Simform

Every tech leader knows that data alone isn't enough—it’s how you analyze and act on it that drives impact. OHDSI (Observational Health Data Sciences and Informatics) combined with the OMOP Common Data Model (CDM) on Azure enables scalable, patient-level data analysis that’s transforming healthcare. 🏥 Here’s how things work: 👉 Azure Pipelines streamline the setup of OHDSI tools, with Bootstrap and OMOP resource groups ensuring a structured, automated environment. 👉 Atlas and Achilles provide intuitive, actionable insights, helping decision-makers understand patient data faster and more accurately. 👉 The use of BroadSea WebTools brings advanced data visualization, while Azure SQL supports the OMOP CDM for standardized, high-quality data management. And the results will be? 30% faster clinical trials, reduced costs for patient data processing, and improved patient outcome predictions through better data standardization and analysis. 🎯 Bottomline: If your goal is to harness healthcare data to deliver better patient outcomes, this is the solution that will take you there. #HealthcareData #CloudInfrastructure #Azure  #AIinHealthcare #Simform | Microsoft Azure

Leading hospitals use IoT, 5G, Azure Stack Edge, and Azure Machine Learning to transform patient care and operations. So, how do things operate behind the scenes? 👇 - Health monitoring devices (patient monitors, CT scanners) collect crucial data. - This data flows via 5G/LTE networks to Azure Stack Edge for preprocessing at the edge. - Azure IoT Hub then transfers it to the cloud for further analysis. - Azure Machine Learning analyzes the data for predictive insights, improving care and operational efficiency. - Azure Digital Twins creates a virtual hospital model for proactive decision-making - Lastly, Azure Time Series Insights tracks long-term trends for better health outcomes and performance, with secure storage in Azure Data Lake. Easy, right? Yes, but a minor error can bring things down. So, it is recommended to contact Engineering Experts like Simform to have it done for you. Results? Faster decision-making, minimized downtime and enhanced patient care. PS.: Is your organisation ready for IoT? 🗣️💬👇 #simform #azure #IoT #healthcare #AI | Microsoft Azure

Perplexity has transformed the AI answer engine space by doubling throughput and cutting costs, all powered by Azure AI Studio. Here’s the scoop: - By integrating Azure OpenAI Service into their existing architecture, coupled with Virtual Machine Scale Sets, they doubled their token processing speed to 600,000 tokens per minute.  - To cut latency by 30%, Azure’s Edge Zones were integrated with their existing API Gateway infrastructure. This localized data processing at the network edge, results in faster response times and an enhanced user experience. - For secure query handling, Azure Security Center was embedded into the system’s backend, ensuring enterprise-grade security measures like advanced threat protection and compliance across the entire data flow. - They utilized Azure Kubernetes Service (AKS) in combination with Azure AI Studio, enabling dynamic scaling of AI workloads. This allowed them to balance costs effectively while maintaining high performance during traffic spikes. - Through Azure DevOps integration, the engineering team streamlined the CI/CD pipeline, allowing rapid development and deployment and bringing the full product to market in just six months. Denis Yarats CTO and Cofounder of Perplexity stated "Azure AI Studio improved our experience, enabling faster development, greater throughput, scalability, security, and trust." P.S. How are you scaling your AI products while keeping costs down? #azureai #azure #ai #genai #perplexity | Microsoft Azure

Mastering AI doesn’t mean using it everywhere; it’s about strategic use. Optimize your AI journey with Azure AI Studio & Gen AI. Key strategies: - Identify your optimal AI usage level—avoid both underutilization and saturation. - Align Gen AI capabilities with clear business objectives; prioritize data quality via Azure Databricks. - Begin with manageable projects like Azure chatbots to grasp core AI applications. Embrace responsible AI practices. Start small, scale smart, and lead with precision. — PS. Visit my profile, Hiren, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies. #AzureAI #AzureDatabricks #GenAI #CloudMigration #Simform

