In an era where data is the new gold, simply storing and retrieving information isn’t enough to stay competitive. Traditional SQL databases are adequate for basic queries, but when it comes to tackling sophisticated tasks like semantic search, recommendation systems, and natural language processing, they fall short. This is where data vectorisation becomes crucial. By converting data into embeddings, businesses can leverage advanced AI techniques to gain deeper insights and enhance their capabilities. Embeddings allow AI models to understand and process complex data relationships, enabling more accurate and effective solutions.
What are embeddings?
Embeddings are dense vector representations of data, typically used in the context of natural language processing (NLP) and machine learning. They transform high-dimensional data, such as text, into lower-dimensional vectors while preserving the semantic relationships between different pieces of data. This allows for more sophisticated and efficient similarity searches compared to traditional keyword-based searches.
When to use embeddings?
Embeddings offer a versatile solution for various complex data tasks where traditional methods fall short. Here are some key scenarios where embeddings can make a significant impact:
Semantic search
Example: A legal firm needs to search through vast amounts of legal documents to find cases that are contextually similar. Using embeddings, they can efficiently retrieve documents that are relevant to specific legal arguments or precedents, even if the exact keywords are not present.
Recommendation systems
Example: An e-commerce platform uses embeddings to analyse user behaviour and preferences. By understanding the context of user actions and product features, the platform can recommend products that align more closely with individual tastes, leading to higher customer satisfaction and increased sales.
Natural language processing (NLP)
Example: A customer service chatbot for a telecommunications company uses embeddings to understand and respond to customer queries more accurately. This enables the chatbot to perform advanced text analytics, provide sentiment analysis, and support language translation, offering a better customer experience.
Anomaly detection
Example: An insurance company employs embeddings to detect unusual invoice claim patterns indicative of fraud. By comparing current transactions against a vectorised history of normal behaviour, the system can identify and flag potential fraudulent activities more effectively.
Benefits of using embeddings over traditional SQL queries
Contextual understanding: Embeddings capture the context and meaning of data, enabling more accurate and relevant search results.
Efficiency: Vector-based searches can be significantly faster and more scalable for large datasets compared to traditional text searches.
Enhanced capabilities: Embeddings support more advanced machine learning and AI applications, such as chatbots and virtual assistants.
Let’s delve deeper into the case of e-commerce product recommendations to understand how embeddings can significantly enhance the personalisation and relevance of product suggestions for customers.
An online retailer wants to enhance its product recommendation system. The current system uses basic keyword matching, which often misses context and fails to recommend relevant products. By transforming product descriptions and user reviews into embeddings, the retailer can create a more sophisticated recommendation engine that understands the context and semantics of user queries and product descriptions.
Improved recommendations: Customers receive more relevant product suggestions, increasing the likelihood of purchases.
Increased customer satisfaction: Better recommendations lead to a more personalised shopping experience.
Higher conversion rates: Relevant suggestions can convert browsing into sales more effectively.
By adopting embeddings, the retailer can leverage advanced machine learning models to understand customer preferences better and provide recommendations that align more closely with individual tastes and needs.
What kind of resources will a business require for transforming their data into embeddings?
Transforming data into embeddings requires a combination of specific skill sets, time investment, and developer resources. Here’s a detailed breakdown:
Skill Set: If your business lacks in-house expertise in this field, you will need to hire skilled professionals, which can be time-consuming and challenging. The demand for experts in machine learning and data engineering is high, and they often command high salaries.
Time: The time required for this transformation varies widely based on data size, quality, current infrastructure, and the tech stack in use. On average, it may take a few months to build the necessary infrastructure, prepare the data, develop search retrieval and data upload mechanisms, create interfaces for data visualisation, and build APIs for interaction. This also includes tailoring the system to specific business use cases, performing ongoing maintenance and rigorous testing for reliability and performance, and implementing caching mechanisms to speed up repeated queries. Larger datasets may extend this timeline further.
Number of developers: A small team of 2–3 developers with the appropriate skill set can efficiently manage this task. However, depending on the complexity and scale of the projects, it might require even more resources.
However, with Trismeg, all these time-consuming and expensive processes are automated and ready for your team. A single LLM/ML engineer or software engineer can lead the project, significantly reducing both the time and cost involved. Trismeg’s automation streamlines the transformation process, enabling your business to leverage embeddings quickly and efficiently.
