Build vs Buy: Text Statistics & NLP in 2022
Are you considering adding new natural language processing (NLP) capabilities to your product, application, or analytical infrastructure? But before you dive in, ask yourself: Should I build my system using open-source components or purchase a license from an NLP solution provider?
“Create or Buy” to Get Text Stats and NLP
When it comes to text analysis and NLP, you are faced with a critical decision between building your system with open-source tools, licensing a basic cloud API, or customizing an NLP platform. NLP open-source models have evolved significantly, offering a plethora of free tools and services to choose from. This article will guide you through making an informed decision by exploring your options, the potential of building your system, and selecting the right NLP provider for your needs.
Summary and Key Steps
Building your own NLP system using open-source tools may seem like a cost-effective option, but it often falls short of meeting your requirements. Instead of reinventing the wheel, consider licensing an API or NLP platform based on factors like document types, data volumes, analytics requirements, and other specific needs. This decision can save you time, money, and resources in the long run.
System Cost: $0 (open source)
Technical Costs: $81,000+ (hiring NLP experts and engineers)
Timeframe: Weeks
Skills Required: Limited without additional effort
In contrast, licensing an API or NLP platform may incur some upfront costs but offers immediate access to advanced NLP capabilities, customization options, and expert support.
“Build or Buy” Question Explained
The age-old decision between building or buying a solution is a common dilemma faced by individuals and businesses alike. Whether it’s about cooking dinner, managing investments, or doing laundry, the choice boils down to efficiency and expertise. Building an NLP system from scratch is akin to building a car engine – a complex and time-consuming endeavor with multiple pitfalls.
Basic text analysis involves several layers of processing, including language parsing, sentiment analysis, and entity recognition, all of which require advanced machine learning models. While open-source tools and cloud services can handle simple tasks, they fall short when it comes to custom solutions or in-depth analysis.
When deciding between building or buying an NLP solution, consider your document types, data volumes, analytical requirements, and specific needs. While an open-source model may suffice for basic tasks, a dedicated NLP platform offers a more robust and customizable solution tailored to your business objectives.
Conclusion: Make an Informed Decision
Whether you opt to build your own NLP system or license an existing platform, consider the long-term impact on your business goals and resources. Choosing the right approach can help you unlock the full potential of text statistics and NLP in 2022 and beyond.
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