You will get End-to-End MLOps Pipeline for ML Model Deployment
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Project details
Build a scalable End-to-End MLOps Pipeline for seamless machine learning model deployment, automation, monitoring, and lifecycle management. This solution is designed to help businesses streamline ML workflows, accelerate deployment, and maintain reliable production-ready AI systems with modern DevOps and cloud technologies.
The pipeline includes model deployment, CI/CD automation, containerization, orchestration, monitoring, and cloud infrastructure setup using tools like Docker, Kubernetes, FastAPI, GitHub Actions, MLflow, and AWS/GCP services. The system is optimized for scalability, reliability, security, and continuous model delivery for AI and machine learning applications.
Services Included
✔ MLOps Pipeline Development
✔ ML Model Deployment
✔ CI/CD Pipeline Automation
✔ Docker Containerization
✔ Kubernetes Deployment
✔ FastAPI / Flask API Development
✔ Cloud Infrastructure Setup (AWS/GCP/Azure)
✔ MLflow Integration & Model Versioning
✔ Monitoring & Logging Setup
✔ Workflow Automation
✔ Infrastructure as Code (IaC)
✔ Secure Authentication & API Security
✔ Scalable Production Deployment
✔ Performance Optimization
✔ Post-Deployment Support
The pipeline includes model deployment, CI/CD automation, containerization, orchestration, monitoring, and cloud infrastructure setup using tools like Docker, Kubernetes, FastAPI, GitHub Actions, MLflow, and AWS/GCP services. The system is optimized for scalability, reliability, security, and continuous model delivery for AI and machine learning applications.
Services Included
✔ MLOps Pipeline Development
✔ ML Model Deployment
✔ CI/CD Pipeline Automation
✔ Docker Containerization
✔ Kubernetes Deployment
✔ FastAPI / Flask API Development
✔ Cloud Infrastructure Setup (AWS/GCP/Azure)
✔ MLflow Integration & Model Versioning
✔ Monitoring & Logging Setup
✔ Workflow Automation
✔ Infrastructure as Code (IaC)
✔ Secure Authentication & API Security
✔ Scalable Production Deployment
✔ Performance Optimization
✔ Post-Deployment Support
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, Keras, MLflow, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, PyTorch, RapidMiner, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$25
|
Standard
$250
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 1 day | 5 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | |||
Model Documentation | |||
Ontology | |||
Source Code | |||
Taxonomy |
Frequently asked questions
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DF
Danisavage F.
Aug 14, 2025
Spam detection and email analytics dashboard
About Indumati
Data Engineer | Python, AI/ML, Power BI, ETL/ELK, Neo4j, SQL, AWS, API
100%
Job Success
Jamui, India - 1:25 am local time
🚀 I am specializing in LLMs, RAG Systems, AI Agents, and Generative AI Applications.
💡 Helping startups and businesses build production-ready intelligent, scalable, data-driven, and AI-powered solutions—from machine learning models and LLM applications to data engineering, automation, and full-stack SaaS platforms—designed for performance, reliability, and growth.
🎯 Focus: Designing, developing, and deploying scalable AI solutions that drive automation, intelligence, operational efficiency, and business growth.
I help businesses transform data into intelligent applications, automated workflows, and interactive analytics platforms. With expertise in AI/ML, data engineering, and modern visualization technologies, I build scalable solutions that convert complex data into actionable insights.
Core Skills
• Python Development
• Artificial Intelligence & Machine Learning
• Generative AI & LLM Applications
• Data Analytics & Business Intelligence
• Graph Databases & Knowledge Graphs
• Interactive Data Visualization
• Cloud Data Engineering
• API Development & Integration
• AI Automation & Workflow Optimization
Tech Stack
Programming & Backend
• Python
• FastAPI
• REST APIs
• SQL
AI & Machine Learning
• OpenAI
• Claude
• LangChain
• RAG Pipelines
• AI Agents
• Vector Databases
• NLP
• Predictive Analytics
Data Analytics
• BigQuery
• Neo4j
• Power BI
• Looker
• ETL Pipelines
• Data Warehousing
Data Visualization
• D3.js
• Interactive Dashboards
• Real-Time Analytics
• Custom Reporting
Cloud & Deployment
• AWS
• Docker
• CI/CD
• Serverless Architecture
🏢 𝗜𝗡𝗗𝗨𝗦𝗧𝗥𝗜𝗘𝗦
Healthcare, Finance & FinTech, E-commerce, Real Estate, Education, Legal Tech, Marketing & Sales, Logistics & Supply Chain, Human Resources, Customer Support, Manufacturing, SaaS Startups & Enterprises
Keywords: Data Scientist, AI Developer, Machine Learning Engineer, Business Intelligence Developer, Power BI Expert, Dashboard Developer, Python Developer, Data Analyst, AI Agent Developer, Generative AI, ChatGPT, OpenAI, RAG, ETL, SQL, Data Engineering, SaaS Development, Analytics Consultant, KPI Dashboard, Data Visualization, Backend Engineer, Full Stack Developer, API Integration, AI Engineer, MCP Server, LLM Apps, RAG Pipeline, Vector Database, Graph Database, LangChain, LangGraph, GraphRAG, LangSmith, Neo4j, Elasticsearch, Kibana, Grafana, OpenClaw, OpenSearch, AI Bot, Agentic AI, Workflow Automation, AI Automation.
What I Build
✔ AI-Powered Applications
✔ Machine Learning Solutions
✔ Analytics Dashboards
✔ Business Intelligence Platforms
✔ Knowledge Graph Systems
✔ BigQuery Data Pipelines
✔ Neo4j Graph Databases
✔ Interactive D3.js Visualizations
✔ Generative AI & Claude Integrations
✔ End-to-End Data & AI Platforms
I focus on delivering scalable, production-ready solutions that combine AI, analytics, and modern data engineering to drive business growth and operational efficiency.
Steps for completing your project
After purchasing the project, send requirements so Indumati can start the project.
Delivery time starts when Indumati receives requirements from you.
Indumati works on your project following the steps below.
Revisions may occur after the delivery date.
End-to-End MLOps Pipeline for ML Model Deployment
• Gather project requirements and deployment goals • Analyze ML models • Design MLOps architecture • Set up cloud infrastructure • Implement CI/CD pipeline • Containerize using Docker • Configure Kubernetes • Deploy ML models & API service • Testing