The Stevie® Awards’ Post

https://lnkd.in/eqfHG2rF 💡 Exciting insights on #AIOps and its foundational role in shaping the landscape for modern IT teams! Envision having a skilled assistant at your fingertips—streamlining the definition of system and architecture components, offering diverse patterns with detailed pro/con analyses, and providing ready-to-deploy Infrastructure as Code (IaC). Picture the efficiency boost as DevOps processes seamlessly automate, optimizing delivery times and ushering in a new era of operational excellence. This transformative technology goes a step further, predicting anomalies within your infrastructure and significantly slashing Mean Time to Detect (MTTD) from hours to minutes. Ready to shift gears from a reactive to a proactive, even predictive, framework in IT management? 🚀 I wish to hear your thoughts and ideas on this game-changing paradigm. How do you see #AIOps revolutionizing your approach to IT strategy? Share your insights, and let's spark a conversation! In the coming days, I'll be diving into practical ideas around this, and your input is invaluable! #ITTransformation #cloudcomputing #cloudarchitecture #artificialintelligence #aws

Some trends that will impact enterprises over the next three years. ❅ Containers add value to AI/ML use cases through better agility, high elasticity, and automated pipeline construction. Container management vendors have been building a cohesive link between DevOps toolchains and AI/ML workflow, along with providing ecosystem integration with software and hardware vendors that offer AI infrastructure and data science workbench tools. There is also increased interest in node pools supporting compute instances, with GPUs becoming more critical. ❅ Vendors increasingly offer containerized software or a complete Kubernetes deployment that must run within a container management environment. Some vendors even specify specific container management environments where their software must run. They are finding the same container-enabled benefits around application development agility and speed enterprises are experiencing. ❅ Containers are increasingly being adopted along with, or instead of, virtu VMs for the edge because containers are more adaptable to modern application architectures and more convenient for updating software at the edge. ❅ Traditional container/Kubernetes offerings increasingly overlap with serverless container and non-container offerings. These technologies will increasingly be preferred over standard container/Kubernetes deployments. ❅ Support for stateful applications has been a challenge for many container deployments. However, stateful application support for container deployments (managing data using storage infrastructure accessed by containerized applications through block or file-based abstractions) has improved. And it will get even better with the growing maturity of container-native storage and data management solutions. ❅ As Kubernetes adoptions grow, IT teams increasingly find that they must deploy and manage multiple clusters, which may be located in a single region, on-premises, in the cloud and/or across multiple regions. Cluster fleet management is an emerging set of tools and processes to manage multiple Kubernetes clusters' life cycles and states. The key capabilities include deploying and upgrading orchestration software and distributing containerized applications and operational policies across clusters. Offerings can support single and/or heterogeneous Kubernetes distributions. ❅ Generative AI has enabled a new generation of virtual assistants that leverage large language models. GenAI improves virtual assistant performance, adds new functionality, extends task automation, and supports new value outcomes. GenAI-enabled virtual assistants will add a new user interface for many container management offerings. In Gartner's article 'Quick Answer: Preparing for Future Container Management Trends'

XaaS : An Evolutionary tale I have the privilege in my current company to design, engineer, and operate big data analytics and ML systems from the ground up. My team oversees and manages several hundred big data platform deployments across all our business lines for their specific data-crunching, analytics, and ML needs. The ability to deploy and operate big data platforms at such a scale, requires a conscious trade-off between centralization and federation. Especially in the context of the cloud, aligning early on our systems engineering to our enterprise cloud journey was a key evolution we were able to leverage. But it was not easy and there were many pitfalls along the way that we had to engineer around as the XaaS paradigm itself evolved along the way. TL;DR https://lnkd.in/e6WQAmJi Conclusions In summary, I can share few key lessons learnt in my extensive experience in establishing and successfully evolving our center of excellence for big data analytics and ML platforms. 1) Continuously adopt and align your journey whilst ensuring to not get stuck in any single vendor hyped platform type. You can achieve this by embracing a shift left pattern and factor in scale in your systems design considerations from the very start. 2) Avoid hybrid PaaS at all cost as I explained in the PaaS section above and instead evolve from a pure-play PaaS to SaaS directly. 3) Embrace a shift left approach and thereby empowering your analytics teams whilst at same time implementing well managed oversight and governance procedures and establish stringent guard rails (we call them minimum compliance baselines) upfront. 4) Ensure you can avail of best systems engineers in your team. Systems thinking and systems design is a completely different skill than application development and applies equally for big data analytics and AI platforms.

