This video showcases a multi-agent AI system that analyzes the stock market, generates buy/sell signals, and lets you approve or reject trades.
Once approved, a smart execution strategy kicks in to buy stocks based on your request.
🔹 How It Works:
✅ AI agents analyze the market and send trade signals
✅ Users approve or reject AI-generated recommendations
✅ Upon approval, a moving strategy executes trades seamlessly
👨💻 This is the future of AI-powered trading! Watch how AI can assist traders in making smarter decisions.
How can businesses successfully adopt AI and see real ROI?In this webinar, I break down AI adoption for businesses, covering:
- How AI is transforming businesses
- Identifying profitable AI opportunities using pain points & prioritization
- Assessing AI readiness & deciding whether to buy or build AI
- Exploring automation vs. augmentation strategies
- Best practices for AI implementation: starting small & train teams
- Measuring the ROI of Generative AI (costs, benefits, risk factors)
In this tutorial, I walk you through step-by-step installation using Python virtual environments, Jupyter Notebook, and Ollama. Then, I show you how to set it up on Google Colab and AWS for cloud-based deployment. Finally, I compare different AI models on AWS to help you choose the best one for your needs.
In this tutorial, you’ll learn how to build a multi-agent AI system that automates financial analysis using Python.
Why Multi-Agent Systems?
Multi-agent systems allow you to handle complex tasks by splitting them between specialized agents. Whether you're analyzing stock trends, tracking news, or synthesizing data, this approach delivers faster and more accurate results.
In this video, we go beyond strategy to show you exactly how to implement AI-powered content creation in your business. Using a serverless architecture on AWS, we’ll walk through each step, from summarizing content and generating images to creating voiceovers and assembling video—all powered by AWS Services such as Step Functions, Lambda Function, Amazon Polly, Amazon Bedrock, Amazon Titan Foundation Models, and AWS Element Media Convert. By the end of this video, you’ll see how easily you can build a streamlined, scalable content workflow that drives engagement and saves time and resources.
In this video, I introduce the Amazon EMR Cluster and how it works. Then, I recommend setting up the EMR cluster using an instance fleet.
In this video we’ll explore 5 different reasons why we should use serverless architecture when designing and implementing a new project.
In this video, I'll walk you through how to use foundation models to build an AI Stylist using Amazon Bedrock. I used Python programming and Jupyter Notebook for this demo.
In this video, I introduce Big Data, Streaming Data, and MapReduce for beginners. This is a snippet of my Udemy course, Mastering Solutions Architecture, where I cover key soft and technical skills for the solutions architect role.
In this tutorial, I’ll walk you through how to build the core functionality of a digital wardrobe using Python programming and AWS Cloud services. The solution is designed using AWS Lambda, S3, Amazon Rekognition, DynamoDB, EventBridge, and IAM.
This is a complete beginner guide to build an AI agents using OpenAI API, and Python.
This is a complete beginner guide for anyone who just started with Python Programming and Prompt Engineering.
This course is ideal for organizations seeking to implement solutions architecture processes to foster strategic relationships with their clients and partners.
Let’s talk about the right toolkit for your business.