Before one can understand the role of cloud data lakehouses in machine learning and deep learning, one needs to know what machine learning and deep learning actually are.
Anyone vaguely interested in artificial intelligence (AI) might have come across these two terms before. Contrary to some confusion, these terms aren't interchangeable (despite both having something to do with learning). For anyone interested, here's an article that discusses the difference between machine learning and deep learning.
It'll clarify why structured, unstructured, and semi-structured data are all so important in artificial intelligence.
What's the Difference Between Machine Learning & Deep Learning?
To understand how ML and deep learning differ, people first need to understand what these two types of learning are.
What Is Machine Learning?
Machine learning is the process of teaching computers to make decisions and predictions based on a bunch of rules. These include simple if-then logic, using mathematical equations, and neural network architecture.
The algorithms used to teach computers through ML generally rely on structured data.
What Is Deep Learning?
Deep learning is a process of teaching computers in a manner similar to how humans learn. Instead of using structured data with structured rules, this process uses unstructured data. Obviously, this type of learning takes longer and requires specialised AI learning processors.
Deep learning is used for AI that has to mimic human-like decision-making processes; eg., Natural Language Processing (NLP), software for self-driving vehicles, and image recognition software.
So, to summarise, machine learning is a structured learning model that takes less time, whilst deep learning is a more organic learning system, which takes longer, is more complicated, and requires complex hardware.
Machine learning is useful for solving simpler, linear problems, like classification, regression, dimensionality reduction, and clustering.
Deep learning, on the other hand, is used for solving more complex problems, where human-like thinking and processing might be required. These include image and speech recognition, AI game bots, NLP, and autonomous systems.
Structured Data vs. Unstructured Data vs. Semi-Structured Data
So, now that we know what machine learning and deep learning are, let’s move on to structured and unstructured data. As we just saw, both have a role in the development of AI. Here is the difference between the two.
Structured data, as the name suggests, is, well… structured. It follows a standard format and can be worked on directly. If you’ve ever worked with an Excel spreadsheet, with the information neatly organised in cells and tables, you’ve encountered structured data.
Such data is easy to store, access, and process, because it’s all so well organised.
Unlike structured data, unstructured data cannot be organised as easily. It doesn’t follow a standard format and each item in the database could have different properties. Examples of unstructured data include images, video files, audio files, social media posts, or behavioural data.
Since this data is so varied, it cannot be organised into neat little compartments. As a result, it needs more storage space and it can be slightly difficult to retrieve.
This type of data, whilst largely unstructured, does have some organisational logic to it. In fact, some people argue that there is no true unstructured data. Even an image will have some meta-data included, which can be used to retrieve it.
However, unlike structured data, semi-structured data too requires more storage.
This brings us to cloud data lakehouses.
What Is a Cloud Data Lakehouse?
When you want to store clean, organised structured data, you use data warehouses. These are ideal for business intelligence data.
On the other hand, if you want to store unstructured data and semi-structured data, you want data lakes. These types of data can’t be housed in neat, logical data warehouses.
But, having two types of storage for structured and unstructured data means you cannot derive benefits from both. That’s where a data lakehouse enters the picture.
A data lakehouse combines the logical, analytical storage of a warehouse with the flexibility of a data lake—ideal for an artificial intelligence model which uses both deep learning and machine learning.
Whilst a data warehouse is simple in structure, the data lakehouse architecture is largely dependent on your business’s needs. You might need an expert, like Agile Solutions, to help you design a bespoke solution.
However, having a cloud data lakehouse can be an important resource if you want to make the most of the data—both structured and unstructured—that your company owns.
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