Developing an artificial intelligence or machine language model is not so easy. It requires a lot of skills and knowledge and advanced and enriched experience to make sure it works successfully in various scenarios. In addition to it, you may also need high-quality training data, especially while training your visualperception-based AI models. The most critical stage in ML development is collecting training data and using it to train the ML model effectively. Any possible mistakes while acquiring data and training the model may make it perform inaccurately.Sometimes it could also be highly disastrous while making a compromised model to make business decisions, especially in critical areas of healthcare, finance, autonomous vehicles, etc.
While training the machine language for AI models, multistage activities needed to be performed for utilizing the training data in the best possible manner. It is also important to ensure that the outcome is satisfying on multiple trials at various settings.This article will discuss some common mistakes one should understand and avoid beforehand to make sure that your machine learning-based AI model is successful.
Top mistakes to avoid in ML-based AI
Using unstructured and unverified data
Unfortunately, the use of unstructured data is one of the most common mistakes ML engineers tendto make in AI model development. Some of the most significant errors in this area are like.
- Conflicting data
- Duplication of data
- Improper categorization of data
- Errors in data and other issues will end up in anomalies during the training.
So, before you use the data for machine learning training, examine the raw data sets and eliminate any unwanted or irrelevant information by helping your AI model work with optimum accuracy. For effective incorporation of automated models in enterprise database administration, you may check out the services of RemoteDBA.
Using already used data for testing your model
When you are building a fresh machine learning model, you should avoid using data that had already been used to test another or the same model before.This mistake must be avoided at any cost. For example, suppose someone has already learned something using the particular set of data and applied the knowledge to their work area. In that case, using the same learning on a different area of work may lead to one being biased and cause repetitive inferencing. The same logic applies in machine learning too. For example, AI may learn with the bulk of a dataset for predicting the answers accurately. Therefore, using the same set of training data on AI-based applications may make model biased and derive results from previous learning. Hence, while we test the capabilities of an AI model, it is very important to test it using the new data set only, which is not used earlier for any other machine learning training.
Using insufficient data sets
To make any AI model successful, you have to use sufficient training data so that it will be able to predict the output with higher accuracy. Lack of sufficient data for training the AI model is the primary reason behind the failure of many models. Based on the type of AI models for the industries in which it is being used, the fields of the training data requirement may be varied. For example, you may need to have more quantitative and qualitative data sets to ensure it works with high precision for deep learning purposes.
Ensure that your machine learning model is unbiased
It may not be possible to develop a machine learning model which can give you 100% accurate results in varying settings. Like humans, machines also may be biased due to different factors like gender, age, income level, orientation, etc. This can surely affect the output of the AI models in one way or another. So, you have to minimize this by using statistical analysis methods to find each variable factor and how it affects the data during the AI training process.
We can see Amazon’s hiring algorithm as one of the examples of an unbiased machine learning model. In 2015, Amazon started to fine-tune its algorithm for hiring employees as they found it biased against recruiting women employees. The reason behind this was the algorithm was based on many resumes that were submitted over the previous 10 years, and since most of them were male applicants, the algorithm got favored more men over women. Later, this was restructured by Amazon to become an equal opportunity employer.
Relying on the AI model and learning independently
Even though you need some experts to train the AI model, using a huge volume of training datasets is needed. However, AI is using repetitive machine learning process which has to be considered while training such models. Therefore, machine learning engineers need to ensure that the AI model is learning by using the data along with the right strategy. To ensure this factor, you should frequently check the AI training process and the results at regular intervals to ensure the best possible outcomes.
However, while developing machine-learning artificial intelligence models, one should also keep asking themselves some important questions like whether the data source is from a test source? Does the AI model you developed cover wider demographics, and is there anything else affecting the results?
Not using properlylabeled datasets
To achieve a winning streak in developing a machine-learning-based AI model, you must derive a well-define strategy to run it. It will help you get the best outcome from your AI model, but it will also ensure your machine learning models are more reliable among the users. The points mentioned above are the key points you have to keep in mind while training the AI models. Training the data accurately with the highest level of precision is the most critical factor in AI. It is also important to make the AI work successfully and with the best accuracy in different settings. If the data is not labeled properly, it will surely affect the performance of your AI model adversely.
If the machine learning model you developed is computer-vision oriented, you may use precise techniques like image annotation to get the right set of training data.Getting properly labeled data sets is a big challenge for all the AI companies while training their models. But there are a lot of third-party companies offering data labeling services for machine learning AI. You need to do thorough research to identify reliable services in data labeling and get your data sets properly labeled according to the AI model’s requirement.