🌟 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗖𝗹𝗼𝘂𝗱 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 𝗠𝗶𝗱𝗱𝗹𝗲𝘄𝗮𝗿𝗲.𝗶𝗼 𝗟𝗮𝘂𝗻𝗰𝗵𝗲𝘀 𝗮 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗿𝘆 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 🚀 In an innovative leap, Middleware.io has introduced a game-changing observability platform, harnessing the power of Generative AI to streamline and enhance cloud infrastructure management. This platform is not just a tool; it's a visionary leap forward in supporting developers and DevOps teams to navigate the complex dynamics of modern IT ecosystems. 🌐 The platform offers an intuitive and predictive approach, significantly reducing the time spent on debugging and allowing professionals to preempt potential issues with unprecedented accuracy. As the digital space burgeons with multi-cloud environments, Middleware.io's solution stands as a beacon of progress. ✨ 🔍 With this evolution, Middleware.io joins the ranks of companies like IBM, SAP, and Adobe, who are also steering their ship towards the integration of generative AI, reshaping how developers, marketers, and creatives tackle their daily tasks with enhanced efficiency and creativity. 𝗥𝗲𝗮𝗱 𝗠𝗼𝗿𝗲: https://lnkd.in/gANZqmkc #artificialintelligence #datascience #technology #tech #software

Serverless Functions #teamnamos #oracle #oraclecloudcustomerconnect #oraclesupport #saas #saasplatform #saasdevelopment #waterfallprojectmanagement #agile #agiledevelopment #devops #devopscommunity #monolithic #microservicesarchitecture #oci #cloud #cloudinfrastructure #cloudarchitect #cloudinnovation #Integretions #PaaSSolutions

#Use Cases of #Kubernetes: #Container #Orchestration: Kubernetes is primarily used to manage and orchestrate containerised applications, making it easier to #deploy, #scale, and manage containers #efficiently. #Microservices Architecture: Kubernetes is well-suited for deploying and managing micro-services, where different #components of an #application are deployed as independent services. #Hybrid and Multi-Cloud #Deployments: Kubernetes allows organisations to deploy applications consistently across multiple cloud providers and on-premises environments, providing flexibility and avoiding vendor lock-in. #Continuous #Integration and #Continuous #Deployment (CI/CD): Kubernetes integrates seamlessly with CI/CD #pipelines, enabling automated and rapid application deployment and #updates. #Big Data and #Machine Learning: Kubernetes is used to #manage and scale containerised big #data and machine learning #workloads, providing the #necessary #resources to handle large-scale data processing. #Kubernetes has become the #de #facto #standard for container orchestration, enabling organisations to manage modern, cloud-native applications effectively and efficiently. Its extensive #features, vibrant ecosystem, and wide #community support make it a powerful tool for deploying and scaling applications in today's dynamic and fast-paced #IT landscape.

