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KServe: A Robust and Extensible Cloud Native Model Server
If you are familiar with Kubeflow, you know KFServing as the platform’s model server and inference engine. In September last year, the KFServing project has gone through a transformation to become...
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Serve TensorFlow Models with KServe on Google Kubernetes Engine
I introduced KServe as a scalable, cloud native, open source model server in the previous article. This tutorial will walk you through all the steps required to install and configure KServe on a...
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Jetson Mate: A Compact Carrier Board for Jetson Nano/NX System-on-Modules
Containers have become the unit of deployment not just for data center and cloud workloads but also for edge applications. Along with containers, Kubernetes has become the foundation of the...
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Tutorial: Edge AI with Triton Inference Server, Kubernetes, Jetson Mate
In this tutorial, we will configure and deploy Nvidia Triton Inference Server on the Jetson Mate carrier board to perform inference of computer vision models. It builds on our previous post where I...
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Tutorial: Real-Time Object Detection with DeepStream on Nvidia Jetson AGX Orin
Last month, Nvidia unleashed the next-generation edge computing hardware device branded as Jetson AGX Orin at GTC. Courtesy of Nvidia, I was fortunate enough to get a Jetson AGX Orin Developer Kit to...
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Ondat’s Unlimited Nodes for Kubernetes Stateful Workloads
Ondat (formerly StorageOS), the cloud native storage provider, announced the launch of a free community edition, pay-to-go, and custom enterprise editions of its storage platform. Ondat aims to make...
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Zero Trust Network Security with Identity-Aware Proxies
Not long ago, enterprises relied on a combination of VPN and firewalls deployed within the network perimeter to secure their infrastructure and applications to provide users remote access. Once users...
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Acorn, a Lightweight, Portable PaaS for Kubernetes
Acorn, a new application deployment framework launched by the founders of Rancher, comes extremely close to what I expect from a development environment running on top of Kubernetes. For a long time,...
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Acorn from the Eyes of a Docker Compose User
In the last article of our series on the Acorn Platform as a Service for Kubernetes, I introduced the architecture and the design of the Acorn deployment framework. This tutorial will compare Acorn...
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Tutorial: Deploy Acorn Apps on an Amazon EKS Cluster
Acorn is a new application deployment framework for Kubernetes launched by the founders of Rancher. It is open source, simple, lightweight, and a portable framework to deploy and scale microservices...
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Beyond ChatGPT: Exploring the OpenAI Platform
Since the launch of ChatGPT on November 2022, OpenAI has been getting a lot of attention from knowledge workers, developers, and almost everyone who uses the Internet and the web. But, OpenAI has been...
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Prompt Engineering: Get LLMs to Generate the Content You Want
The generative AI models are trained to emit content based on the input. The more descriptive the input instruction is, the more accurate and precise the output is. The input instructions fed to a...
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Google’s Generative AI Stack: An In-Depth Analysis
At the recently concluded Google I/O 2023 conference, the search giant unveiled its generative AI strategy. From Bard to Project Tailwind, generative AI dominated the conference. Google’s long-term...
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How to Reduce the Hallucinations from Large Language Models
In the previous part of this series, we have seen various types of prompts to extract the expected outcome from large language models. In this article, we will explore the techniques to reduce...
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How Large Language Models Fuel the Rise of Vector Databases
Language Models, specifically Large Language Models (LLMs) like GPT-4 and LLaMa, are playing a key role in shaping the future of data management, specifically driving the adoption of a new breed of...
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Generative AI Cloud Platforms: AWS, Azure, or Google?
With the rise of generative AI, the top hyperscalers — Amazon Web Services, Google, and Microsoft — are engaging in yet another round of intense competitive battles. Generative AI needs massive...
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Freshen up LLMs ‘Retrieval Augmented Generation’
The foundation models, including large language models (LLMs) like GPT, are typically trained offline on large corpus data. This makes models ignorant of any data generated after they are trained....
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Tutorial: Build a Q&A Bot for Academy Awards Based on ChatGPT
In a previous article, I introduced the concept of Retrieval Augmented Generation (RAG), which is used to provide context to Large Language Models (LLMs) to improve the accuracy of the response. This...
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Exploring Chroma: The Open Source Vector Database for LLMs
The rise of large language models has accelerated the adoption of vector databases that store word embeddings. A vector database stores data in vector form, leveraging the potential of advanced...
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Tutorial: Use Chroma and OpenAI to Build a Custom Q&A Bot
In the last tutorial, we explored Chroma as a vector database to store and retrieve embeddings. Let’s extend the use case to build a Q&A application based on OpenAI and the Retrieval Augmentation...
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