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: 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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AWS
Cloud Infrastructure
Provides scalable, reliable, and inexpensive cloud computing services. Services include EC2, S3, RDS, Bedrock, Lambda, Sagemaker, and more.
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Python
Programming language
A versatile language known for its simplicity and readability, used in web development, data science, and more.
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FastAPI
REST API framework
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.
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PyTorch
Machine Learning Framework
PyTorch by Facebook is essential for developing and deploying machine learning models. It provides a comprehensive set of tools for both research and production.
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MLflow
ML Experiment Tracking and Deployment
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.
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Redis
In-Memory Data Store
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.
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Docker
Containerization
Simplifies deployment by containerizing applications, ensuring consistency across environments and facilitating CI/CD.
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Kubernetes
Orchestration
Manages and scales containerized applications, optimizing resource use and automating deployment.