MLOps

MLOps: Streamlining Model Training and Deployment

MLOps is a streamlined process encompassing model training and deployment, ensuring end-to-end management of machine learning models. The Augmatrix platform provides a comprehensive suite of features for model training, analytics, deployment, monitoring, and testing.

A. Model Training

Model training on the Augmatrix platform is divided into two categories for user-friendly experience:

1. Base Model Training: Utilize the Model Explorer feature to select from various models tailored to different pipelines. Train and deploy your chosen model effortlessly.

2. Fine-Tuning of Trained Model: Augmatrix supports fine-tuning of already trained models, enabling users to further optimize performance.

Example Model Training: Basic Text-classification Model Training

1. Navigate to Model Explorer.

2. Select the text-classification pipeline.

3. Choose a model and initiate training.

4. Provide a model name and select annotated data.

5. Copy credentials and redirect to Google Colab.

6. Complete the training process.

7. View the trained model in the Model Catalogue.

8. Access model metrics from the Model Catalogue.

B. Model Deployment

Deploying machine learning models is crucial for making them accessible and usable. Augmatrix platform offers a user-friendly and secure environment for deployment, providing dedicated infrastructure to safeguard model data.

User-Friendly Deployment Options:

• Cluster Configuration: Configure computational resources.

• Computation Type: Choose between CPU or GPU.

• Computation Size Selection: Dynamically select computational size.

• POD Size Selection: Flexibility with a range of pod sizes.

Scalability Options: CPU-Based Scalability: Auto-scales based on pod usage. Request-Based Scalability: Scales based on incoming requests.

Endpoint Selection: Private Endpoint: Enhanced security. Public Endpoint: Balances security with accessibility.

Deployment Process Summary:

The deployment process concludes with assigning an endpoint, integrated seamlessly into workflows, ensuring security and adaptability.