Build vs buy for data science platforms
It’s an age-old question among computer scientists, engineers, and business professionals: should you build your data from the ground up or purchase a SaaS solution to make your organisation function more smoothly? There are so many software solutions available in many professions and areas of the company that it can be difficult to keep track of them all. The two most critical things to ask oneself, though, are: What is the maximum amount I am willing to spend? Is there any way to tell how much time I have left? Most companies don’t have a lot of time or money to devote to software that isn’t essential to their product or service. Hence this article puts forward factors that can help you make an informed choice to either build your own data platform or buy one. Time duration of the project Is this a long-term or one-time data project? AI advancements come and go at a breakneck speed, with little indication of slowing down. Setting up Hadoop or Pig clusters were once considered smart technology decisions, but they are no longer so. Just a few important technologies from the big data, data science, and now AI era have become obsolete in approximately the same amount of time as it takes to properly put them up. If the endeavour is intended to be long-term, purchasing a whole platform capable of accommodating rapidly emerging data technologies may be necessary, although short-term initiatives may be simple to assemble using today’s components. Scope Moreover, what is the scope of the data endeavour planned to be? On small-scale AI projects, there is less need for overarching cohesion or coherence, resulting in a more natural “create” scenario. If the bigger objective is to use data to enhance a significant part of the organisation’s activity, various talents and corporate procedures will be required to support project design and operationalisation. In this instance, a buy strategy is more likely to be effective. Time and effort Data scientists frequently spend time developing solutions to supplement their existing infrastructure in order to accomplish projects. Engineering intensive, non-data science duties, including tracking, monitoring, configuration, compute resource management, serving infrastructure, feature extraction, and model deployment, can take up 65 per cent of their time. This squandered time is known as “hidden technological debt,” and it is a common roadblock for machine learning teams. It can take anywhere between 6 months to one year to build an in-house solution or manage an ineffective one. Even once you’ve established a working infrastructure, you’ll need lifecycle management and a committed team to maintain it and keep it up to date with the latest technology. Total cost A thorough examination of the scope of designing, administering, and maintaining a data science platform is required. In the build approach, many firms underestimate the total cost of ownership. Through an assessment, it was found that the TCO of building a data science platform was over $30 million in a four-year scenario where an organisation creates a data science platform supporting 30 data scientists at first (and increasing at a 20% annual rate in following years), while the total cost of buying is only a fraction of that. Human resources It takes a lot of engineering to put machine learning into practice. Each data science team must have an operations team that knows the unique requirements of deploying machine learning models in order to have a smooth workflow. To handle resources, microservices, clusters, and other things, a typical AI team comprises a specialised team of engineers and DevOps. These processes can be totally automated by investing in an end-to-end MLOps platform, allowing operations teams to focus on optimising and maximising the use of their infrastructure. A business must know whether it can afford such a team or whether outsourcing will be a better option. Building a custom platform can be a wise investment, but only if an organisation has the time and resources to devote to development. They should also have a strategy in place to assure communication with external applications in order to ensure long-term survival, as well as user interfaces that are accessible and straightforward for day-to-day use. When time is of the essence and leading digital transformation is a top concern, investing in a comprehensive, extendable platform can save money and improve performance far more than any indigenous solution. It also ensures a specific solution that can be continuously improved by a dedicated partner, allowing your IT team to focus on what they do best instead of being distracted from important business duties.
It can take anywhere between 6 months to one year to build an in-house solution or manage an ineffective one.
It can take anywhere between 6 months to one year to build an in-house solution or manage an ineffective one.
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["data science", "Go", "machine learning", "AI", "ML", "MLOps", "Git", "microservices", "cost", "DevOps", "R"]
["AI", "machine learning", "ML", "data science", "MLOps", "microservices", "R", "Go", "Git", "DevOps"]
Abhishree is a budding tech journalist with a UGD in Political Science. In her free time, Abhishree can be found watching French new wave classic films and playing with dogs.