Sedai’s Post

🔧 𝐓𝐚𝐜𝐤𝐥𝐢𝐧𝐠 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 𝐢𝐧 𝐌𝐮𝐥𝐭𝐢-𝐓𝐡𝐫𝐞𝐚𝐝𝐞𝐝 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬: 𝐊𝐞𝐲 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐭𝐨 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫 🔧 When implementing recovery in a multi-threaded environment—whether on-premises, cloud, or SaaS—there are unique challenges that demand our attention. 🔹 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Handling multiple threads can lead to race conditions, deadlocks, and inconsistent states. Concurrency issues are often complex, making reliable recovery mechanisms critical. Sources like the IEEE and Oracle’s JVM guidelines highlight these complexities. 🔹 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲 & 𝐈𝐬𝐨𝐥𝐚𝐭𝐢𝐨𝐧: Ensuring data integrity across threads can be tricky, especially in environments without strict data isolation. IBM and Microsoft Azure stress that recovery solutions need to enforce transactional integrity even when failures disrupt threads mid-operation. 🔹 𝐒𝐭𝐚𝐭𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Cloud and SaaS environments face added complexity with stateless and stateful architectures. Google Cloud’s documentation discusses state management’s importance in avoiding thread disruptions that affect recovery and reliability. Balancing these is essential for resilient, high-performance systems that reliably recover under any condition. #𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 #𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 #𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲

🚀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 𝗞𝗲𝘆 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗖𝗹𝗼𝘂𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Effective monitoring and observability are critical to ensuring the health, performance, and reliability of your cloud infrastructure. By leveraging logs, metrics, and traces, you gain real-time insights into your systems, enabling proactive issue resolution. 🔹 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Logs: Capture detailed records of system events. Use tools like CloudWatch or ELK Stack to gather logs for troubleshooting and auditing. Metrics: Continuously track system health and performance through metrics like CPU usage, memory, and latency. Tools like Prometheus and Grafana offer deep visibility. Traces: Monitor request flow across microservices, helping to pinpoint the root cause of issues. Tools like Jaeger or AWS X-Ray are essential for distributed tracing. 💡 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Improved Troubleshooting: Pinpoint issues faster by correlating logs, metrics, and traces. Proactive Monitoring: Detect anomalies early and prevent outages before they affect users. Enhanced Performance: Optimize your infrastructure with real-time insights. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻   https://lnkd.in/e2sq98PN                                    https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX #Observability #CloudMonitoring #DevOps #CloudOps #Logs #Metrics #Traces #Prometheus #Grafana #CloudWatch LP809

In the ever-evolving landscape of technology, staying ahead of the curve is not just a goal it's a necessity. At OpsNinja, we constantly innovate and adapt to ensure we provide our clients with the best cloud and DevOps solutions. 💡 Key Trends to Watch: 1. Serverless Computing: Reducing infrastructure management, increasing agility. 2. AI and Machine Learning Integration: Enhancing predictive analytics and automation. 3. Multi-Cloud Strategies: Leveraging the strengths of multiple cloud providers for optimized performance. 4. Security Enhancements: Implementing advanced security measures to protect against evolving threats. 5. DevOps Culture: Fostering collaboration and continuous improvement within organizations. We are excited about these trends and how they will shape the future of our industry! What are your thoughts on the future of cloud computing and DevOps? Let's discuss it! 🚀 #CloudComputing #DevOps #Innovation #TechTrends #OpsNinja

