What Is Training Data in Machine Learning:
Definition, Benefits, Challenges, Example & Datasets
The Ultimate Buyers Guide 2023
In the world of artificial intelligence and machine learning, data training is inevitable. This is the process that makes machine learning modules accurate, efficient and fully functional. In this post, we explore in detail what AI training data is, training data quality, data collection & licensing and more.
It is estimated that on average adult makes decisions on life and everyday things based on past learning. These, in turn, come from life experiences shaped by situations and people. In the literal sense, situations, instances, and people are nothing but data that gets fed into our minds. As we accumulate years of data in the form of experience, the human mind tends to make seamless decisions.
What does this convey? That data is inevitable in learning.
Similar to how a child needs a label called an alphabet to understand the letters A, B, C, D a machine also needs to understand the data it is receiving.
That’s exactly what Artificial Intelligence (AI) training is all about. A machine is no different than a child who has yet to learn things from what they are about to be taught. The machine does not know to differentiate between a cat and a dog or a bus and a car because they haven’t yet experienced those items or been taught what they look like.
So, for someone building a self-driving car, the primary function that needs to be added is the system’s ability to understand all the everyday elements the car may encounter, so the vehicle can identify them and make appropriate driving decisions. This is where AI training data comes into play.
Today, artificial intelligence modules offer us many conveniences in the form of recommendation engines, navigation, automation, and more. All of that happens due to AI data training that was used to train the algorithms while they were built.
AI training data is a fundamental process in building machine learning and AI algorithms. If you are developing an app that is based on these tech concepts, you need to train your systems to understand data elements for optimized processing. Without training, your AI model will be inefficient, flawed and potentially pointless.
It is estimated that Data Scientists spend more than 80% of their time in Data Preparation & Enrichment in order to train ML models.
So, for those of you looking to get funding from venture capitalists, the solopreneurs out there who are working on ambitious projects, and tech enthusiasts who are just getting started with advanced AI, we have developed this guide to help answer the most important questions regarding your AI training data.
Here we will explore what AI training data is, why is it inevitable in your process, the volume and quality of data you actually need, and more.
What is AI Training Data?
AI training data is carefully curated and cleaned information that is fed into a system for training purposes. This process makes or breaks an AI model’s success. It can help in developing the understanding that not all four-legged animals in an image are dogs or it could help a model differentiate between angry yelling and joyous laughter. It is the first stage in building artificial intelligence modules that require spoon-feeding data to teach machines the basics and enable them to learn as more data is fed. This, again, makes way for an efficient module that churns out precise results to end users.
Consider an AI training data process as a practice session for a musician, where the more they practice, the better they get at a song or a scale. The only difference here is that machines have to also first be taught what a musical instrument is. Similar to the musician who makes good use of the countless hours spent on practice on stage, an AI model offers an optimum experience to consumers when deployed.
Why is AI Training Data Required?
The simplest answer to why AI training data is required for a model’s development is that without it machines wouldn’t even know what to comprehend in the first place. Like an individual trained for their particular job, a machine needs a corpus of information to serve a specific purpose and deliver corresponding results, as well.
Let’s consider the example of autonomous cars again. Terabytes after terabytes of data in a self-driving vehicle comes from multiple sensors, computer vision devices, RADAR, LIDARs and much more. All these massive chunks of data would be pointless if the central processing system of the car does not know what to do with it.
For instance, the computer vision unit of the car could be spewing volumes of data on road elements such as pedestrians, animals, potholes and more. If the machine learning module is not trained to identify them, the vehicle would not know that they are hindrances that could cause accidents if encountered. That’s why the modules have to be trained on what every single element in the road is and how different driving decisions are required for each one.
While this is just for visual elements, the car should also be able to understand human instructions through Natural Language Processing (NLP) and audio or speech collection and respond accordingly. For instance, if the driver commands the in-car infotainment system to look for gas stations nearby, it should be able to understand the requirement and throw appropriate results. For that, however, it should be able to understand every single word in the phrase, connect them and be able to understand the question.
While you could wonder if the process of AI training data is complex only because it is deployed for a heavy use case such as an autonomous car, the fact is even the next movie Netflix recommends goes through the same process to offer you personalized suggestions. Any app, platform or an entity that has AI associated with it is by default powered by AI training data.
What types of data do I need?
There are 4 primary types of data that would be needed i.e., Image, Video, Audio/Speech or Text in order to effectively train machine learning models. The type of data needed would be dependent on a variety of factors such as the use case in hand, the complexity of models to be trained, the training method used, and the diversity of input data required.
How much Data is Adequate?
