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Is artificial intelligence or machine learning relevant to me?
Business users in nearly every industry use technologies that feature artificial intelligence (AI) or machine learning (ML), a subset of AI, to automate tasks and save money on time and labor — sometimes without even realizing it. There’s AI and ML integrated into email, banking, and marketing tools; social media feeds, smart assistants, and chatbots; and in manufacturing and retail services.
The first few steps in an organization’s foray into digital transformation may be to adopt these tools, but as businesses scale, they may find that they need customized solutions that are more robust. Further digital transformation may require orgs to either build their own ML models or adopt pretrained ML models to:
- Automate repetitive manual data work at a greater scale
- Allow for better and faster data analyses
- Create tailored services for clients
- Improve data and network security
Adopting machine learning into your business
To incorporate machine learning into your business, you could create a machine learning algorithm from the ground up, or you can choose a pre-trained model, where much of the work has been done for you. Either option could be workable for an organization with an internal data science team, along with ample computational resources and time, to get this task done. Many businesses don’t want to allocate time and resources toward building their own ML solutions from scratch, so they opt for pretrained ML models.
Pretrained ML models: Do they fit?
Pretrained ML models are algorithms with parameters that are built to solve a certain category of problems. The goal is accurate recall and precision by being trained over large sets of data, but this doesn’t always yield great results with new incoming data. You increase your chances of getting great results by increasing the size of your training and testing datasets.
There are a number of reasons why a business may adopt a pretrained model. They tend to be useful to a wide audience for a number of general use cases. Also, if a business ends up using a very similar dataset as to what the model was trained on, the end results could be of comparable quality as the test results.
Though a company can save time by adopting pretrained models over building models from scratch, these models still need tweaking and re-training. Customizing pretrained models can require a considerable amount of time and resources.
Many pretrained models are purpose-built for specific tasks, so they tend to be brittle and fail when they encounter new, unique data or document layout. Even the slightest variations, like a rotated page or text in a new location on the document can cause them to break. These models may work well in a vacuum and with consistent data, but can easily fail when faced with unexpected or new elements.
A better solution: an ML model that adapts itself to fit you
Impira is the better solution for businesses who want to adopt machine learning into their workflow, but don’t have in-house data science team or the resources to do so.
By not relying on pre-built ML models, Impira stands ready for your data, and upon receiving it, will begin to automatically build a model in response. As you interact with it, Impira’s custom models will refine themselves and improve in accuracy and confidence. This process is possible because Impira combines several technology tools like OCR, AI, and AutoML that work in tandem so you can create and deploy customized models automatically.
Even if your company doesn’t have an in-house data science team, Impira makes it possible to harness the power of AI and ML with the workforce you currently have in place. Training machine learning models is no longer a daunting task because you don’t need tons of training data, time, or computing power to do so. You can go from signing up for a free account to getting set up to extract data in just a few minutes.
Other pre-built ML solutions try to anticipate user’s needs, but we know that our users understand their business needs the best, and that they’re equipped to make the best decisions for themselves even if they've never written a single line of code. Impira allows users to go from being a passenger to being the driver, and we’ve made it easier than ever to drive your business’ data automation process.
Just for fun: Let’s go down an analogy rabbit-hole
Imagine ML models as machinery — literal, physical machinery that’s used by a factory to make any number of products. In this analogy, those products are frozen pizzas.
Company A: Building models from scratch
Company A makes great frozen pizzas and they’ve grown to be a global frozen pizza empire over decades of hard work. Their production process is proprietary and they need custom machinery to make their special pizzas. That’s not a problem because they’ve built an in-house department filled with mechanical engineers, machinists, and tool-and-die makers. These experts have all the necessary equipment to fabricate the right tools the company needs.
They’ve recently introduced a new pizza, and it’s in the shape of a rhombus. Again, this isn’t a problem because their fabricators can build a machine to make rhombus-shaped pizzas. However, developing this machine still takes time and resources for R&D, fabrication, and testing.
Company B: Adopting pre-built models
Company B is a bit newer to the frozen pizza market. They’re doing well and considered to be up-and-comers in the world of pizza, but they’re smaller than Company A and don’t have an in-house fabrication department, so they opt to buy pre-built machines.
These tools are generally suited for Company B’s pizza needs, but need to be tweaked and tested to make sure they can produce Company B’s unique, modern pizzas. Company B has a small in-house team that can do general maintenance and make a few tweaks, but their expertise and resources are limited.
Company B decides to develop a new, ambitious pizza and introduce it to the market next year. It features a triple-stuffed crust. Unfortunately, their current machinery can only handle double-stuffed crusts, so they’ll need to either find a new machine or bring in a fabrication team to retrofit their existing machinery. This is very costly and Company B has to decide whether the success of their new triple-stuffed crust pizza will outweigh the cost of bringing in specialists to retrofit their machinery to produce them.
Company C: Using Impira
Company C came out of left field and is taking the frozen pizza world by storm. They bought a machine that can 3D print any pizza that their pizza scientists come up with. This machine has no preconceived notions of what shape a pizza should be or what type of crust a pizza should have. Instead, it waits on Company C to tell it what they want. It’s also simple and easy to use because the controls are straightforward, and anyone in the company can make adjustments to make sure the pizzas are coming out right with little or no training at all.
Fun and imperfect analogies aside, Impira is an AI/ML solution that a company of any size can utilize to automate nearly any workflow. The team of technologies that power Impira can empower you to take on automation yourself and give yourself the opportunity to offload manual data entry saving your business time and money — letting you and your team get back to doing what you do best.