Simform’s Post

🚨 ALERT: If your business misses personalized experiences, you are losing revenue. But you can turn things around with the right strategy to interpret data! This process can be summarized into four key stages with Azure Databricks. - Ingestion - Storage - Processing - Delivery To achieve these stages, we recommend utilizing the following tech services: 1️⃣ Capitalize on Azure Event Hubs and Azure Data Factory to pull in real-time customer data from various sources. 2️⃣ Utilize Azure Data Lake Storage. We would organize everything into Bronze, Silver, and Gold layers. 3️⃣ Use Apache Spark within Azure Databricks to analyze customer behavior and develop segmentation models with MLflow. 4️⃣ Integrate with Azure Machine Learning for predictive insights and visualized segments using Power BI, and operationalized insights at scale with Azure Synapse Analytics for complex queries and Azure Kubernetes Service for app deployment. 🎯 Along the way, address challenges around scalability for both structured and unstructured data, utilizing Delta Lake's storage optimization and Azure Purview for governing sensitive data. Look, personalizing services isn't just a nice-to-have anymore; it’s essential. Automating customer segmentation can significantly enhance engagement with your audience. Now, We’re curious: How do you leverage data for personalized services? 💬 P.S. What tools do you swear by for customer insights? (We can’t wait to see your answers!) #customerexperience #azuredatabricks #dataanalytics #azure #simform | Microsoft Azure

Imagine 200 billion rows of data piling up daily. Could your organization handle this? Most would probably hesitate, but KPMG didn’t. They took this challenge seriously and revamped their entire data strategy to turn insights into days instead of weeks. Before using this, KPMG was drowning in data. Here, we’re talking about 236,000 employees, 145 countries, and billions of rows from various sources. Their existing setup just couldn’t keep up, slowing down insights and holding back client services. So, KPMG embedded Microsoft Fabric into their data strategy and brought in these Azure services: #1. OneLake: KPMG used OneLake as a unified data lake, connecting global data sources in real time. So, by integrating with existing systems to eliminate silos and provide instant access to data. #2. Power BI + Copilot: Power BI, integrated with OneLake, enabled real-time data visualization. Copilot automated report generation, speeding up decision-making and reducing manual effort. #3. Azure Databricks: Databricks transformed KPMG’s data pipeline, handling billions of rows efficiently. Processing times dropped from weeks to days. The result? Faster insights, better client services, and a more efficient workflow. Ultimately, KPMG’s move to Microsoft Fabric shows how the right data strategy can transform how you work, serve clients, and scale your operations. Now, how do you manage data overload in your organization? Share your experience below 👇 #simform #azure #datastrategy #databricks #microsoftfabric

Generative AI in healthcare is not just about: 👉 Saving time 👉 Automating tasks 👉 Searching medical literature It’s about trust, transparency, and confidence in every clinical decision. Physicians have made one thing clear: Gen AI's value lies not only in its technology, but in the quality of the content it relies on, the transparency around how it was sourced, and the confidence it provides at the point-of-care. According to one new survey: 📈 40% of doctors are ready to use Gen AI this year during patient interactions. 📈 81% believe it will improve how care teams interact with patients. 📈 Over half expect it to save them 20% or more time. 📈 89% demand transparency on the source of Gen AI's information before they trust it for clinical decisions. Meaning, Gen AI adoption is accelerating, but transparency and data integrity are non-negotiable for healthcare providers. So, this is what you should focus on: 1. Integrate Gen AI responsibly by partnering with trusted vendors who can provide expert-sourced content and transparency around data. 2. Ensure your solutions align with healthcare’s demand for trusted, transparent AI—doctors are relying on AI, but only when they know it’s reliable. 3. Optimize workflows by embedding Gen AI that saves time without sacrificing accuracy or patient care. What’s your take on Gen AI in clinical settings? Ready to trust it? Let us know in the comments! #HealthAI #clinicalai #AIinHealthcare #GenerativeAI #AI #Simform

Pharmaceutical conveyor belts need constant uptime. Azure's AI predictive maintenance helps manufacturers foresee issues, prevent disruptions, and maintain smooth production. Here's how it works: 🔽 1. Data Monitoring: Sensors track conveyor belt conditions like temperature and vibration in real-time, with Azure Time Series API overseeing operational status. 2. Data Streaming & Ingestion: Azure IoT Hub streams data from the floor, while Azure Data Factory orchestrates seamless data ingestion. 3. Data Storage: Collected sensor data is stored in Azure Data Lake and is ready for analysis. 4. Model Training: Azure Databricks preps the data, and Azure Machine Learning trains models to predict potential failures. 5. Real-Time Inference & Alerts: Models are deployed for real-time inference, alerting teams via Power BI or custom dashboards when anomalies are detected. As a pharmaceutical manufacturer, you can optimize operational efficiency and ensure seamless production cycles by leveraging Azure's predictive maintenance technology, leading to higher output and reduced operational risks. P.S. Have you explored predictive maintenance solutions in your manufacturing facility? #ai #azure #healthcare #simform | Microsoft Azure

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

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