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Animation tools: Illustrator and After Effects ABOUT US: Covering topics and trends in large-scale system design, from the authors of the best-selling System Design Interview series.

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The system design interview evaluates your ability to design a system or architecture to solve a complex problem in a semi-real-world setting. It doesn’t aim to test your ability to create a 100% perfect solution; instead, the interview assesses your ability to: - Design the blueprint of the architecture - Analyze a complex problem - Discuss multiple solutions - Weigh the pros and cons to reach a workable solution In this video, Neamah explains a helpful high-level framework for system design interviews. 0:00 - Introduction 0:20 - What is a system design interview? 1:24 - Step 1: Defining the problem 1:43 - Functional and non-functional requirements 2:40 - Estimating data 3:10 - Step 2: High-level design 3:30 - APIs 4:00 - Diagramming 4:20 - Step 3: Deep dive 5:10 - Step 4: Scaling and bottlenecks 6:20 - Step 5: Review and wrap up Watch more system design videos here: - Meta engineering manager answers a rate limiter interview question:

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- Flipkart EM “Design Amazon Prime Video” system design interview question:

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ABOUT US: Want to land your dream career in tech? Exponent is an online community, course, and coaching platform to help you ace your upcoming system design interviews. Exponent has helped hundreds of thousands of candidates pursue and land their dream careers at companies like Google, Microsoft, Amazon, and high-growth startups. Exponent is currently licensed by Stanford, Yale, UW, and others. Our system design courses include interview lessons, questions, and complete answers with video walkthroughs. Get access to hours of real interview videos, where we analyze what went right or wrong, and our community of expert coaches and industry professionals to help you get your dream job and more!

Learn something new every week by subscribing to our newsletter:

Checkout our bestselling System Design Interview books: Volume 1:

Volume 2:

ABOUT US: Covering topics and trends in large-scale system design, from the authors of the best-selling System Design Interview series.

Learn something new every week by subscribing to our newsletter:

Checkout our bestselling System Design Interview books: Volume 1:

Volume 2:

ABOUT US: Covering topics and trends in large-scale system design, from the authors of the best-selling System Design Interview series.

Ray’s Plasma object store can reduce the cost of loading deep learning models for inference almost to zero.

Ray is a powerful distributed computing framework. However, as data sets grow and computation requirements become more complex, managing memory usage across multiple computing nodes becomes increasingly challenging. Issues that slow down performance include the data copying between the computing nodes, data spilling out of memory into storage, and the data skew among computing nodes. We'll introduce Gismo, a multi-node shared memory object store based on Compute Express Link (CXL) technology to address this challenge. With the help of Gismo, Ray does not need to serialize and transfer the extra copies of the objects across network. Data spill and skew issues will be minimized because each host has direct memory access to the whole object store. This talk will demonstrate how Gismo is integrated with Ray and showcase how it improves overall performance and reduces memory overhead for Ray users. Find the slide deck here:

About Anyscale --- Anyscale is the AI Application Platform for developing, running, and scaling AI.

If you're interested in a managed Ray service, check out:

About Ray --- Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.

#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai

Terraforming ArgoCD: The GitOps Bridge - Brian Fox, Midnite Terraform may not be the first tool that comes to mind when thinking about managing resources in ArgoCD. The Terraform Provider for ArgoCD can, both literally and metaphorically, bridge the worlds of IaC and GitOps. In the literal sense, it provides a relatively simple mechanism to enable application workloads to be configured using resources provisioned via Terraform. In the metaphorical sense, it provides a stepping stone between working with conventional IaC pipelines and Kubernetes-based GitOps operators. Building on the speaker's experience helping teams migrate workloads from ECS to EKS, this talk looks at the use of the Terraform provider for ArgoCD within existing Terraform-based deployment pipelines. The practice can be a powerful tool that simplifies migrating application workloads to Kubernetes; by reducing the scope of a migration effort and the impact on existing workflows, we can enable golden paths that limit the cognitive load on application teams. But, as always, there are trade-offs involved, and the approach falls short of meeting the GitOps principles.

Want to learn GitOps? ArgoCD is GitOps for Kubernetes. In this practical tutorial video I teach you everything you need to know get started with ArgoCD on your Kubernetes cluster. Watch or follow along with your own Kubernetes cluster and let's implement ArgoCD! 🛍️ Amazon Store (homelab/youtube setup):

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📘 Chapters: 00:00 What you will learn in this video 0:30 Helm Theory - Everything you need to know 1:33 Example Pipeline for ArgoCD 2:45 ArgoCD Disaster Recovery and Multi-Cluster Support 3:36 What we will do in the Lab 4:15 Installing ArgoCD, Portforward, Argo Password, Login 6:39 Deploying a Helm Application with ArgoCD 15:12 ArgoCD to Rollback applications 17:48 Deploying a Kustomize application with ArgoCD 19:44 Kustomize vs Helm Configmap Demonstration 25:13 Using ArgoCD CLI to create, sync, delete and troubleshoot 👨‍💻 Join our Discord Community of DevOps Engineers:

#Kubernetes #ArgoCD #GitOps #Tutorial #GettingStarted #Declarative #Configuration #ContiuousDelivery #Deployment