabilities and plan for potential adoption. About Azure AI Azure AI, part of Microsoft Azure, offers a broad range of artificial intelligence capabilities designed to help developers and organizations build intelligent applications. To provide flexibility, quick adoption, and realization of AI to Organizations, Azure AI
Quick List of AI Use Cases Almost every current software or hardware is a candidate for AI adoption due to the nature of excellent AI capabilities. It's not about just Generative capabilities but also how you can leverage new vector capabilities to improve user experiences. Check this index
Build, Train, Tune, and Deploy foundation models, machine learning models, and custom AI. About IBM Watsonx.ai IBM watsonx.ai is a powerful AI development studio designed for building, training, tuning, and deploying foundation models, machine learning models, and custom AI — all in one place. It's a key
Monitor, Control, Manage & Trust your organization's AI systems. About IBM Watsonx.governance IBM watsonx.governance is a platform designed to provide automated governance, risk management, and compliance for AI models and the data they rely on. It's part of the broader IBM watsonx suite and
Your organization's data to work for AI, ML, and Analytics About IBM Watsonx.data IBM Watsonx.data enables organizations to consolidate and leverage enterprise-wide data for A.I., ML, and Analytics with scalability, performance, governance, and security. IBM Watsonx.data is a hybrid, open data lakehouse platform designed
Building an AI Bank Risk Mitigator Here, I focus on the specific context of building a Bank's AI Risk Mitigator that shows better interactive and generative information for better decisions and augments current static risk mitators. AI or Generative AI can dramatically boost current risk mitigators' capabilities,
Understand high-level Amazon SageMaker AI capabilities and plan for potential adoption. Goals Understand What Amazon SageMaker AI offers About Amazon SageMaker AI Amazon SageMaker AI, formerly named Amazon SageMaker, is a cloud-based fully managed machine learning (AI/ML) service, extends old SageMaker's capabilities with AI. Amazon SageMaker AI
Set up and run DeepSeek-R1 in Amazon Bedrock Goals Install DeepSeek-R1 in Amazon Bedrock and try DeepSeek. And, Understand Amazon Bedrock's capabilities to jumpstart AI adoption. About Amazon Bedrock Amazon Bedrock is a fully managed service that offers high-performing foundation models (FMs) from leading AI companies and provides
Set up and run DeepSeek-R1 on AWS cloud with EC2, and Try DeepSeek Queries. You can do the same in your own servers, IBM Cloud, Oracle Cloud, Google Cloud, Azure Cloud, or personal laptop. But, I will choose AWS in this post for simplicity. Goals Install DeepSeek-R1 in the AWS
Firstly, Read Part-1 and Part-2 before you try to understand the AI Journey. Secondly, Start connecting all the dots with the right AI Journey by following the steps. 1. Understand the current AI market state with the current AI technology (products, agents, and companies) landscape. 1. Know how others solved