This is the sixth post in our “Precision Agriculture series.” In this part, we’ll address the fifth challenge of data labeling: smart tooling. Be sure to stay tuned for our seventh and final post in this series, addressing how you can find the right platform to overcome these five most common data labeling challenges.
High quality data relies on a combination of well-trained workers and smart tooling such as AI-assisted data annotation, automation, data management and data pipelines. We see that as AI reaches more domains and is expected to understand more human tasks, tooling requirements keep rising.
Help! Our Data is Growing Exponentially and We Can’t Keep Up!
In our experience, organizations that begin with tools built in-house often discover they tend to build for those early stage purposes. As soon as their organization starts scaling, then they start to question if their in-house tooling still suits their needs.
For example, a smart farming business might decide to develop in-house models that demand large amounts of data to classify and cluster specific types of weeds in order to generate a more urgent alert when it spots particularly destructive and fast-multiplying species.
The cost to develop this tool might be negligible, but what about when the amount of data grows? Climate change, unintentional plant migration by global travelers, and a growing hardiness to herbicides are causing new weeds to appear around the world. One of the unexpected side effects of the coronavirus was that urban areas scaled back their weed control programs, causing rises in certain plant populations in roadside verges and increasing the amount of data that your in-house labeling team has to handle. Models that had been ticking over smoothly suddenly had to consume a lot more data at a much faster rate in order to spot patterns and make predictions in the changed environment, raising costs for businesses.
Without having factored in the expansion of their data labeling needs, businesses could discover that in-house tooling is demanding far too much of their time and money for something that isn’t core to their business focus. As I mentioned above, due to the repercussions of Covid-19, climate change, plant migration and newly appearing weeds, the situation demands a more cost-effective solution for farmers. This is where a single platform becomes beneficial allowing farmers the ability to upload terabytes of data, classify and adapt it, build new models and connect to existing ones, train data, and repeat the iterative cycle without exorbitant maintenance costs. By using external platforms that support dynamic workflows, you can draw on existing tools and integrate pre-established functions into your processes, thereby cutting the cost of training new models.
When you face the question of whether to build or buy, it is important to take into account both your short term as well as long term goals for the business. Building an internal data tool means risking paying over the odds in terms of time, cost of going to market, and continual maintenance.
As your project matures and your business begins to scale there’s going to be an increase in (unforeseen variables) and you’re going to continually need to invest in more resources, something that will inevitably slow down your projects, making it more expensive, more time consuming, and less valuable. When this happens you know it’s time to reassess your options. It’s important to consider whether the tools you’re considering are suited for your needs now, as well as in the future. That’s why it’s critical you find a platform that is robust enough to evolve with your projects, but also mature enough to ensure stability.
Today, it’s all about moving fast and delivering quality. And the fact is, businesses have many workflows that are repetitive, time consuming and challenging, ending up delaying moving to production whilst at the same time spiking operational costs. The solution to bringing these costs down is by automating these processes in order to be able to scale as well as cut down the expenditure on production.
Automoting these tasks not only cuts down on this manual work, but also eliminates human error. The only thing left to do is to ascertain which platform is the right fit for your businesses needs.
In our next and last post of this series, we’ll discuss how to choose the right platform and beat data labeling challenges. If you’d like to discuss your specific needs with an expert and see how Dataloop can help you, then click here.