Build a robust foundation for model development, Well Architected framework , AI Perspective : Part 1

To develop and deploy scalable, reliable AI systems that help you achieve your business goals, a robust model-development foundation is essential. Such a foundation enables consistent workflows, automates critical steps in order to reduce errors, and ensures that the models can scale with demand. A strong model-development foundation ensures that your ML systems can be updated, improved, and retrained seamlessly. The foundation also helps you to align your models' performance with business needs, deploy impactful AI solutions quickly, and adapt to changing requirements.

To build a robust foundation to develop your AI models, consider the following recommendations.

Define the problems and the required outcomes

Before you start any AI or ML project, you must have a clear understanding of the business problems to be solved and the required outcomes. Start with an outline of the business objectives and break the objectives down into measurable key performance indicators (KPIs).

Define the problems and the required outcomes

Before you start any AI or ML project, you must have a clear understanding of the business problems to be solved and the required outcomes. Start with an outline of the business objectives and break the objectives down into measurable key performance indicators (KPIs). To organize and document your problem definitions and hypotheses in a Jupyter notebook environment, use tools like Vertex AI Workbench. To implement versioning for code and documents and to document your projects, goals, and assumptions, use tools like Git. To develop and manage prompts for generative AI applications, you can use Vertex AI Studio.

Collect and preprocess the necessary data

To implement data preprocessing and transformation, you can use Dataflow (for Apache Beam), Dataproc (for Apache Spark), or BigQuery if an SQL-based process is appropriate. To validate schemas and detect anomalies, use TensorFlow Data Validation (TFDV) and take advantage of automated data quality scans in BigQuery where applicable

To create synthetic datasets based on existing patterns or to augment training data for better model performance, use BigQuery DataFrames and Gemini. Synthetic data is particularly valuable for generative AI because it can help improve prompt diversity and overall model robustness. When you build datasets for fine-tuning generative AI models, consider using the synthetic data generation capabilities in Vertex AI.

For generative AI tasks like fine-tuning or reinforcement learning from human feedback (RLHF), ensure that labels accurately reflect the quality, relevance, and safety of the generated outputs.

Select an appropriate ML approach

When you design your model and parameters, consider the model's complexity and computational needs. Depending on the task (such as classification, regression, or generation), consider using Vertex AI custom training for custom model building or AutoML for simpler ML tasks. For common applications, you can also access pretrained models through Vertex AI Model Garden. You can experiment with a variety of state-of-the-art foundation models for various use cases, such as generating text, images, and code.

You might want to fine-tune a pretrained foundation model to achieve optimal performance for your specific use case. For high-performance requirements in custom training, configure Cloud Tensor Processing Units (TPUs) or GPU resources to accelerate the training and inference of deep-learning models, like large language models (LLMs) and diffusion models.

Set up version control for code, models, and data

To manage and deploy code versions effectively, use tools like GitHub or GitLab. These tools provide robust collaboration features, branching strategies, and integration with CI/CD pipelines to ensure a streamlined development process.

Use appropriate solutions to manage each artifact of your ML system, like the following examples:

For code artifacts like container images and pipeline components, Artifact Registry provides a scalable storage solution that can help improve security. Artifact Registry also includes versioning and can integrate with Cloud Build and Cloud Deploy.

To manage data artifacts, like datasets used for training and evaluation, use solutions like BigQuery or Cloud Storage for storage and versioning.

To maintain the consistency and versioning of your feature data, use Vertex AI Feature Store. To track and manage model artifacts, including binaries and metadata, use Vertex AI Model Registry, which lets you store, organize, and deploy model versions seamlessly.

To ensure model reliability, implement Vertex AI Model Monitoring. Detect data drift, track performance, and identify anomalies in production. For generative AI systems, monitor shifts in output quality and safety compliance.