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Peak performance in AWS architecture is a must.  Yet, many are still underperforming.  Here’s why: 1. You're paying for unused storage 2. Your AWS bill spikes unexpectedly. 3. Your databases are oversized and costly. 4. Tracking expenses across accounts is difficult. 5. Your EC2 instances are running at low utilization. 6. Your security groups are overly permissive. 7. Your reserved instances are underutilized. 8. Database queries are timing out. 9. Your app responds too slowly. 10. Your network latency is high. 11. Your audit logging is inadequate. 12. Your IAM policies are inadequate. 13. Your data in transit is unencrypted. 14. Your app can't handle traffic spikes. 15. Your data residency controls are insufficient. 16. Your disaster recovery plan is incomplete. 17. Your single-region deployment is risky. 18. Your backup strategy is unreliable. 19. Your auto-scaling is inefficient. 20. Your manual scaling is slow. 21. Your alerts are delayed. 22. Your environments are inconsistent. 23. Your deployment process is manual. 24. Your resource usage visibility is poor. 25. You lack infrastructure-as-code practices. 26. You're running outdated instance types. 27. Your data storage strategy is inefficient. 28. You're using deprecated AWS services. 29. You neglect updates and patches. 30. Your monitoring is not proactive. 31. Your data transfer is unoptimized. 32. Your API management is inconsistent. 33. Legacy cloud integration is challenging. 34. Managing your hybrid cloud is complex. 35. Your data lifecycle management is lacking. Improving your AWS operations involves regular inspections of your cost and usage, unused AWS services, alerting mechanisms, "top talker" account activity, and execution failures. If you face any of these issues in your architecture, now is the optimal time to take immediate action. Found this useful? Follow Simform for more AWS insights! #aws #awscloud #awsarchitecture #cloud #cloudarchitecture | Amazon Web Services (AWS)

The foundation of any AI system is the data it is trained on. But what happens when that data is trained by inherent biases? The consequences can be far-reaching and devastating. Here are some key considerations: 1. Sampling bias: If the data used to train AI is not representative of the population, the AI will make decisions based on that limited and potentially biased view. 2. Correlation vs. Causation AI might notice, for instance, that there are fewer Black women in computer programming. However, it could amplify the problem by refusing to hire qualified Black women for programming jobs because it deduces that Black women make worse programmers. This is a classic example of how AI can perpetuate existing biases. To mitigate these biases, we need to adopt a proactive approach that involves several steps: 1. Data Quality and Representation Ensure that the data used to train AI models is diverse and representative of the population. Use techniques like data augmentation to expand the training data and reduce bias. 2. Debiasing Techniques Implement debiasing methods that change the word vectors used in AI models. These methods can help reduce language biases and stereotypes. Develop tools that analyze the training data for bias and provide insights on adjusting the data to reduce biases. 3. Ethical Considerations Incorporate ethical principles into the development and deployment of AI systems. Ensure that AI systems are transparent and explainable, allowing users to understand how decisions are made. 4. Continuous Monitoring Regularly monitor AI systems for biases and update them as needed. Implement feedback mechanisms that allow users to report biased outcomes and enable the AI to learn from these experiences. The consequences of biased AI extend far beyond just ethical concerns. From economic displacement to the potential for malicious misuse, the stakes have never been higher. Despite these challenges, AI holds tremendous promise. In fact, popular leaders like Bill Gates believe that Generative AI is the key to solving some of the major problems and making the world a better place. It's time to take a proactive stance and ensure your AI is a force for good. === PS. Visit my profile, Hiren Dhaduk, & Subscribe to my newsletter that can help you build a future-proof organization with Gen AI & cloud. The latest edition was released yesterday. It covers: - How Addidas functions and enables the digital revolution - Continuously evolve core systems, data, and automation - Revolutionize product design with generative AI #ai #genai #generativeai #artificialintelligence #AWS

It takes weeks & months to decide when to transition to a microservices architecture… (Better to go slow instead of taking WRONG turn) We disagree! Why? Because this happens only when you’re doing it unguided. To ensure you can make faster decisions, we are sharing key indicators of when we make an architectural move 👇 ➢ Complex Monolith: If we notice a complex monolith where developers struggle to understand the full end-to-end flow, we will break it into microservices. ➢ Organizational Maturity: Having the organizational maturity and staff to support the added complexity of microservices is crucial. This typically requires more than just a few developers. When our teams have a clear understanding of the business capabilities that should be separated into microservices, we proceed with the transition. ➢ Multiple Independent Teams: Microservices come into play when we have multiple teams that need to work independently on different parts of the application. This allows teams to deploy changes without coordinating with each other. ➢ Specific Technical Scaling Needs: We adopt microservices for specific technical scaling needs, such as scaling certain components independently due to high traffic. However, microservices are not necessary just for horizontal scaling - we can also scale a monolith effectively. David Heinemeier Hansson, CTO of 37signals (Makers of Basecamp + HEY), said in one interview that the microservice pattern is possibly the most damaging pattern in web services in a decade! And we agree with him to a certain extent. That’s because many adopt microservices without fully understanding their own needs, often underestimating the advantages of monoliths. You should take that leap only when you see that Microservices clearly outweigh the added complexity. #appmodernization #microservices #cloudmigration #simform

