What is the fundamental data architecture for machine learning and AI?

Data Collection: The bedrock of any machine learning or AI system is data. This involves gathering raw data from various sources such as databases, sensors, internet transactions, or user-generated content. The quality and quantity of this data are crucial for the performance of AI models.

Absolutely correct. In my opinion, a platform that unifies data warehousing and data lakes to accommodate all different data formats would be ideal. And this platform is a lakehouse platform which has been introduced and pioneered as a category by Databricks.

A lakehouse platform would be the best option in this scenario, as it combines the strengths of both data lakes and data warehouses. It supports unstructured data, a feature of data lakes, while also incorporating the fine-grained governance model typical of data warehouses. Additionally, it accommodates all sort of use cases, including streaming, data warehousing, business intelligence, machine learning, and data science.

Data Storage: Once collected, the data must be stored in a manner that makes it easily retrievable and manageable. This often requires robust database systems, data lakes, or cloud storage solutions that can handle large volumes of structured and unstructured data. Data Processing: This involves cleaning and transforming raw data into a format suitable for analysis. It may include tasks like handling missing values, normalizing data, encoding categorical variables, and more. The aim is to make the data as informative and representative as possible for the algorithms to work effectively.

Data Analysis and Feature Engineering: Before feeding data into a machine learning model, it’s crucial to analyze it to understand patterns and relationships. Feature engineering is the process of using domain knowledge to extract features from raw data that make machine learning algorithms work.

Model Training: In this phase, algorithms learn from the processed data. This is where the actual "learning" happens, as models adjust their parameters to minimize errors in predictions or to maximize the likelihood of the outcomes given the input data. Model Evaluation: After training, models are evaluated using separate test data to assess their performance. This helps in understanding the model's predictive power and generalizability to new data. Model Deployment: Once a model is trained and evaluated, it’s deployed into a production environment where it can start making predictions or decisions based on new data.

Feedback Loops: An often-overlooked but critical part of the architecture is the feedback mechanism. As models are used in the real world, their predictions and outcomes should be monitored, and the feedback should be used to refine and improve the models continuously

The challenge of data quality becomes more evident with data lakes because data is ingested in raw format with ELT as opposed to ETL which is followed in data warehouse solutions.