Machine learning operations (mlops) for generative ai

Module 1: Data Management for Generative AI

1.1 Data Ingestion Pipelines

Data Ingestion Pipelines in MLOps for Generative AI are automated systems that collect, process, and prepare data for training and evaluating generative AI models. They ensure a consistent, high-quality data stream, crucial for these models’ performance.

Key Functions:

• Data Collection: Gathering data from various sources (databases, APIs, cloud storage, web scraping).
• Data Validation: Checking data for errors, inconsistencies, and missing values.
• Data Transformation: Cleaning, normalizing, and structuring data into a usable format.
• Data Storage: Storing processed data in a centralized location for efficient access.

Examples:

1. Image Generation Model:

   • Data Source: Web scraping for images from various websites.
   • Validation: Checking for image resolution, file type, and presence of watermarks.
   • Transformation: Resizing images to a standard dimension, converting them to grayscale, and augmenting the dataset with rotations and flips.
   • Storage: Storing processed images in a cloud bucket, organized by category.

2. Text Generation Model:

   • Data Source: Reading text data from books stored in a database.
   • Validation: Removing HTML tags, correcting spelling errors, and filtering out offensive content.
   • Transformation: Tokenizing text into individual words or subwords, and converting tokens into numerical representations.
   • Storage: Storing the tokenized and vectorized text in a format suitable for training the model (e.g., TFRecords).

3. Music Generation Model:

   • Data Source: API calls to music databases for MIDI files.
   • Validation: Ensuring MIDI files are properly formatted and contain valid musical notation.
   • Transformation: Converting MIDI data into a sequence of notes, durations, and instruments. Transposing keys and adjusting tempos to introduce variety.
   • Storage: Storing the transformed musical data in a time-series format.

In essence, Data Ingestion Pipelines provide a repeatable and reliable way to get the right data, in the right format, to generative AI models. This leads to better model performance, reduced development time, and improved maintainability.

1.2 Data Versioning and Lineage

1.3 Data Quality Monitoring

1.4 Feature Store Management

Module 2: Model Development and Training

2.1 Experiment Tracking and Management

2.2 Distributed Training Strategies

2.3 Hyperparameter Optimization

2.4 Model Validation and Evaluation

Module 3: Model Deployment Strategies

3.1 Containerization and Orchestration (Docker, Kubernetes)

3.2 Serverless Deployment

3.3 Edge Deployment

3.4 A/B Testing and Canary Deployments

Module 4: Model Monitoring and Observability

4.1 Performance Monitoring (Latency, Throughput)

4.2 Input Data Monitoring (Drift Detection)

4.3 Output Quality Monitoring (Bias Detection, Hallucination)

4.4 Model Health Monitoring (Resource Utilization)

Module 5: Infrastructure as Code (IaC) and Automation

5.1 Infrastructure Provisioning (Terraform, CloudFormation)

5.2 Automated Model Deployment Pipelines (CI/CD)

5.3 Automated Scaling and Resource Management

5.4 Configuration Management (Ansible, Puppet)

Module 6: Security and Governance

6.1 Model Security (Adversarial Attacks, Data Poisoning)

6.2 Access Control and Authentication

6.3 Data Privacy and Compliance (GDPR, CCPA)

6.4 Model Explainability and Interpretability

Module 7: Cost Optimization

7.1 Resource Utilization Analysis

7.2 Model Optimization (Quantization, Pruning)

7.3 Infrastructure Cost Management

7.4 Spot Instance Utilization

Module 8: Version Control and Reproducibility

8.1 Model Versioning

8.2 Code Versioning (Git)

8.3 Data Versioning

8.4 Reproducible Experiments (MLflow, Kubeflow)