Slow load times and frequent app crashes... They are silent killers of your business. But you can solve this! 👇 I've witnessed firsthand how application modernization strategies can transform struggling systems into high-performing, scalable powerhouses. Here are 6 key strategies for app modernization (they improve app’s performance & scalability) 1. Cloud-Native Architectures: - Assess the Current Architecture. - Adopt Containerization. - Leverage Managed Cloud Services. This will allow applications to scale seamlessly to meet growing demands while maintaining high levels of performance and reliability. 2. Microservices: - Identify Microservice Boundaries. - Implement Asynchronous Communication. - Implement Service Discovery. This will enhance scalability by enabling each service to scale independently, reducing the impact of increased traffic on the entire application. 3. DevOps Practices: - Implement CI/CD pipeline. - Adopt Infrastructure as Code. - Implement Monitoring and Observability. This will help with rapid deployment and testing, ensuring that applications are always up-to-date and performant. 4. Refactoring and Migration: - Assess Technical Debt. - Modernize Legacy Components. - Implement a Phased Migration. This will improve performance by optimizing code efficiency and reducing latency. 5. Integration and Interoperability: - Identify Integration Points. - Leverage API-Driven Design. - Implement API Management. This will enhance the overall performance and scalability of applications by enabling them to interact with other systems efficiently. 6. User Experience Enhancement: - Conduct User Research. - Optimize User Workflows. - Implement Responsive Design. This will reduce the load on applications, thereby enhancing performance and scalability. But, just like Pearl Zhu points out, never take a blinded action while modernizing your application. Start by doing some careful, painstaking business analysis. Taking the right steps can ensure a smooth transition that maximizes the benefits - improved performance, cost efficiency, scalability, and enhanced security. ====== PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies. #microservices #awscloud #cloudmigration #softwareengineering #simform

The real reason most AI projects struggle: It's not data. It's not algorithms. It's not computing power. It's this 👇 Organizations are trying to build models from scratch. Please don’t! Instead, use these 5 approaches to avoid this trap: #1. Leverage Pre-Trained Models and Transfer Learning ↳ Faster Training: Start with a solid foundation by utilizing pre-trained models. ↳ Higher Accuracy: Leverage rich knowledge from these models and fine-tune them to suit your specific needs. #2. Faster Deployment and Rapid Prototyping ↳ Swift Implementation: Test your ideas quickly using pre-existing AI models and tools. ↳ Quicker to Market: Shorten development cycles, enabling business innovation and competition. #3. Lower Costs with Cloud-Based AI Services ↳ Less Computing Power: Use cloud-based AI services that provide ready-to-use AI functionalities. ↳ Lower Entry Barriers: Eliminate the need for large teams, making AI accessible to more businesses. #4. Better Performance through AutoML and MLOps ↳ Automated Processes: Implement AutoML platforms to automate many parts of the machine learning process. ↳ Quality and Speed: Embrace MLOps to maintain the quality and improve the speed of AI model development and deployment. #5. AI for Everyone through Skill Building ↳ Wider Adoption: Lowering of entry barriers and costs leads to more businesses adopting AI. ↳ Skill Building: Allows newcomers to start with AI, fostering skill building and driving industry innovation. If you ask us, then these steps are the extended version of what Matt Wood, VP of AI at AWS, said at AWS Summit Los Angeles 2024: "Today, it is easier to be able to build your own application from scratch, which is exactly what you need, than it is to be able to find one in a catalog and personalize it." Companies are taking advantage in the right way. You should do it too! Found this helpful? Follow Simform for more Gen AI insights. #genai #generativeai #ai #artificialintelligence #simform

You'll add AWS Lambda today, & tomorrow, you might connect it with Amazon RDS. This way, your cloud environment keeps evolving. But it can create a problem! New features, scaling demands, and technology updates can impact and degrade your architecture's core characteristics over time. This is why Well-Architected Reviews are essential checkpoints whenever your cloud footprint undergoes significant changes. But the real question is: When should you review the AWS Well-Architected Framework? 👉🏻 Review periodically  Review the AWS Well-Architected Framework annually to ensure your architecture remains strong as workloads and requirements evolve. Regular reviews help identify improvement areas and maintain a robust cloud architecture. 👉🏻 While making major changes Conduct a WAFR review when making significant changes to your architecture, such as adopting new AWS services, implementing new security controls, or scaling workloads. This ensures your architecture remains well-designed and resilient. 👉🏻 While adding new workloads Review the WAFR when launching new workloads or applications on AWS. This helps validate that your architecture aligns with AWS best practices, identifying potential issues early for a well-architected design from the start. 👉🏻 Moving the AWS account Perform a WAFR review when moving your AWS account to a new owner, reseller, or managed service provider. This ensures the transition does not impact your architecture, maintaining control and visibility over your cloud environment. If you admire the suggestions by Werner Vogels, VP & CTO of Amazon, you might agree with him when he says, “Regularly reviewing the AWS Well-Architected Framework is essential to ensure your architecture remains robust and responsive as your cloud footprint evolves.” Bottomline: The AWS Well-Architected Framework isn't a luxury—it's an indispensable tool for calibrating and future-proofing your cloud architecture. In your opinion, what should be the ideal frequency for reviewing the AWS Well-Architected Framework - monthly or yearly?  --- PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies. #aws #database #scalability #softwareengineering #simform

