Help customers achieve their desired business outcomes, Senior Enterprise Account Manager @ Amazon Web Services (AWS)
Seats are filling up fast for this GenAI workshop - Register now! You will learn how to: 1) Set up a Data Mesh using Amazon DataZone and Amazon Redshift. 2) Accelerate insights with GenAI, leveraging Amazon DataZone and Redshift. 3) Make your data universally accessible with Amazon Bedrock Knowledge Bases and Agents.
For anyone who's interested in services like Bedrock and Redshift, and how they relate specifically to financial services use cases, there may be a few 'seats' left on this workshop on the 18th.
Get hands-on experience using #generativeAI to address financial business challenges, derive meaningful insights from unstructured data, and more. ☁️💰 https://go.aws/440539d Register now for the virtual workshop on April 18th, 2024. #AWS #AmazonBedrock
Just finished the course “Delivering Data-Driven Decisions with AWS: Applying Machine Learning, Data Engineering, and Generative AI” by Wendy Wong! Check it out: https://lnkd.in/gRRJFhET #datadrivendecisionmaking #artificialintelligence #amazonwebservices.
We’re ready to support you. With AWS Analytics and Machine Learning, you can easily build, train, and deploy models—even if you’re new to ML. Let us work with you to transform your data into intelligence, effortlessly! #AWSAnalytics #MachineLearning #Fasttrack #AWS #DataAnalytics #DataInsights #FasttrackDataSolutions
Senior Software Development Engineer at AWS | Java, Python, TypeScript | AWS Cloud Tools & Serverless Computing | Expert-Automation, CI/CD, & Kubernetes | Passionate About AI, Software System Optimization, System Design
Ever wondered how to predict your resource needs even before they spike? Explore the magic behind AI-driven resource scaling automation! This is how we do it at amazon: Step 1: Data Collection and Monitoring To automate resource scaling, the first step is to gather data — lots of it. Imagine a vast network of sensors collecting real-time data on user requests, including their volume, frequency, and geographic origin. This data, along with performance metrics from Amazon CloudWatch, forms the foundation of our AI-driven approach. Historical data on traffic patterns, peak usage times, and seasonal trends is also crucial, much like historical weather data is vital for accurate forecasting. Step 2: Predictive Analytics with AI With data in hand, the next step is predictive analytics. Using machine learning models such as time series forecasting, regression, and clustering, we can analyze historical data and predict future demand. It’s like having a weather forecast that tells you not just if it will rain, but exactly when and where. For real-time data processing and on-the-fly prediction adjustments, tools like AWS Kinesis come into play. These tools ensure that our predictions are always current and accurate, ready to guide our scaling decisions. Step 3: Automated Decision Making With predictions in place, the system needs to make decisions. By defining scaling thresholds and policies — such as triggering scaling actions when CPU usage exceeds 70% or during regional traffic spikes — we create a framework for automated decisions. The AI models use these predictions to scale resources preemptively, much like how a smart thermostat adjusts your home’s temperature before you even feel the change. Step 4: Implementation and Automation Finally, the system implements these decisions. AWS Auto Scaling Groups are configured to adjust instances based on our policies. Load balancers like ELB distribute traffic efficiently, ensuring high availability. Serverless and container services such as AWS Lambda and Amazon EKS provide dynamic scaling based on load. By integrating AI models with AWS infrastructure using tools like AWS SageMaker and Lambda, scaling actions are automated seamlessly. Follow me for more insights like this! #artificialintelligence #aws #amazon
Helping clients in their Digital Transformation journey with the focus on ROI benefits. I am aiming to decode the hype around AI to implement value-driven solutions.
🚀 Want to supercharge your operations? Amazon Q for Business might be your secret weapon. 👇👇👇 Amazon Q for Business is a generative AI assistant tailored to your business needs. → Integrates with your data and systems to streamline tasks and accelerate decision-making. → Perfect for improving issue resolution with faster MTTI (Mean Time To Identify) and MTTR (Mean Time To Recovery). Connects to data sources like Amazon S3 and AWS documentation for in-depth problem-solving. → Customizable based on roles and permissions. → Your data remains secure—AWS doesn’t use it to train models. You need to have your data updated in order to reap the benefits The architecture diagram below showcases the use case 📽️ Demo video coming soon! 🔗 Learn more here https://lnkd.in/gcyazYte #AmazonQ #AIforBusiness #MTTR #CloudOps #AWS #GenerativeAI #AItools #OperationsEfficiency #TechInnovation
Just finished the course “Delivering Data-Driven Decisions with AWS: Applying Machine Learning, Data Engineering, and Generative AI” https://lnkd.in/edric7e5 #datadrivendecisionmaking #artificialintelligence #amazonwebservices
Specialized in Global Leadership and C-Level Hiring | Passionate about Executive Hiring | Industry Mapping | Recruitment Analysis | Strategic Hiring | Market Intelligence | Diversity Hiring | Project Management
In this new AWS article, read how ZS is pioneering use cases leveraging Amazon Q and generative BI to power #ZAIDYN, ZS’s digital and connected analytics platform tailored for life sciences. https://go.aws/4aRqfB3
In this new AWS article, read how ZS is pioneering use cases leveraging Amazon Q and generative BI to power #ZAIDYN, ZS’s digital and connected analytics platform tailored for life sciences. https://go.aws/4aRqfB3
