Omjee Mishra

Yes, so, so I'm

in data engineering, where I worked on data collection, data storage, data transformation, okay. And actually, I rely on data to make informed decision. As a data engineer, I always play a vital role in providing the necessary infrastructure and tools. And as a business grows, the data needs grow too. So as a data engineer, we build a scalable system to handle increasing data volume. And I'm good into Python SQL and Scala. And I do have knowledge of relational database and NoSQL. And I do have experience with cloud platform like AWS, Azure, and GCP.

So, uh, in terms of, uh, you know, uh, implementing CI CD pipeline, uh, for deploying, uh, uh, Python application. Okay. Uh, Python application on AWS. Oh, so the service that. So basically on, in terms of, you know, uh, we can use service AWS code, uh, build and test AWS code build, uh, AWS code deploy. Okay. Infrastructure as code that is optional. AWS cloud watch. Okay. AWS code pipeline.

So, basically, if I'll be talking about how we use Docker container to manage, you know, dependency and streamline deployment for Django based machine learning service on AWS. So, basically, the entire application involvement, including all dependency like Python library system. So, outline the various AWS services that can be used for deploying a Dockerite service like Amazon Elastic Container Services, Amazon Elastic Kubernetes Services, Amazon Elastic. So, basically, we can say like, streamline the deployment process by providing consistent. Okay. So, some of the we can consider including a simple diagram. Okay.

So basically, the benefit we can say in terms of Neo4j integration in machine learning workflow, it can be graph-based feature engineering that excel at representing complex relationship between entities. This can be leveraged to engineer powerful features for machine learning model like network embedding, path-based features, community detection. It can also improve model performance as well as scalability and performance. So the approach for integration are data loading, feature engineering, model training, integrating with model training, model evaluation, and we can use Python code with Neo4j.

so basically we can break down how we can implement serverless microservice in AWS lambda using python so basically first we can say microservice is a small independent service serverless architecture where we don't manage server directly okay so first we need to create an AWS lambda function where we can choose runtime write function code configure handler then we can create an API gateway then we can configure API gateway then we can deploy and test so these are the process

So, there can be some of the potential issue like, you know, lack of entirety context in query, hard-coded entity URL, no error handling, potential framework bottleneck, ok. So, we can recommend like refining the query, parse entity URL argument, we can add error handling, we can consider indexing and we can optimize SparkQL queries.

so first we need to do logging then matrix then tracing and in terms of reliability it can be error handling and retry, idempotency, fault tolerance, monitoring and alerting and some of the AWS specific considerations like AWS step function, lambda, batch.

so basically if I will be talking about like you know some of the service component like API gateway lambda function model serving and second one is high availability and fault tolerance in terms of high availability and fault tolerance it can be API gateway lambda function model serving okay data storage and in terms of python implementation it can be request handler inference handler monitoring and logging security testing and deployment

So, yeah, in my last project, actually, you know, I worked on the Django application that hosted a set of RESTful API for a large e-commerce platform where we encountered a performance issue as the application failed to handle increasing traffic. To address this, I implemented several optimization strategies, like database optimization, API endpoint optimization, server-side optimization, cloud-specific optimization. So by implementing these optimizations, I was able to significantly improve the performance and scalability of the Django application.

so basically if I will be talking about in my previous project as I told you I worked on larger scale e-commerce platform where we needed to process a high volume of order and user interaction to achieve this I implemented a task process using Salary a popularly attributed task queue in python so some of the key aspect of implementation are task definition, task queue integration, message broker, task scheduling, error handling and retry.