Our week was super fun at AWS Summit New York! Met many new tech enthusiasts. Loved answering their questions about Gen AI in robotics and serverless applications. And showed how to innovate faster & more securely. Thanks for being there! But, if you didn't get a chance to meet us, Then, DM us now! We’ll be happy to share quick insights on how to leverage Gen AI for your specific use cases! #AWSSummit #AWS #GenerativeAI #Robotics #Serverless | AWS Events AWS Partners

Should you use an existing library or  Build your own design system?  That’s a tough call 🤔 Over the past 13 years, we've worked with many startups and enterprises. Each approach has its own benefits and caters to different needs. ⫸ For Startups We focused on delivering value quickly by using existing libraries. Because building from scratch would have taken too much time. Here's why this approach works: – Existing libraries like Material-UI offer robust, pre-built components. – Development time is significantly reduced with ready-to-use components. – Active communities ensure access to the latest features and updates. – This strategy allows teams to ship faster and focus on core functionalities. ⫸ For Enterprises We built a custom design system for brand consistency. Here's why this approach is beneficial: – Custom components enable a seamless, branded user experience. – Documenting guidelines & creating component libraries improves sustainability. – A custom system ensures uniformity and quality across products and teams. – It allows for greater flexibility and control over the design language. But here’s the thing 👇 Large companies can justify this investment. However, startups with limited resources might prefer existing libraries. A common strategy is starting with a library like Material-UI or Ant Design. Then, over time, develop a custom system. This method initially allows for fast progress and evolves into a unique user experience. When deciding where to focus during times of rapid change, it's important to concentrate on enduring principles. Now, TL;DR? → Startups benefit from existing libraries for speed and value.  → Enterprises need custom systems for brand consistency and growth. → The best choice depends on team size, project scope, and business needs. Found this useful? Follow Simform for more digital engineering insights. #digitalengineering #DesignSystems #UXDesign #simform

Scaling AI is a silent killer of 70% of enterprise digital transformations. But you could be in the successful 30% Here’s what I would do! 👇 (10 steps to scale AI without draining millions in resources) Spoiler: It starts way before you write a single line of code. 1. Start with data science Develop tailored algorithms using appropriate APIs and expert data scientists. 2. Locate and ingest data Identify and use relevant internal and external data sets, ensuring quality. 3. Involve stakeholders Engage various departments to align AI development with business needs. 4. Manage data lifecycle Develop secure, standardized data structures for up-to-date AI training. 5. Optimize MLOps Choose a platform compatible with team skills and IT infrastructure. 6. Assemble cross-functional AI team Form a multidisciplinary team for comprehensive understanding. 7. Select high-potential projects Choose projects likely to succeed for early wins and momentum. 8. Incorporate governance and compliance Integrate AI governance and reportability from the start. 9. Employ right tools Use cloud-based platforms for collaboration and efficient AI development. 10. Monitor AI models Track end-to-end performance using metrics like speed, cost, and user value. Recently, Matt Wood, VP of AI Products at Amazon Web Services (AWS), said in one of his speeches that "Generative AI represents probably the biggest technical shift in how we're gonna interact with data and information and each other since the advent of the very, very earliest internet." So, it is evident that everyone will be adopting AI. The only thing you should remember is that successful AI scaling is a journey that begins long before implementation. Start laying the groundwork today for a successful AI-driven future. And by leveraging this transformation, your organization can achieve unprecedented growth and innovation. — PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies. #aws #database #scalability #softwareengineering #simform

Cloud migration is simple. Yet many fail. WHY? Because many miss important strategic, technical, and operational aspects. If you're thinking about moving to the cloud, Choose AWS. Here's why: ↳ 43% faster time-to-market for new features ↳ Up to 66% infrastructure cost savings ↳ 45% decrease in security incidents ↳ 29% increase in staff productivity The cost savings alone can be transformative. As Aaron Rallo, Director & General Manager at AWS, points out: "Slash data center costs by 36% — move to the cloud and stop paying for resources you're not using." This efficiency is just the beginning. To truly leverage AWS, understand these 7R execution migration strategies: ✅ Rehost ✅ Relocate ✅ Replatform ✅ Refactor ✅ Repurchase ✅ Retire ✅ Retain Once this is done, do these 5 things: #1 Set up a migration strategy #2 Ensure security and compliance #3 Develop a disaster recovery strategy #4 Get Operational ready #5 Get in touch with a Trusted migration partner (like Simform, an AWS Premier Partner) Implement these for confident cloud migration and goal achievement. #cloudmigration #awsmigration #awscloud #aws #simform | Amazon Web Services (AWS)