Tools and Technologies for MLOps
Category | Tool/Technology | Purpose |
---|---|---|
Data Management | Apache Kafka | Real-time data streaming |
Apache Airflow | Workflow orchestration | |
Model Development | Jupyter Notebooks | Interactive model development |
TensorFlow | Building and training models | |
Model Deployment | Docker | Containerizing models |
Kubernetes | Orchestrating containerized applications | |
Monitoring and Maintenance | Prometheus | Monitoring metrics |
Grafana | Visualizing performance data | |
CI/CD | Jenkins | Automating integration and deployment |
GitLab CI | Managing CI/CD pipelines |
MLOps Pipeline: Implementing Efficient Machine Learning Operations
In the rapidly evolving world of artificial intelligence and machine learning, the need for efficient and scalable operations has never been more critical. Enter MLOps, a set of practices that combines machine learning (ML) and operations (Ops) to automate and streamline the end-to-end ML lifecycle.
This article delves into the intricacies of the MLOps pipeline, highlighting its importance, components, and real-world applications.
Table of Content
- MLOps Pipeline: Streamlining Machine Learning Operations for Success
- Steps To Build MLops Pipeline
- 1. Data Preparation
- 2. Model Training
- 3. CI/CD and Model Registry
- 4. Deploying Machine Learning Models
- Tools and Technologies for MLOps
- Implementation for Model Training and Deployment
- Strategies for Effective MLOps