The transition from monolithic systems to microservices marks a strategic shift towards greater efficiency and flexibility in business operations. Microservices enable companies to decompose large applications into smaller, independently deployable services, reducing complexity, enhancing scalability, improving IT resilience and critically, improving the utility of data in these systems by creating tailored solutions. Opportunities for Streamlined Operations Microservices architecture offers significant opportunities for businesses aiming to streamline operations: ◾ Rapid Deployment and Scalability: Enables quicker updates without disrupting the entire system, supporting continuous integration and deployment. ◾ Enhanced Data Management: Segmenting services allows for manageable data handling and storage, reducing silos and improving security. ◾ Cost Efficiency: Utilizing cloud-native technologies optimizes resource usage and reduces overhead costs. To fully leverage microservices, consider a phased approach: 1️⃣ Assessment of Current Infrastructure: Evaluate existing systems to identify components suitable for migration to maximise current use cases. 2️⃣ Strategic Planning: Develop a roadmap for gradual implementation to minimize disruption. 3️⃣ Building Data Management Layers: Establish key data layers: ◾ Bronze Layer: Raw data extraction and storage. ◾ Silver Layer: Data cleaning and standardization. ◾ Gold Layer: Data refinement for specific business needs. Case Study: Simplifying Data Access and Management A practical application of microservices in data management involves simplifying access to enterprise systems like SAP, Salesforce, Oracle, etc. By creating dedicated services for data extraction, cleaning, and presentation, businesses can significantly reduce the time and resources spent on data management tasks. This approach has liberated teams in organisations we've worked with to focus on delivering enhanced customer service using timely and accurate analytics that's easy to access. Leveraging Low-Code Platforms for Rapid Development Microservices are compatible with low-code platforms, simplifying application development and allowing non-technical users to contribute to business applications. This democratization of development accelerates innovation and aligns IT outputs with business needs. Flexible Digital Future The shift to microservices is a strategic response to the need for agile, cost-effective, and scalable IT solutions. Adopting microservices enhances operational flexibility, reduces costs, and accelerates innovation. As the digital landscape evolves, microservices architecture will play a crucial role in enabling businesses to thrive. Ready to explore the benefits of microservices? Contact us at iAi.cx to discover how our expertise in microservices can overcome infrastructure hurdles and maximise the value you extract from the data you own.

Azure Elastic Search - ML Based Enterprise Observability Solution We have implemented an Enterprise Log Monitoring solution that is specifically tailored to address data-level custom monitoring based on the business requirements rather than service-level monitoring. Utilizing the CSA (Cloud Service Architecture) framework, we have effectively extracted audit, error, and log data from major EDP (Enterprise Data Platform) PaaS (Platform as a Service) services. These services include Azure Data Factory, Databricks, App Service, AKS (Azure Kubernetes Service), ADLS (Azure Data Lake Storage), regular storage, and Synapse services. This solution offers proactive alerts, Yes you heard it right!! through the integration of Machine Learning (ML) capabilities it providing anomaly detection and performance degradation analysis based on historical log data. When an issue is detected, it automatically generates a service ticket in ServiceNow, sends email notifications to the operations team, and triggers OpsGenie for on-call support. This architectural framework is well-contained within Azure's PaaS services, with horizontal auto-scaling enabled at all layers, ensuring high availability and reliability. We have leveraged Elastic Search Cloud to support the storage tier. Recent logs are stored in Hot blob storage, while historical logs are stored in the cold blob. Elastic Search, being open source, offers a cost-effective solution where you only pay for the resources you use. In terms of cost-effectiveness, this solution is notably efficient, particularly when handling large log ingestion. In such cases, we have the flexibility to replace Azure Stream Analytics with Azure Databricks streaming integrated with SQL Serverless for more scalability. Additionally, we have used Grafana Enterprise for visualizing and monitoring the dashboards, providing a comprehensive and user-friendly interface for monitoring and analysis.

As #Cloud architects we want to design and manage open-hybrid cloud that supports true #DevOps but DevOps is how best Infra to serve applications What about the data ,considering 🦉70% of enterprise data being generated only in last 2-3 years 🦉 Applications today are not data intrinsic so both observation and actuation is a challenge 🦉DevOps need to evolve to Data friendly A.K.A #MLOPS in one pipeline For specifically to telecoms still many architectures are normative (i.e not fully standardized) but surely #NDAF , #NEF and #Intent automation is where to converge networks to the data world . Two exciting ready to evolve your Network #Clouds for the #Data era . #iwork4dell https://lnkd.in/gK7ww-yw https://lnkd.in/gfeQJ5xj