MLOps Implementation by Mayur Parab

MLOps Implementation by Mayur ParabMLOps Implementation by Mayur Parab

MLOps Implementation

Mayur Parab

Starting at

/hr

About this service

Summary

Transform your ML model lifecycle with MLOps expertise. As a highly skilled data scientist with deep expertise in MLOps, I offer end-to-end machine learning pipeline solutions to help businesses streamline the development, deployment, and monitoring of their machine learning models. With a focus on cloud-based architecture, I ensure that your ML systems are scalable, reliable, and optimized for continuous learning and improvement.

Whether you're looking to automate model deployment, scale operations, or monitor models in production, I provide robust and tailored MLOps solutions to meet your specific business needs. I ensure that your ML pipelines are efficient, scalable, and designed for continuous improvement, helping you achieve fast and reliable model iterations.

Deliverables

Model Development & Training

I will build and train machine learning models based on your business requirements, ensuring high performance and scalability. This includes data preprocessing, feature engineering, and hyperparameter tuning.

CI/CD Pipeline Setup for ML Models

I will create and implement continuous integration and deployment pipelines using tools like Docker, MLFlow, and cloud platforms (e.g., Azure, AWS, GCP) to automate the release of new models and updates, ensuring seamless deployment.

Model Monitoring & Management

I will set up tools to track model performance post-deployment, monitoring key metrics such as accuracy, latency, and data drift. This will ensure that your models remain reliable and relevant over time.

MLOps Infrastructure Setup

I will configure a scalable cloud infrastructure for hosting ML models, ensuring that it is optimized for real-time inference and batch processing. This includes setting up containerization (Docker) and orchestration (Kubernetes) where necessary.

End-to-End ML Pipeline Documentation

I will provide detailed documentation for every component of the ML pipeline, including model architecture, data flow, and the CI/CD processes. This ensures that your team has clear visibility into the system and can maintain it effectively.

What's included

Model Development & Training
I will build and train machine learning models based on your business requirements, ensuring high performance and scalability. This includes data preprocessing, feature engineering, and hyperparameter tuning.
CI/CD Pipeline Setup for ML Models
I will create and implement continuous integration and deployment pipelines using tools like Docker, MLFlow, and cloud platforms (e.g., Azure, AWS, GCP) to automate the release of new models and updates, ensuring seamless deployment.
Model Monitoring & Management
I will set up tools to track model performance post-deployment, monitoring key metrics such as accuracy, latency, and data drift. This will ensure that your models remain reliable and relevant over time.
MLOps Infrastructure Setup
I will configure a scalable cloud infrastructure for hosting ML models, ensuring that it is optimized for real-time inference and batch processing. This includes setting up containerization (Docker) and orchestration (Kubernetes) where necessary.
End-to-End ML Pipeline Documentation
I will provide detailed documentation for every component of the ML pipeline, including model architecture, data flow, and the CI/CD processes. This ensures that your team has clear visibility into the system and can maintain it effectively.

Skills and tools

Prompt Engineer

ML Engineer

Claude

LlamaIndex

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