The Transformative Role of Cloud Computing in IT Industries : 1- Cloud computing has revolutionized the IT industry by providing scalable, cost-effective, and flexible solutions. Its ability to dynamically allocate resources ensures businesses can adjust to varying demands without significant capital investment. By shifting to a pay-as-you-go model, companies save on upfront costs and maintenance, allowing them to focus on innovation and growth. 2- Enhanced collaboration is another key benefit, as cloud-based tools enable remote teams to work seamlessly together from anywhere. Data security is significantly bolstered, with cloud providers offering advanced protection measures and compliance with industry standards, reducing the burden on internal IT teams. 3- Furthermore, cloud computing ensures business continuity with robust disaster recovery solutions, minimizing downtime and maintaining operations during disruptions. The cloud also fosters rapid innovation, enabling businesses to deploy new applications quickly and stay competitive. 4- Environmental sustainability is enhanced through the efficient use of energy in large data centers powered by renewable sources. Lastly, improved performance and reliability are guaranteed by cloud providers, ensuring high availability and quick issue resolution. Embracing cloud computing is essential for businesses looking to thrive in the digital age, providing a solid foundation for growth, innovation, and resilience. Boostr Netwave , MakeAuthority , Nikhil Patra , Lingaraj Senapati , Tanmaya Sahu , #cloudcomputing #digitaltransformation #itindustry #businessinnovation #techtrends #hrms #googlecloud #aws #cloud #cloudcomputing #azure #google #googlepixel #technology #machinelearning #awscloud #devops #bigdata #python #coding #googlecloudplatform #cybersecurity #gcp #developer #microsoft #linux #datascience #tech #microsoftazure #programming #amazonwebservices #amazon #software #pixel #xl #azurecloud

🤝 Exciting news in the AI infrastructure space! IBM and AMD have announced a groundbreaking collaboration that will bring AMD Instinct™ MI300X accelerators to IBM Cloud. 🔹 What's coming: AMD's powerful MI300X accelerators will be available as a service on IBM Cloud, enhancing performance for generative AI and HPC workloads. 🔹 Key highlights: • Integration with IBM's watsonx AI platform • Support for Red Hat Enterprise Linux AI • 192GB of high-bandwidth memory for large model inferencing • Enhanced security and compliance capabilities for regulated industries This collaboration marks a significant step forward in making enterprise AI more accessible and efficient. Expected availability: First half of 2025. 💡 What excites me most is how this partnership could help Enterprises optimize their AI deployments while managing costs and performance - a crucial balance in today's AI landscape. #EnterpriseAI #CloudComputing #Innovation #TechNews #IBM #AMD #ArtificialIntelligence Source: IBM Newsroom

Handbook for ML Deployment: Part 3.1 - Provision Infrastructure & Serve Models 📘 To ensure ML models are effective in real-world applications, setting up a supportive ML infrastructure is essential. This environment should promote efficient operation and easy access for end-users or applications. Key considerations for ML infrastructure include: 1️⃣ Scalability & Performance:📈 Infrastructure needs: Handle fluctuating demands with high throughput and low latency, particularly for real-time applications. Cloud platforms like AWS and Azure offer scalable, elastic environments for deploying ML models, using tools like Kubernetes for dynamic resource management and serverless options for reducing server overhead. 🌐 2️⃣ Security & Compliance:🔒  Data protection: Implement encryption, access controls, and authentication to protect sensitive data. Compliance with legal standards such as POPPIA, GDPR or HIPAA is crucial, with cloud services providing built-in security features and compliance certifications. 🛡️ 3️⃣ Cost-effectiveness & Manageability:💰 Cost balance: Manage long-term deployment costs, including setup, operation, and personnel costs like training and maintenance. Managed ML platforms may provide easier deployment and management but can raise issues with flexibility and cost as needs scale. 🧰 The upcoming section, "Model Serving," will delve into deploying these models in a manner that is secure, accessible, and cost-efficient, preparing for detailed discussion in the next instalment. 🚀 #MelioAI #MachineLearning #AIInfrastructure #DataSecurity #CloudComputing #MLDeployment

🧑🎓 Lesson Learned: cheaper cloud compute instances can create massive speedups! 🤯 Last week, Ricardo Elizondo made a significant improvement for a customer's ML pipelines, achieving a 4x speed-up while reducing costs by ~60%. Here's how he did it: The Challenge For months, the customer had been running their pipelines on a "good value" compute type: not the cheapest or priciest, but seemingly reasonable. It worked fine, until they started processing datasets that were hundreds of GBs. One critical pipeline: reading, processing, and storing images for training, took 8 hours in production. Not ideal. The Investigation Together with the customer's team he started digging into the logs and found the compute instances were heavily bottlenecked by disk and network I/O, while CPU usage remained low. The Solution After researching Microsoft Azure's various compute types (there are hundreds of options!), they identified one tailored to the workload: optimized for high I/O rather than raw CPU power. Cost-wise, CPU has a massive impact, while I/O upgrades are relatively cheap. The results? ✅ Pipeline runtime slashed from 8 to 2 hours. ✅ Cost reduced by ~60%. ✅ Happy engineers and smoother production workflows. Key Takeaways 1️⃣ Know your bottleneck: Not all pipelines are CPU-heavy. If disk or network I/O is the limiting factor, investigate compute types that specialize in these areas. 2️⃣ Don't settle: Even if a setup works, reassess it as workloads evolve. What works for small datasets may fail at scale. 3️⃣ Leverage metrics & logs: They're crucial for identifying bottlenecks and data-driven optimizations. This was a great reminder that small infrastructure tweaks can lead to big wins—both in speed and cost efficiency. 🚀 💸 Have you ever turned a major bottleneck into a big win? Share your stories or tips below! 👇 #MachineLearning #MLPipelines #CloudComputing #Optimization #LessonsLearned #Azure #MLOPS #DataEngineering #CloudOptimization #DevOps