Choosing the right vector database on Azure isn’t easy. (If you’re struggling, read this) You might evaluate all the top databases, test features, and read countless reviews. But without the right focus, you may still miss the mark. Sound familiar? Here's how to ensure you're picking the right Azure database for smooth, efficient, and scalable AI operations: [1] Understand Your Data Requirements Tailor your Azure database choice (e.g., Azure Cosmos DB, Azure SQL Database) to your data type—whether it's image embeddings, text vectors, or more. [2] Evaluate Performance and Scalability on Azure Prioritize Azure databases like Azure Synapse Analytics, which excels in handling high-dimensional data and scales seamlessly as your datasets grow. [3] Consider Azure Integration and Compatibility Ensure your choice integrates smoothly with Azure AI Studio, Databricks, and machine learning frameworks like TensorFlow and PyTorch. [4] Examine Indexing and Query Capabilities in Azure Look for advanced indexing methods and complex query capabilities, crucial for GenAI applications on Azure’s database ecosystem. [5] Decide Between Azure Cloud vs. On-Premises Deployment Evaluate Azure's managed services versus on-prem options for flexibility, cost efficiency, and scalability. [6] Ensure Azure-Backed Security and Compliance Choose Azure databases that offer end-to-end encryption, access control, and compliance with global regulations. [7] Assess Azure Community and Support With robust documentation, active forums, and Azure’s premium support, you’re never alone in your AI journey. [8] Review Azure Cost Implications Factor in Azure's pay-as-you-go pricing, reserved instances, and other cost models to avoid surprises. 🔖 Save this post. And start choosing the right Azure database for your Gen AI needs. Follow Simform for more insights into leveraging cutting-edge technologies on Azure for AI success. #GenerativeAI #Azure #DataManagement #MachineLearning #simform | Microsoft Azure

"Modernization is too risky." I’ve heard this a hundred times. Many argue, saying: “Our legacy systems work just fine!” “Why chase the latest tech trend?” “The costs? Unjustifiable.” But here’s the truth. That thinking is outdated. In fact, clinging to those legacy systems costs you more than you realize. It drains your budget, stalls scalability, and puts you behind competitors who are already embracing modern technologies like AI and Azure Cloud Computing. I still remember an incident. I listened to similar arguments in a boardroom a few months ago. We were on the fence about overhauling an aging system. I admit that the risks seemed daunting. But, the risks of not modernizing? Even BIGGER. Fast forward: we took the leap and integrated AI into our infrastructure using Azure AI Studio and Azure Fabric. And guess what? We didn’t just save costs—we revolutionized how we work. Here’s why AI, particularly through Azure, became a game-changer for us—and how it can do the same for you: 1️⃣ Faster Development, Lower Costs: Azure's AI capabilities automate mundane coding tasks, accelerating timelines and freeing up teams to focus on high-impact projects. 2️⃣ Seamless System Integrations Azure simplifies the mess of legacy system migrations, making integrations more streamlined and cost-effective. 3️⃣ Proactive Maintenance: Instead of waiting for failures to disrupt operations, Azure AI predicts and prevents issues before they even happen. Less downtime. More productivity. 4️⃣ Security at Scale: Azure’s AI continuously monitors and neutralizes threats, reducing both risk and the cost of breach recovery. 5️⃣ Smarter Testing, Fewer Fixes: With AI-driven testing on Azure, bugs are squashed faster, meaning smoother product launches with fewer hiccups. So here’s my question to you: What’s holding you back? Every day spent relying on legacy systems means more technical debt, higher costs, and missed opportunities. If other companies can revolutionize their operations with Azure AI, why can’t you? #genai #modernization #azure #cloud 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.

Healthcare manufacturers can’t afford downtime. AI can keep such problems away. AI tracks thousands of parameters in the healthcare industry. ⚠️ But here’s the real challenge: Healthcare manufacturers face strict regulations and sensitive product conditions. 👉 Take vaccine production as an example: Every second counts, and even a small deviation can risk lives. For mRNA vaccines, ultra-low freezers must remain stable. And AI tracks everything:   - Air quality   - Pressure   - Equipment vibrations. If there’s even a slight temperature shift, AI sends an instant alert. The team checks production records, and quality assurance steps in, keeping everything safe and sound. Here’s how it works: #1. Data Collection: IoT sensors track temp, humidity, and air quality. #2. Data Flow: Azure IoT Hub moves data securely to Azure Data Lake. #3. Processing: Azure Databricks cleans and prepares the data. #4. Model Training: Azure ML builds models for anomaly detection. #5. Detection: Real-time analysis triggers instant alerts immediately. #6. Action: Immediate alerts help teams act quickly, ensuring safety. The result? ✅ Improved efficiency ✅ Early anomaly detection ✅ Simplified regulatory compliance Bottom line? AI doesn’t just monitor—it protects, anticipates, and ensures the safety of life-saving products, leaving nothing to chance—whether it’s 3 PM or 3 AM. Is your product ready to adopt AI? If this sparked ideas, hit repost to share with your network. ♻️  P.S. Follow @Simform for more such insights. #simform #healthcare #azure #AI #IoT

