MLOps Turnkey

Accelerate your AI journey with AI Gil's MLOps Turnkey solution. This comprehensive package streamlines your machine learning projects from start to finish, covering data pipelines, model deployment, automated retraining, and performance monitoring. Seamlessly integrate cutting-edge ML practices into your operations and gain a competitive edge in the AI-driven market.

from: 13.200 € 30% discount *
from: 9.240 € **

* Limited availability offer (2 out of 3 available)

** Smallest possible project (Four weeks delivery guaranteed)

What You Get

Data Pipeline Development

Designing and implementing efficient data pipelines to collect, process, and transform raw data into a format suitable for machine learning models. This includes setting up ETL processes, data cleaning, and ensuring data quality and consistency.

Included

Model Development & Training

Creating and training machine learning models tailored to your specific business needs. This involves selecting appropriate algorithms, feature engineering, hyperparameter tuning, and optimizing model performance.

Add-on

Model Deployment & Integration

Implementing trained models into production environments, ensuring seamless integration with existing systems. This includes containerization, API development for model serving, and handling model updates.

Included

Automated Model Retraining

Setting up systems to automatically retrain models on new data, maintaining model accuracy over time. This involves creating schedules for retraining, monitoring model drift, and implementing triggers for retraining when performance degrades.

Add-on

Scalable ML Architecture

Designing and implementing a robust, scalable architecture for machine learning operations. This includes selecting appropriate tools and technologies, setting up distributed computing environments, and ensuring the system can handle increasing data volumes and model complexity.

Included

Monitoring & Logging of ML Models

Implementing comprehensive monitoring and logging systems for deployed ML models. This involves tracking model predictions, performance metrics, and system health, as well as setting up alerts for anomalies or performance issues.

Included

CI/CD Pipeline Implementation for ML Models

Establishing continuous integration and continuous deployment pipelines specifically for machine learning workflows. This includes automating model testing, validation, and deployment processes to ensure rapid and reliable updates to production models.

Included

Model Versioning and Management

Implementing systems to track and manage different versions of ML models throughout their lifecycle. This includes version control for model code, data, and hyperparameters, as well as facilitating easy rollbacks and comparisons between model versions.

Add-on

Cloud Infrastructure Setup and Management

Configuring and managing cloud resources optimized for machine learning workloads. This includes setting up virtual machines, storage systems, and networking components, as well as implementing best practices for cost optimization and scalability.

Included

Model Performance Monitoring

Implementing systems to continuously track and analyze the performance of deployed models in real-time. This involves setting up dashboards, defining key performance indicators, and creating alerts for performance degradation or unexpected behavior.

Add-on

Other Considerations

  • Code ownership transferred via GitHub or as a zip file
  • Final pricing determined during the strategy session
  • API hosted on AI Gil's AWS account under the aigil.dev domain
  • Includes 3 months of maintenance; €20/month per service-unit thereafter
  • Option for API hosting on client's own account for an extra fee

Stack

The following technology stack comprises tools I trust and currently use on a regular basis, though not all of them may be employed in every project.

  • AWS

    Cloud Infrastructure

    AWS

    Provides scalable, reliable, and inexpensive cloud computing services. Services include EC2, S3, RDS, Bedrock, Lambda, Sagemaker, and more.

  • Python

    Programming language

    Python

    A versatile language known for its simplicity and readability, used in web development, data science, and more.

  • FastAPI

    REST API framework

    FastAPI

    A modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints. It's very efficient for ML models serving due to its async capabilities.

  • PyTorch

    Machine Learning Framework

    PyTorch

    PyTorch by Facebook is essential for developing and deploying machine learning models. It provides a comprehensive set of tools for both research and production.

  • MLflow

    ML Experiment Tracking and Deployment

    MLflow

    An open-source platform for managing the end-to-end machine learning lifecycle. It tracks experiments, packages code into reproducible runs, and shares and deploys models.

  • Redis

    In-Memory Data Store

    Redis

    Used for caching, session management, real-time analytics, and as a message broker. It can significantly speed up data retrieval in ML applications where model predictions need to be fast and can be cached.

  • Docker

    Containerization

    Docker

    Simplifies deployment by containerizing applications, ensuring consistency across environments and facilitating CI/CD.

  • Kubernetes

    Orchestration

    Kubernetes

    Manages and scales containerized applications, optimizing resource use and automating deployment.