You will get End-to-end ML system development
Project details
Built an AI-driven semantic engine using FastText and NLP to auto-classify IT support tickets. Achieved 90%+
accuracy by understanding context and predicting routing categories. Integrated with ServiceNow APIs for real-
time recommendations. Deployed the solution using a custom MLOps pipeline on AWS (EKS, MSK,
DocumentDB).
Designed an end-to-end MLOps pipeline using ZenML, MLflow, and FastAPI for training, versioning, and
inference. Set up CI/CD with GitHub Actions and automated EKS, VPC, IAM setup via Terraform across AWS
accounts. Enabled containerized deployment with ECR and added monitoring via Prometheus and Grafana,
reducing infra setup from 3 days to 2 hours.
accuracy by understanding context and predicting routing categories. Integrated with ServiceNow APIs for real-
time recommendations. Deployed the solution using a custom MLOps pipeline on AWS (EKS, MSK,
DocumentDB).
Designed an end-to-end MLOps pipeline using ZenML, MLflow, and FastAPI for training, versioning, and
inference. Set up CI/CD with GitHub Actions and automated EKS, VPC, IAM setup via Terraform across AWS
accounts. Enabled containerized deployment with ECR and added monitoring via Prometheus and Grafana,
reducing infra setup from 3 days to 2 hours.
AI Algorithms
Autoencoder, Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer Model, Variational Autoencoder, YOLOAI Applications
AIOps, Anomaly Detection, Image Processing, Image Recognition, Machine Translation, Natural Language Understanding, Object Detection, Sentiment Analysis, Text Recognition, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, Replit, Streamlit, TensorFlow, Word2vecAI Models
BERT, GPT-4, LLaMA, Midjourney AI, Stable DiffusionWhat's included
| Service Tiers |
Starter
$1,200
|
Standard
$2,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 30 days | 50 days | 70 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | |||
Batch Normalization | |||
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | |||
MLOps | |||
Model Deployment | |||
Model Documentation | |||
Model Monitoring | |||
Model Testing & Optimization | |||
Model Tuning | |||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | |||
Prompt Engineering | |||
Setup File | |||
Source Code |
About Shivayogimath
Exp Automation Engineer in AIML&MLOPS
Bengaluru, India - 6:09 am local time
I’ve led large-scale projects in AI-driven IT Service Desk Automation, airline operations observability platforms, and enterprise cloud security, working extensively with AWS (EKS, S3, SageMaker), Kubernetes, Terraform, Docker, and CI/CD pipelines. My expertise includes developing and deploying end-to-end MLOps pipelines, fine-tuning BERT models for NLP, building FastAPI microservices, and implementing real-time monitoring solutions with Prometheus and Grafana.
Highlights of my work:
AIMLOps & NLP: Designed and deployed intelligent IT service desk solutions (VESA & STAAR) that classify and route tickets in real time using BERT, spaCy, and FastText.
DevOps & Automation: Automated model training, deployment, and monitoring with ZenML, MLflow, DVC, and GitHub Actions for seamless CI/CD.
Cloud & Infrastructure: Hands-on with AWS services, Kubernetes (EKS), and Infrastructure as Code (Terraform, Helm, Ansible) for secure and optimized deployments.
AI & Analytics: Built multi-agent AI systems for real-time airline operations analytics, leveraging Python, Traditional RAG, AgenticRAG, LangChain, LangGraph, Autogen , N8N and advanced ML models.
I thrive on collaboration across teams, aligning developers, data scientists, and business stakeholders to deliver solutions that are both technically sound and business-focused. My approach combines innovation with operational excellence, ensuring systems are reliable, cost-optimized, and future-ready.
The ever-evolving tech landscape keeps me motivated. I’m always exploring GenAI, LLMOps, and next-gen MLOps practices to bring fresh, cutting-edge solutions to clients.
Let’s connect and explore how we can work together to build intelligent, scalable, and impactful solutions.
Steps for completing your project
After purchasing the project, send requirements so Shivayogimath can start the project.
Delivery time starts when Shivayogimath receives requirements from you.
Shivayogimath works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Discovery & Requirement Gathering
Clarification & Scope Finalization Project Planning & Timeline Estimation Design & Architecture Development & Implementation Testing & Quality Assurance