"Modernizing our apps... someday." Relatable? Here’s the reality: Legacy systems are stifling innovation, hindering agility, and draining resources. But modernization doesn't have to be an uphill battle. We’ve distilled our extensive experience into 5 Quick-Win strategies that can transform your legacy systems into cutting-edge digital assets—leveraging the power of Azure and modern practices. (No fluff, just actionable insights) 👇 1️⃣ Hybrid Cloud Re-platforming with Azure ✅ Accelerate Deployment: Utilize Azure Migrate to streamline the transition. ✅ Slash Operational Costs: Leverage Azure Cost Management to optimize expenses. ✅ Preserve Core Logic: Modernize without disrupting core business processes. 2️⃣ Automated Language Transformation Using Azure AI ✅ Speed Up Transformation: Harness Azure Cognitive Services to automate and enhance codebases. ✅ Leverage AI: Use Azure Machine Learning to drive transformation with advanced algorithms. 3️⃣ Strategic Containerization with Azure Kubernetes Service (AKS) ✅ Faster Time-to-Market: Implement AKS for rapid deployment and scaling. ✅ Enhance Portability: Transition monolithic applications into containerized environments with ease. ✅ Optimize Resources: Improve utilization and efficiency through Azure’s container solutions. 4️⃣ Incremental Microservices Adoption for Scalable Solutions ✅ Boost Development Speed: Gradually decompose applications into microservices for agile development. ✅ Increase Scalability: Utilize Azure’s Service Fabric to scale your microservices architecture. ✅ Seamless Integration: Adapt microservices with Azure’s integrated services for a smooth transition. 5️⃣ Cloud-Native Refactoring with Azure ✅ Achieve Near-Perfect Uptime: Leverage Azure’s high availability and disaster recovery options. ✅ Reduce Infrastructure Costs: Implement Azure’s serverless solutions to cut costs. ✅ Implement Advanced Patterns: Use Azure’s elasticity to optimize application performance. Start implementing these quick wins today to unlock new possibilities and drive innovation without the burden of legacy constraints. P.S. Which quick-win path have you found most effective in your modernization journey? Share your experiences! #ApplicationModernization #Azure #CloudMigration #Simform

Exactly a year ago, we embarked on a transformative journey in application modernization, specifically harnessing generative AI to overhaul one of our client’s legacy systems. This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic business objectives Reflecting on this journey, here are the key lessons and outcomes we achieved through Gen AI in application modernization: [1] Assess Application Portfolio. We started by analyzing which applications were both outdated and critical, identifying those with the highest ROI for modernization.  This targeted approach helped prioritize efforts effectively. [2] Prioritize Practical Use Cases for Generative AI. For instance, automating code conversion from COBOL to Java reduced the overall manual coding time by 60%, significantly decreasing costs and increasing efficiency. [3] Pilot Gen AI Projects. We piloted a well-defined module, leading to a 30% reduction in time-to-market for new features, translating into faster responses to market demands and improved customer satisfaction. [4] Communicate Success and Scale Gradually. Post-pilot, we tracked key metrics such as code review time, deployment bugs, and overall time saved, demonstrating substantial business impacts to stakeholders and securing buy-in for wider implementation. [5] Embrace Change Management. We treated AI integration as a critical change in the operational model, aligning processes and stakeholder expectations with new technological capabilities. [6] Utilize Automation to Drive Innovation. Leveraging AI for routine coding tasks not only freed up developer time for strategic projects but also improved code quality by over 40%, reducing bugs and vulnerabilities significantly. [7] Opt for Managed Services When Appropriate. Managed services for routine maintenance allowed us to reallocate resources towards innovative projects, further driving our strategic objectives. Bonus Point: Establish a Center of Excellence (CoE). We have established CoE within our organization. It spearheaded AI implementations and established governance models, setting a benchmark for best practices that accelerated our learning curve and minimized pitfalls. You could modernize your legacy app by following similar steps! #modernization #appmodernization #legacysystem #genai #simform — 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.

Choosing the right monitoring tools for your cloud infrastructure can feel overwhelming. With so many options, it’s easy to get lost in the sea of choices.   But don't worry—we're here to simplify it for you. We've created a cheat sheet that lays out the key monitoring tools across major cloud providers like AWS, Google Cloud, & Azure, as well as some powerful open-source and third-party alternatives. This cheat sheet will: ✔️ Help you quickly understand your monitoring options. ✔️ Save you from making common mistakes when setting up your monitoring infrastructure. ✔️ Point you to the right tools that best suit your project needs. Save this post for easy reference during your next cloud project, and be sure to share it with your fellow tech enthusiasts. This post is inspired by the excellent work from Alex Xu #cloudmonitoring #aws #googlecloud #azure #cheatsheet #monitoringtools