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You will get End-to-End MLOps Pipeline Setup with AWS SageMaker and MLflow
Rising Talent
Rising Talent
Project details
This project delivers a fully automated, end-to-end MLOps pipeline built using AWS SageMaker and MLflow, designed to take your machine learning workflow from experimentation to reliable production deployment. You get scalable training pipelines, automated model deployment, experiment tracking, model registry integration, and continuous delivery—all structured using industry-standard best practices.
The setup ensures your ML models are versioned, reproducible, and consistently deployable, with CI/CD automating every step from code commit to production rollout. By combining the strengths of SageMaker’s managed ML services with MLflow’s flexible tracking and registry, this solution provides clear visibility, traceability, and governance across your entire ML lifecycle.
Whether you're a startup building your first ML system or an enterprise improving your current workflow, this pipeline is engineered for performance, maintainability, and long-term scaling. You get clean, modular source code, robust automation, well-documented architecture, and a production-ready workflow that significantly reduces manual effort while increasing speed, reliability, and efficiency.
The setup ensures your ML models are versioned, reproducible, and consistently deployable, with CI/CD automating every step from code commit to production rollout. By combining the strengths of SageMaker’s managed ML services with MLflow’s flexible tracking and registry, this solution provides clear visibility, traceability, and governance across your entire ML lifecycle.
Whether you're a startup building your first ML system or an enterprise improving your current workflow, this pipeline is engineered for performance, maintainability, and long-term scaling. You get clean, modular source code, robust automation, well-documented architecture, and a production-ready workflow that significantly reduces manual effort while increasing speed, reliability, and efficiency.
Machine Learning Tools
Amazon SageMaker, Apache Spark, Apache Spark MLlib, Azure Machine Learning, ChatGPT, Databricks Platform, Databricks MLflow, GitHub Copilot, Google AutoML, Google Sheets, GPT-3, Kubeflow, Microsoft Excel, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, TensorFlow, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$500
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | - | - | |
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code |
About Ajay
Senior Machine Learning Engineer Python, Deep Learning, MLOps, AWS/GCP
Lucknow, India - 2:46 pm local time
slow deployments that block releases are the kinds of problems solved here. The focus
is on making machine learning systems reliable, observable, and easier for teams to
iterate on over time.
● Lead end-to-end lifecycle of ML systems: data ingestion, training, evaluation,
deployment, and monitoring in production.
● Build and ship models with Python, TensorFlow, PyTorch, scikit-learn, and strong
statistical foundations.
● Apply MLOps practices (MLflow, SageMaker, registries, experiment tracking) so
models are versioned, auditable, and safe to update.
● Design and operate infrastructure using Docker, Kubernetes, CI/CD, and major
cloud platforms for reproducible deployments.
● Use a QA mindset for ML: tests for data quality, model behavior, and APIs before
any release.
● Work directly with founders and technical teams to connect ML work with
product and business metrics.
● Help teams move from prototypes to long-lived ML services that can be
maintained and extended safely.
I am a senior machine learning and MLOps engineer with around ten years in software
and data-intensive systems. My approach is to clarify the problem, choose pragmatic
solutions, and build systems that remain understandable as they grow. Communication
is straightforward, expectations are explicit, and trade-offs are discussed early so
surprises are minimized later in the project.
Tools & Technologies I Work With:
✅ Machine Learning & AI
Machine Learning, Machine Learning Model, Machine Learning Algorithm, Artificial
Intelligence, Deep Learning, Neural Network, Artificial Neural Network, Convolutional
Neural Network, Computer Vision, Natural Language Processing, OpenCV, Data Science,
Data Analysis, Data Mining, Predictive Analytics, Statistics, Reinforcement Learning.
✅ MLOps & Infrastructure
MLOps, MLflow, Amazon SageMaker, Docker, Kubernetes, CI/CD, DevOps, Cloud
Computing, Amazon Web Services, Google Cloud Platform, Microsoft Azure, ETL
Pipeline, Data Extraction, Data Scraping, Data Annotation, monitoring and logging for ML
systems.
✅ Data & Backend
Python, SQL, PostgreSQL, PostgreSQL Programming, BigQuery, RESTful API, API
Development, FastAPI, Django, Flask, JSON, pandas, NumPy.
✅ QA & Reliability
Automated Testing, Software Testing, Software QA, Selenium, checks for data integrity,
API correctness, and model outputs in staging and production.
Services I Offer
✓ Production Machine Learning Systems
Turn existing ideas or experimental models into services or batch workflows with clear
interfaces, tests, and monitoring.
✓ MLOps Pipeline Design
Set up pipelines for data preparation, training, evaluation, deployment, and rollback
using tools such as MLflow and SageMaker.
✓ Model Deployment and Operations
Deploy models on AWS, GCP, or Azure with Docker and Kubernetes, adding metrics,
logs, and alerts so behavior in production is visible.
✓ Model Quality and Performance Improvement
Improve stability, latency, and predictive performance through better features, tuning,
and evaluation practices.
✓ Data Pipelines for ML Workloads
Design and maintain ETL pipelines and datasets that support both training and real-time
or batch inference, with validation at each stage.
✓ End-to-End ML Engagements
Support the complete path from initial problem framing through to live deployment and
post-launch iteration with client teams.
Relevant skills & keywords:
machine learning, machine learning model, machine learning algorithm, artificial
intelligence, deep learning, neural network, artificial neural network, convolutional neural
network, computer vision, natural language processing, data science, data analysis, data
mining, predictive analytics, statistics, reinforcement learning, Python, Python
scikit-learn, TensorFlow, PyTorch, Keras, MLflow, Amazon SageMaker, MLOps, model
deployment, model optimization, model tuning, ETL pipeline, data pipelines, Docker,
Kubernetes, CI/CD, DevOps, cloud computing, Amazon Web Services, Google Cloud
Platform, Microsoft Azure, SQL, PostgreSQL, BigQuery, RESTful API, API development,
FastAPI, Django, Flask, pandas, NumPy, data visualization, forecasting, monitoring,
production ML, model registry, experiment tracking, OpenCV, transformer model, large
language model, AI model development, AI model training, AI model integration.
Steps for completing your project
After purchasing the project, send requirements so Ajay can start the project.
Delivery time starts when Ajay receives requirements from you.
Ajay works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Review & Environment Setup
Understand your model, data, cloud setup, and CI/CD tools. Configure AWS access, repositories, and initial project structure.
Data & Training Pipeline Integration
Connect your data sources and integrate your training script into a scalable SageMaker pipeline.