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Want a robust, modern data science practice? That'll be tens of millions of dollars and years of consistent investment. Building a data science practice and machine learning pipeline takes expertise. It doesn't matter how great marketing teams are at scraping the perfect dataset – transforming raw data into learning isn't easy. Forget understanding the business need, even. There’s a whole checklist after that.
Let’s face it; data science is a luxury many small-to-medium sized businesses can’t afford. Running robust, modern teams means more than hiring a data scientist off LinkedIn. Modern teams need machine learning engineers, ML Ops specialists, data engineers, big data engineers, research scientists…the list goes on. Even if you have access to a wealth of well-structured, quality data, (which most organizations don't) getting to the insights is not a breeze. Production technical systems need things like health monitoring for failures. Redundancy for data integrity. Continuous integration and deployment for updates. All things you shouldn't ask data scientists to create at scale.
Most new hires take about 90 days to be onboarded onto their team. Starting a data science team from zero means a much longer time scale. Before you’re even ready to start training models and getting insights, you need to decide on which data you’d like to collect, make sure the data set has the right columns and attributes, actually collect the data, store it. This step doesn’t even account for the costs of hiring these team members.
The average salary for a mid-level engineer at a top AI company is $550,000. Data science and machine learning expertise is a hot commodity right now, and the numbers aren’t going down. Aside from paying top dollar for actual good data scientists, you’ll need to invest in hardware and software to train your models. In these instances a SaaS tool might seem to be handy, but one machine learning pipeline won’t translate to another.
Keeping the data in-house and outsourcing expertise is the best approach if you’re faced with challenges starting from zero.
You don’t have to spend time and massive recurring costs to spin up a data science team.
Let’s look at a real-world example. One of our clients is running this system to identify high-quality leads on their customer’s websites.
They compared leads they picked in their standard format to leads identified from our ML pipeline as high-potential.
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NineTwoThree is a leading provider of AI application development services, and has been building AI applications since 2016.
Whether you’re starting from zero, have some data best practices in place, or just want to bring in some expertise, we’re here to help. We’ve done this before, successfully, in less time than it takes to start from scratch in-house.
