In artificial intelligence and machine learning, problems can be challenging to solve. However, this is also what makes it so interesting. No problem is too difficult for AI and ML-- there are just different ways to tackle each one that might work better for you in any given circumstance. You must pick the right tool for the job and find the easiest way to achieve your desired outcome. This blog post will look at some of the most common problems you may encounter when working with ML. These are not standalone issues but rather broad cateries with many sub-problems that repeatedly arise when working on AI solutions. Let’s dive in!
Data Scraping and Preparation
Some call data preparation the “gatekeeper” to machine learning since it is an important part of the process. Before you can put data through a machine learning model and put any effort into tuning hyperparameters or developing a functional architecture, you must be able to access the data. When working with real-world data, it rarely is in the format you need it in. This means getting your data where you need it can be a bit of a process. You will likely have to scrape it from a website, write scripts to organize your data, and clean it up. What you need to do will depend on the type of data you’re working with.
This is another reason why it is so important to understand your use case and data-- you need to know what type of data you are looking for and what to do with it once you have it. Once you have your data in the right format and structure, you need to clean it up. This includes removing any erroneous data, unwanted characters, or excessive information. You also need to ensure that any missing data is filled in somehow. You have a couple of options: you could replace the data with a “missing value” or “NULL” or fill the gaps with the average or maximum value in the dataset. You’ll want to ensure you have the right data in the right place before trying to build any models.
Incorrect Or Missing Data
First, let’s talk about how to handle incorrect data. You’ll almost certainly run into some data that is just plain incorrect. Maybe someone input the wrong value, or the data is being scraped incorrectly from a source. One approach to dealing with incorrect data is to remove it from the dataset. This is often the simplest solution and works in many cases. However, there are times when you may want to keep the data around to mark it as incorrect. This could be helpful if you want to account for erroneous data in your model.
You could also use it to inform you of a problem in your data-collection process. You may also want to check to see if there is a missing value in the data. While simply removing the data may be the best solution in some cases, replacing the missing data with the average or maximum value in the dataset would be better. This will ensure that your model is not biased or negatively impacted by the missing data.
Another common problem you might encounter is rigidity in your machine learning model. Your model is not very flexible, making it difficult to change or adapt to new data and circumstances. One way to combat this issue is to choose a model that is more flexible from the start. This means choosing a model that is more generic and not specialized for your particular problem. For example, let’s say you want to predict the weather. You could use a specialized model like a weather model built specifically for this task. Or you could use a more generic model, like a multilayer perceptron, that is more flexible.
This way, you can use the same model for other things, like traffic or how a disease might spread across a population. This will also allow you to change your model as you gather more data and learn more about your problem. This can be a significant factor in many problems-- as you gather more data, you may realize that your model is not as accurate as it could be. You may want to change your model’s architecture, add more data, or replace some of your data with new data to account for any change.
Over-fitting and Model Selection
Another common problem is overfitting. This happens when your model is too specialized for its data. Essentially, your model is trying to account for too many factors in the data, and it is not general enough to apply to new input. Overfitting can be difficult to address, but there are a couple of things you can do. One way is to increase the data you use to train your model. Since your model is too specialized for the data, it will be more generalized by training on more data. Another option is to use a standardization technique.
This will help reduce the variance in your data and make the model less specialized. You can also try to change the architecture of your model. Maybe you want to switch to a recurrent neural network to account for more complex inputs. Or maybe you want to use a wider network with more hidden layers to account for more factors in the data.
Finally, let’s talk about network latency. This is when your model takes too long to produce an output. In many cases, this is a byproduct of having a very large model that takes a long time to run. One solution to this is to break your model up into multiple pieces. You want to split your model into smaller models that are easier to process and run faster. You could also consider using a GPU or other acceleration device to speed up your network.
This will allow you to run your model on a device that is optimized for parallel computation and can run much faster than a CPU. You could also reduce the size of your model by removing unnecessary layers or reducing the amount of data used to train your model. By doing any or all of these, you can reduce the latency of your model and make it more efficient. This can be critical when the timing is important, like in financial trading or autonomous vehicles.
Machine learning is fascinating, but it’s not always easy. It’s common to run into problems and roadblocks when working with ML, but that doesn’t make it less interesting. These challenges can be overcome with the right understanding of the problem you’re solving and the right tools for the job.