Wie können Sie die Datenarchitektur und den Speicher für maschinelles Lernen entwerfen?

Designing Data Architecture for Machine Learning would require following steps: 1. Data Collection: Gather diverse and relevant data sources. 2. Data Preprocessing: Clean, normalize, and transform data for ML readiness. 3. Data Storage: Use scalable, cloud-based storage for easy access. 4. Feature Engineering: Extract meaningful features for ML models. 5. Version Control: Track changes in data and models for reproducibility. 6. Data Security: Implement robust security measures to protect sensitive data. 7. Data Catalog: Maintain a catalog for easy data discovery. 8. Scalability: Design for handling increasing data volumes over time. Build a flexible, well-organized architecture to fuel successful machine learning initiatives.

Moreover, there are varying data formats in which data may get stored. Nowadays, good data cataloging solution options exists, which helps to understand the structure of the data, view sample data, assess data quality.

It is very important to understand the source data. Any assumptions here can lead to extremely deviating conclusions. Any operations done on the data should be to make it cleaner and not add noise to it (by ignorance)

Identify your data sources and quality. The first step is to identify all of the data sources that you will be using for your machine learning project. This may include data from databases, APIs, sensors, web scraping, or user feedback. You also need to assess the quality of your data in terms of accuracy, completeness, consistency, timeliness, and relevance.

A rule of thumb is how convenient is our store for our ML/AI team. What formats their services expect and what is the overall implementation cost on their end. Multi-zone data lake with a multiple native file format capabilities should be a good enough for most organisations.

Storage format is very important so that the pipelines, code, scripts to follow are efficient - both in time and resource management (CPU, Memory, Network, etc.)

Design your data storage system. Once you know your data sources and quality, you can start to design your data storage system. This will involve choosing the right storage technology and architecture for your needs. Some common storage technologies for machine learning include object storage, distributed file systems, and cloud-based storage.

One thing i've found helpful to keep in mind while choosing the storage type beyond data formats is remembering the CAP theorem concept for distributed storage systems (Availability, Consistency and Partitioning) Based on the CAP theorem, you can't have all three guarantees simultaneously in a distributed system. You must make trade-offs depending on your system's requirements and use cases. MongoDB - is designed with a strong focus on consistency by default as writes are acknowledged only when they are written to the primary node and then replicated to secondary nodes Cassandra is designed with a focus on availability and partition tolerance. it provides high availability but may return stale data during node failures.

Data workflows are critical to data architecture as they coordinate data infrastructure, processes, teams, and data flow. Orchestrators usually give you visibility about what happened, when, and why and allow you to take action on specific events, such as sending alerts on failure. Data orchestrators specifically provide control and visibility about the data flow, usually for analytics and automation. More comprehensive data orchestration solutions collect metadata to provide an overview of dependencies between datasets, external systems they interact with, and pipelines that orchestrate that process.

Design your data pipeline. A data pipeline is the process of collecting, processing, and storing data so that it can be used for machine learning. You need to design your data pipeline to be efficient and scalable, so that it can handle the volume and complexity of your data.

This is easiest step to be forgotten. Engineers tend to be so focused on solutions and are generally overwhelmed by finds from the data, they tend to forget the security and governance aspects. Major data leaks / incidents involving data are due to missing policies or control over data or misclassification of data.

Implement data security and governance. It is important to implement robust security measures to protect your data, especially if it contains sensitive information. You also need to establish data governance policies and procedures to ensure that your data is used responsibly and ethically.

Implement data quality monitoring. It is important to monitor the quality of your data on an ongoing basis to ensure that it is still accurate, complete, and consistent. You can use data quality monitoring tools to identify and address any data quality issues that may arise.

This is a very interesting phase in ML. This is where you understand your data better, find anomalies and discover unknow truth about your data. Creating a chart from series of numbers is easy, but to infer the hidden message is fun!

As rightly mentioned, this should be the last step, when all above are in place, however there is a tendency to jump to this step and ignore the previous steps. If the bases for modeling are not proper, how can one expect the model to be of good quality? Like before you can write alphabets, practicing strokes is important, in ML, before you create models, good quality data is a must.

Data Storage Zones As the data lake or lake houses store humongous amount of data over the period of its tenure, It’s always good to have clear separation of physical storage zones based on the logical preparedness of data. For instance, data which is raw in landing zone, data which is derived by some transformations, data which is reconciled, data which is analytics ready, data which is the outcome of the analytics. Moreover, it’s always good to apply security control or access measures based on data users and roles in these separate zones. Lastly, we have to consider multi tenancy in the store, partitioned by the devised tenant partition key.