The recent Amazon Web Services (AWS) #Reinvent in Las Vegas contained a raft of announcements with major implications (and potential opportunities) for #telecom. From #silicon and #GPUs through to #network #automation #software, #AI and #GenAI, via #ecosystems, AWS demonstrated why its influence on the shape of the #telecom industry should not be underestimated. In an extensive 14-page report, Patrick Kelly covers the breadth and depth of announcements and updates from Re:invent from a telecom industry perspective. Includes a report of an *exclusive* Q&A session between AWS CEO Matt Garman and invited analysts. There are #hyperscalers, and there are hyperscalers... [paywall/free on subscription] https://lnkd.in/evA7dEii

While #AWSCloudWatch is a good service for basic monitoring and alerts, on its own it may not be the best solution for #logdata at scale. 📈 Common user interface and scalability issues can hold users back from leveraging Amazon CloudWatch logs for troubleshooting use cases. Whether cloud infrastructure logs across #AWS services, container logs for Lambda functions, security telemetry data, or network device logs, CloudWatch can become unwieldy under the weight of non-stop log streams. CloudOps, DevOps, SecOps, and business users demand better access to more logs for longer periods of time, which requires a dedicated log analytics solution to complement CloudWatch metrics monitoring. 🔎 Querying and scaling data isn’t the best use case for CloudWatch. Once teams reach terabyte scale (and need log retention beyond a short period of time such as a few days or a week), CloudWatch can become impractical and difficult to use. This is especially true if you need a longer retention period for compliance reasons, or to tap into the value of long-term log storage for security use cases, forensics, or customer and product analytics. With that said, choose a #loganalytics strategy that gives you the flexibility to store your data anywhere, and for the long run. Even if you use CloudWatch to collect data initially, unlock additional value by storing all data centrally in Amazon S3 and enabling analytics with a more powerful platform like ChaosSearch. ⚡ Discover how to make CloudWatch more efficient with ChaosSearch and achieve better CloudWatch Log Insights: https://bit.ly/3AarQR8

While #AWSCloudWatch is a good service for basic monitoring and alerts, on its own it may not be the best solution for #logdata at scale. 📈 Common user interface and scalability issues can hold users back from leveraging Amazon CloudWatch logs for troubleshooting use cases. Whether cloud infrastructure logs across #AWS services, container logs for Lambda functions, security telemetry data, or network device logs, CloudWatch can become unwieldy under the weight of non-stop log streams. CloudOps, DevOps, SecOps, and business users demand better access to more logs for longer periods of time, which requires a dedicated log analytics solution to complement CloudWatch metrics monitoring. 🔎 Querying and scaling data isn’t the best use case for CloudWatch. Once teams reach terabyte scale (and need log retention beyond a short period of time such as a few days or a week), CloudWatch can become impractical and difficult to use. This is especially true if you need a longer retention period for compliance reasons, or to tap into the value of long-term log storage for security use cases, forensics, or customer and product analytics. With that said, choose a #loganalytics strategy that gives you the flexibility to store your data anywhere, and for the long run. Even if you use CloudWatch to collect data initially, unlock additional value by storing all data centrally in Amazon S3 and enabling analytics with a more powerful platform like ChaosSearch. ⚡ Discover how to make CloudWatch more efficient with ChaosSearch and achieve better CloudWatch Log Insights: https://bit.ly/3AarQR8