Janakiram Msv
The New Stack
India
Contact Janakiram
Discover and connect with journalists and influencers around the world, save time on email research, monitor the news, and more.
| Recent: |
|
| Past: |
|
Past articles by Janakiram:
Tutorial: Deploy Acorn Apps on an Amazon EKS Cluster
In the last and final part of the series on Acorn, we will explore how to move applications running in a local development environment to a production environment running in the AWS cloud → Read More
Acorn from the Eyes of a Docker Compose User
Acorn simplifies the definition of multicontainer workloads while translating it to respective Kubernetes objects. → Read More
Acorn, a Lightweight, Portable PaaS for Kubernetes
What fascinates me about Acorn is its simplicity and portability. It treats Kubernetes as an ideal runtime environment for deploying, scaling, and running applications without any assumptions. → Read More
Zero Trust Network Security with Identity-Aware Proxies
Implementing Zero Trust is not an easy task for enterprise IT. Some of the challenges include the sprawl of user identities, the proliferation of internal applications, reliance on third-party SaaS applications, and the rise of hybrid, multi-cloud, and edge architectures. → Read More
Ondat’s Unlimited Nodes for Kubernetes Stateful Workloads –
This tutorial will show you how to install Ondat Community Edition on an on-prem clusters running in a bare metal environment. → Read More
Tutorial: Real-Time Object Detection with DeepStream on Nvidia Jetson AGX Orin
This tutorial will walk you through the steps involved in performing real-time object detection with DeepStream SDK running on Jetson AGX Orin. → Read More
Tutorial: Edge AI with Triton Inference Server, Kubernetes, Jetson Mate
In this tutorial, we will configure and deploy Nvidia Triton Inference Server on Jetson Mate to perform inference of computer vision models. → Read More
Jetson Mate: A Compact Carrier Board for Jetson Nano/NX System-on-Modules
A project to build a powerful AI inference engine based on 4X NVIDIA Jetson Nano modules, SeeedStudio’s Jetson Mate Mini, NVIDIA Jetpack 4.6, and the NVIDIA Triton Inference Server. → Read More
Serve TensorFlow Models with KServe on Google Kubernetes Engine
This tutorial will walk you through all the steps required to install and configure KServe on a Google Kubernetes Engine cluster powered by Nvidia T4 GPUs. → Read More
KServe: A Robust and Extensible Cloud Native Model Server
KServe is collaboratively developed by Google, IBM, Bloomberg, NVIDIA, and Seldon as an open source, cloud native model server for Kubernetes. → Read More
Model Server: The Critical Building Block of MLOps
A common misconception is that deploying models is as simple as wrapping them in a Flask or Django API layer and exposing them through a REST endpoint. Unfortunately, this is not the most scalable or efficient approach in operationalizing ML models. → Read More
Tutorial: Speed ML Training with the Intel oneAPI AI Analytics Toolkit
The objective of this guide is to highlight how Modin and Scikit-learn extensions are a drop-in replacement for stock Pandas and Scikit-learn libraries. → Read More
Intel oneAPI’s Unified Programming Model for Python Machine Learning –
Let's take a closer look at Intel Distribution of Modin and Intel Extension for Scikit-learn. → Read More
VMware Tanzu Application Platform: A Portable PaaS for Kubernetes
An introduction to the VMware Tanzu Application Platform. → Read More
Tutorial: A GitOps Deployment with Flux on DigitalOcean Kubernetes
the step-by-step guide to using FluxCD with DigitalOcean Kubernetes. → Read More
5 AI Trends to Watch out for in 2022
Multi-Modal AI and Large Language Models are among the breakthroughs are among the breakthroughs that will keep AI moving ahead in 2022. → Read More
5 Cloud Native Trends to Watch out for in 2022
WebAssembly, GitOps, and managed Kubernetes are among the trends we expect to see in Clod Native 2022. → Read More
Review: Build a ML Model with Amazon SageMaker Canvas
In this post, we will do a hands-on evaluation of Amazon SageMaker Canvas. Follow along to train a logistic regression model. → Read More
Tutorial: Deploying TensorFlow Models with Amazon SageMaker Serverless Inference
The tutorial shows how publishing serverless inference endpoints for TensorFlow models. Hope you found it useful. → Read More
Explore Amazon SageMaker Serverless Inference for Deploying ML Models
Launched at the company’s re:Invent 2021 user conference earlier this month, ‘ Amazon SageMaker Serverless Inference is a new inference option to deploy machine learning models without configuring and managing the compute infrastructure. It brings some of the attributes of serverless computing, such as scale-to-zero and consumption-based pricing. With serverless inference, SageMaker decides to… → Read More