Parameter Efficient Fine Tuning — LoRA, QLoRA — Concepts — In this blog, we will understand the idea behind Parameter Efficient Fine Tuning (PEFT), and explore LoRA and QLoRA, Two of the most important PEFT methods. We will understnad how PEFT can be used to fine tune the model for domain specific tasks, at the lowest cost and minimal infrastrcuture. Motivation …

Implement RAG technique using Langchain, ChromaDB on GPT 3.5 Turbo t for conversational chatbot on a PDF document. — In this blog, we will understand an advanced prompt engineering technique called retrieval augmented generation technique, with hands-on examples using Langchain, ChromaDB, and GPT 3.5 Motivation The field of Natural Language Processing (NLP) has seen significant breakthroughs with the advent of transformer-based models like GPT-3. These language models have the ability…

Chapter 10: Configuring Grafana Loki to aggregate all the logs across the Kubernetes cluster and set Grafana alerts to send notifications on Slack — Hey everyone, it’s been more than six months since I got some peaceful time for myself to get back to my series. I have been getting a lot of great feedback and comments from people who have been reading my blogs. Thanks for all your feedback, that is what motivated…

Chapter 9: Set up the Metrics observability and configure the alert manager to send alerts to our slack channel when something goes wrong. Also, configure Grafana as our single pane of glass. — Observability is one of the very key aspects of distributed application architectures, as it's important for us to get real-time alerts on any failures that may happen in the clusters. It consists of visibility and monitoring of the following

Chapter 8: In this blog, we will configure our ingress to use TLS/HTTPS, to access our web application. To generate the certificates we will use Vault, and then to manage certificates we will be using cert-manager Architecture Ingress exposes the services, that we are built to the outside world. It provides…

Chapter 7: In this blog, we will build a ReactJS application, that will provide UI to search for books, and add them to the personal library. We will be using Firebase for authentication, and Hashicorp Vault to inject some of the secrets. …

Chapter 6: In this blog, we will build a Quarkus native image application to store and fetch selected books, by the user. We will be using Panache ORM to access PostgreSQL to store and retrieve the user preferences and will be using PV & PVCs. Book Library Microservice, is called…

Chapter 5: In this blog, we will build the pipeline to build and deploy the Book Information service that we implemented in Chapter 3. We will be using GitHub Actions for Build and ArgoCD to deploy to our local cluster. — In Chapter 4 we walked through the overall GitOps architecture and approach, Please read that chapter, before u go any further. In that chapter, we also created the base infrastructure.

Chapter 4: In this blog, we will build the GitOps infrastructure for the project. We will be using GitHub actions for the continuous integration and ArgoCD for deploying the application to the Kubernetes cluster. We will also be integrating the pipeline with Slack, to notify the status. — GitOps Architecture In Chapter 3, we build a Quarkus application, as you would have gone through the build and deploy process, there are a lot of steps, that we need to execute manually. It is always the best practice to automate the build and deployment (Continous Integration/Continous Deployment).