Nachiketh Murthy’s Post

As a software engineer who's had little to no interaction with ML deployment, I've been very curious as to why we need ML specific deployment solutions. Every time I saw engineers talking about Kubeflow or tensorflow extended, I wondered—Why do we need Kubeflow? Isn’t Kubernetes enough for any kind of deployment? Turns out it's not that simple. In my latest blog I share my journey of exploring Kubeflow and understanding MLOps concepts along the way. If you're curious about MLOps or exploring Kubeflow, join me in unraveling this exciting tool! #Kubeflow #MLOps #TechJourney #MachineLearning  https://lnkd.in/dbDtfB9g

Just completed the second week of MLOps Zoomcamp. The lessons covered include: 1. Experiment Tracking Intro. 2. Getting Started with MLflow. 3. Experiment Tracking with MLflow. 4. Model Management. 5. Model Registry. 6. MLflow in Practice. 7. MLflow Benefits Limitations and Alternatives. The link to the course is below: https://lnkd.in/dy5Md3WN

Today I have completed the fifth lesson of mlops zoomcamp course, it was dedciated to monnitoring ML models. In my opinion this topic was pretty complicated , because it was not only about coding itself , but also demanded maintaining such infrastructure tools as Grafana and Prefect https://lnkd.in/dNTRDHb2 #mlopszoomcamp

In the coming days, I will be uploading a presentation (and, of course, the code) on how to efficiently design machine learning models, considering their resource consumption requirements in order to create an efficient system using W&B (experiment tracking) and MLflow (from the perspective of Pipelines). Stay tuned for more updates on my GitHub.

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Here are the nine reasons users and stakeholders have told us why they care about how much GenAI is in their codebase, i.e. GenAI Code Transparency. The most common reason: PE-backed companies, and companies who wish to take on later-stage investment or exit, expect to be diligenced on their GenAI code usage. It's just like they are diligenced on Open Source. And they want to stay ahead of any surprises.

When you combine AI-powered code generation tools and the organizational context of your code from a platform like Stack Overflow for Teams, you accelerate the code writing process for your developers and achieve higher quality solutions built from a foundation of trusted data and community knowledge. Learn more about our better together approach: https://lnkd.in/e4xTVYhu

🚀 🎉 I want to share my latest YouTube tutorial on Dockerizing Your Machine Learning Model: Download, Run, and Validate Locally. 🐳🔬 In this comprehensive tutorial, I'll guide you through the process of downloading Docker images of your Machine Learning model from Docker Hub and demonstrate how to rerun them seamlessly on your local system. This approach is invaluable as it allows you to ensure consistent performance across different environments. 💻✨ With step-by-step commands, you'll gain the confidence to package and deploy your ML models effortlessly. 📦🚀 Join me in this hands-on session and supercharge your development workflow! Let's make the process of Dockerizing ML models a breeze! 💪 🎥 Watch the YouTube tutorial here: https://lnkd.in/g5QRX_hj But that's not all! I've also made the project code available on GitHub so you can dive deeper into the implementation details. 📂 Check it out here: [GitHub Repository](https://lnkd.in/g5XjbArp) Last but not least, I've created a Docker image for end-to-end object detection, which you can access on Docker Hub. 🐳🔎 Explore it here: [Docker Image URL](https://lnkd.in/gBZ4yNV3) #DockerizeMLModels #MachineLearning #DockerHub #LocalEnvironment #DevelopmentWorkflow. Let's revolutionize the way we package and deploy ML models with Docker! 🚀✨ Watch the tutorial, explore the code, connect on LinkedIn, and let's embark on this exciting journey together! Happy learning! 🎓😊

Over the last year, we've engaged in extensive discussions with developers, engineering leaders, and executives from organizations worth hundreds of billions of dollars about using GenAI in the SDLC.   A common theme emerged: the importance of GenAI Code Transparency – knowing how much code in a codebase originated from a GenAI tool.   In our latest blog post, we explore nine reasons why developers and organizations care about GenAI Code Transparency.    The top reason? Investor-backed companies expect to have to explain their use of GenAI in future technical due diligences—just like they do today for Open Source usage.   Read the full blog post to learn more about each reason and how GenAI Code Transparency can benefit your organization.   Read more here: https://lnkd.in/dyApKkiD