Let’s say you own a factory that makes computers. You need to have a steady pipeline of parts and raw materials. You can approach this necessity in two ways. The first way is to simply look at what you used during the last batch and make a new order every time. The second is to create a pipeline of steady suppliers, established channels, and a chain that works to deliver you the same results every time.
Trying to do things from scratch every time might work once or twice, but it’s not really scalable to any degree. You’ll focus too much on the small things and not enough to prioritize your results and impact. However, if you spend time building a pipeline, you can automate a lot of the basic work and focus on getting the most out of your operations. Believe it or not, machine learning (ML) works the same way. The data you use is essential, so you should build paths and pipelines that let you access it on demand. Unfortunately, that’s not always the case.
You can have the best-designed ML algorithms, but if you’re scrambling to find the right data to feed them every time you need to retrain them, you’ll be wasting a lot of your resources on gruntwork. That’s where your data pipeline can benefit from Explorium’s data science platform.
What’s in your data pipeline?
It’s not inaccurate to say that your ML models rely on data, but what you really should be saying is that you need a reliable data pipeline to keep your ML models running. A model that uses one set of data once isn’t really useful when you need to continuously make new predictions and gain fresh insights. Think about it this way — the world in which your model is running changes constantly, so why wouldn’t your data?
However, simply finding entirely new data every time is a one-way ticket to poor results. No, what you need is a data pipeline that is constantly adding new data to your sets without requiring any upkeep on your part. A data pipeline means that your models keep running smoothly and remain relevant as new data emerges. With Explorium, it means you get the most relevant and up-to-date data on-demand.
Explorium and the data pipeline, reimagined
It’s just not feasible to rebuild your data pipeline every time you want to re-run your ML models, and Explorium makes sure that’s never the case. However, we go well beyond simply having a place in the cloud to store your datasets so that you can easily run each new model, but we’re getting ahead of ourselves. Let’s first look at how Explorium builds your data pipeline:
- First, you connect your data to Explorium. No matter where you keep your data — databases, apps, analytics tools — you can connect all of them to the platform. Once there, you’ll be able to see your data pipeline with all the relevant connections in a tree structure (which you can build yourself as well).
Next, you run your data pipeline. With the right data connected, you simply run the data pipeline to create a structure that works for your predictive question. Now you have a persistent representation of your data that is easily accessible any time you wish to re-run your models. Moreover, if your data changes, your pipeline will incorporate those changes when you run it again.
- At the same time, Explorium adds thousands of external data sources to your pipeline. More importantly, though, it creates connections to each data source that gives you continuous, real-time access, so you always have the most recent snapshot to work with.
- Now that your pipeline is built, you’re ready to tap into Explorium’s real magic. You can enrich your data, train and test your models, and use our automated feature engineering capabilities to generate impactful, relevant insights with your models. You can do all of this directly on Explorium or use our SDK and API to bring your enriched data into your existing models.
It’s really that easy. The best part of Explorium is that you can skip the grunt work and use your expertise and domain knowledge to maximize your ML models’ potential.
Stop overthinking it, start building better pipelines
Explorium helps you take the unnecessary steps out of your ML and data pipeline, giving you time to focus on results. Instead of having to rebuild your pipeline, you can upload your data, run the platform, and rest easy knowing that every time you want to retrain your models with the most up-to-date versions of your dataset, it’s all just a click away.