The Role of Cloud Data Lakehouses in Machine Learning and Deep Learning

    Before we understand the role of cloud data lakehouses in machine learning and deep learning, we need to know what machine learning and deep learning are.

    If you’re even vaguely interested in artificial intelligence (AI), you might have come across these two terms.

    I had heard of them but always assumed they were the same thing. Both have “learning” in their name. How different could they be?

    Then, I saw this article discussing the difference between machine learning and deep learning.

    Fascinating stuff.

    It also helped clarify in my head why structured, unstructured, and semi-structured data were all so important in artificial intelligence.

    Right, I may be jumping from topic to topic. Let’s move forward logically.

    What Is The Difference Between Machine Learning and Deep Learning?

    To understand how ML and deep learning differ, we 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, e.g., Natural Language Processing (NLP), software for self-driving vehicles, and image recognition software.

    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

    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.

    Unstructured Data

    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.

    Semi-Structured Data

    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 also 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 that 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’ 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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