They say there is no end to learning and this phrase is ideal in the AI training data spectrum. The more the data, the better the results. However, a response as vague as this is not enough to convince anyone who is looking to launch an AI-powered app. But the reality is that there is no general rule of thumb, a formula, an index or a measurement of the exact volume of data one needs to train their AI data sets.
A machine learning expert would comically reveal that a separate algorithm or module has to be built to deduce the volume of data required for a project. That’s sadly the reality as well.
Now, there is a reason why it is extremely difficult to put a cap on the volume of data required for AI training. This is because of the complexities involved in the training process itself. An AI module comprises several layers of interconnected and overlapping fragments that influence and complement each other’s processes.
For instance, let’s consider you are developing a simple app to recognize a coconut tree. From the outlook, it sounds rather simple, right? From an AI perspective, however, it is much more complex.
At the very start, the machine is empty. It does not know what a tree is in the first place let alone a tall, region-specific, tropical fruit-bearing tree. For that, the model needs to be trained on what a tree is, how to differentiate from other tall and slender objects that may appear in frame like streetlights or electric poles and then move on to teach it the nuances of a coconut tree. Once the machine learning module has learnt what a coconut tree is, one could safely assume it knows how to recognize one.
But only when you feed an image of a banyan tree, you would realize that the system has misidentified a banyan tree for a coconut tree. For a system, anything that is tall with clustered foliage is a coconut tree. To eliminate this, the system needs to now understand every single tree that is not a coconut tree to identify precisely. If this is the process for a simple unidirectional app with just one outcome, we can only imagine the complexities involved in apps that are developed for healthcare, finance and more.
Apart from this, what also influences the amount of data required for training includes aspects listed below:
- Training method, where the differences in data types (structured and unstructured) influence the need for volumes of data
- Data labeling or annotation techniques
- The way data is fed to a system
- Error tolerance quotient, which simply means the percentage of errors that is negligible in your niche or domain
Real-world Examples of Training Volumes
Though the amount of data you need to train your modules depends on your project and the other factors we discussed earlier, a little inspiration or reference would help get an extensive idea on data requirements.
The following are real-world examples of the amount of datasets used for AI training purposes by diverse companies and businesses.
- Facial recognition – a sample size of over 450,000 facial images
- Image annotation – a sample size of over 185,000 images with close to 650,000 annotated objects
- Facebook sentiment analysis – a sample size of over 9,000 comments and 62,000 posts
- Chatbot training – a sample size of over 200,000 questions with over 2 million answers
- Translation app – a sample size of over 300,000 audio or speech collection from non-native speakers
What if I don’t have enough data?
In the world of AI & ML, data training is inevitable. It is rightly said that there is no end to learning new things and this holds true when we talk about the AI training data spectrum. The more the data, the better the results. However, there are instances where the use case you are trying to resolve pertains to a niche category, and sourcing the right dataset in itself is a challenge. So in this scenario, if you do not have adequate data, the predictions from the ML model may not be accurate or may be biased. There are ways such as data augmentation and data markup that can help you overcome the shortcomings however the result may still not be accurate or reliable.
How do you improve Data Quality?
The quality of data is directly proportional to the quality of output. That’s why highly accurate models require high quality datasets for training. However, there is a catch. For a concept that is reliant on precision and accuracy, the concept of quality is often rather vague.
High-quality data sounds strong and credible but what does it actually mean?
What is quality in the first place?
Well, like the very data we feed into our systems, quality has a lot of factors and parameters associated with it as well. If you reach out to AI experts or machine learning veterans, they might share any permutation of high-quality data is anything that is –
- Uniform – data that is sourced from one particular source or uniformity in datasets that are sourced from multiple sources
- Comprehensive – data that covers all possible scenarios your system is intended to work on
- Consistent – every single byte of data is similar in nature
- Relevant – the data you source and feed is similar to your requirements and expected outcomes and
- Diverse – you have a combination of all types of data such as audio, video, image, text and more
Now that we understand what quality in data quality means, let’s quickly look at the different ways we could ensure quality data collection and generation.
1. Look out for structured and unstructured data. The former is easily understandable by machines because they have annotated elements and metadata. The latter, however, is still raw with no valuable information a system can make use of. This is where data annotation come in.
2. Eliminating bias is another way to ensure quality data as the system removes any prejudice from the system and delivers an objective result. Bias only skews your results and makes it futile.
3. Clean data extensively as this will invariably increase the quality of your outputs. Any data scientist would tell you that a major portion of their job role is to clean data. When you clean your data, you are removing duplicate, noise, missing values, structural errors etc.
What affects training data quality?
There are three main factors that can help you predict the level of quality you desire for your AI/ML Models. The 3 key factors are People, Process and Platform that can make or break your AI Project.
Platform: A complete human-in-the-loop proprietary platform is required to source, transcribe and annotate diverse datasets for successfully deploying the most demanding AI and ML initiatives. The platform is also responsible to manage workers, and maximize quality and throughput
People: To make AI think smarter takes people who are some of the smartest minds in the industry. In order to scale you need thousands of these professionals throughout the world to transcriber, label, and annotate all data types.
Process: Delivering gold-standard data that is consistent, complete, and accurate is complex work. But it’s what you will always need to deliver, so as to adhere to the highest quality standards as well as stringent and proven quality controls and checkpoints.
Where do you source AI Training Data from?
Unlike our previous section, we have a very precise insight here. For those of you looking to source data
or if you are in the process of video collection, image collection, text collection and more, there are three
primary avenues you can source your data from.
Let’s explore them individually.
Free sources are avenues that are involuntary repositories of massive volumes of data. It is data that is simply lying there on the surface for free. Some of the free resources include –
- Google datasets, where over 250 million sets of data were released in 2020
- Forums like Reddit, Quora and more, which are resourceful sources for data. Besides, data science and AI communities in these forums could also help you with particular data sets when reached out.
- Kaggle is another free source where you can find machine learning resources apart from free data sets.
- We have also listed free open datasets to get you started with training your AI models
While these avenues are free, what you would end up spending are time and effort. Data from free sources is all over the place and you have to put in hours of work into sourcing, cleaning and tailoring it to suit your needs.
One of the other important pointers to remember is that some of the data from free sources cannot be used for commercial purposes as well. It requires data licencing.
Like the name suggests, data scraping is the process of mining data from multiple sources using appropriate tools. From websites, public portals, profiles, journals, documents and more, tools can scrape data you need and get them to your database seamlessly.
While this sounds like an ideal solution, data scraping is legal only when it comes to personal use. If you are a company looking to scrape data with commercial ambitions involved, it gets tricky and even illegal. That’s why you need a legal team to look into websites, compliance and conditions before you could scrape data you need.
As far as data collection for AI training data is concerned, outsourcing or reaching out to external vendors for datasets is the most ideal option. They take the responsibility of finding datasets for your requirements while you can focus on building your modules. This is specifically because of the following reasons –
- you don’t have to spend hours looking for avenues of data
- there is no efforts in terms of data cleansing and classification involved
- you get in hand quality data sets that precisely check off all the factors we discussed some time back
- you can get datasets that are tailored for your needs
- you could demand the volume of data you need for your project and more
- and the most important, they also ensure that their data collection and the data itself complies to local regulatory guidelines.
The only factor that could prove to be a shortcoming depending on your scale of operations is that outsourcing involves expenses. Again, what doesn’t involve expenses.
Shaip already is a leader in data collection services and has its own repository of healthcare data and speech/audio datasets that can be licensed for your ambitious AI projects.
Open Datasets – To use or not to use?
Open datasets are publicly available datasets that can be used for machine learning projects. It does not matter if you need audio, video, image, or text-based dataset, there are open datasets available for all forms and classes of data.
For instance, there is the Amazon product reviews dataset that features over 142 million user reviews from 1996 to 2014. For images, you have an excellent resource like Google Open Images, where you can source datasets from over 9 million pictures. Google also has a wing called Machine Perception that offers close to 2 million audio clips that are of ten seconds duration.
Despite the availability of these resources (and others), the important factor that is often overlooked is the conditions that come with their usage. They are public for sure but there is a thin line between breach and fair use. Each resource comes with its own condition and if you are exploring these options, we suggest caution. This is because in the pretext of preferring free avenues, you could end up incurring lawsuits and allied expenses.
The True Costs of AI Training Data
Only the money that you spend to procure the data or generate data in-house is not what you should consider. We must consider linear elements like time and efforts spent in developing AI systems and cost from a transactional perspective. fails to compliment the other.
Time Spent on Sourcing and Annotating Data
Factors like geography, market demographics, and competition within your niche hinder the availability of relevant datasets. The time spent manually searching for data is time-wasting in training your AI system. Once you manage to source your data, you will further delay training by spending time annotating the data so your machine can understand what it is being fed.
The Price of Collecting and Annotating Data
Overhead expenses (In-house data collectors, Annotators, Maintaining equipment, Tech infrastructure, Subscriptions to SaaS tools, Development of proprietary applications) are required to be calculate while sourcing AI data
The Cost of Bad Data
Bad data can cost your company team morale, your competitive edge, and other tangible consequences that go unnoticed. We define bad data as any dataset that is unclean, raw, irrelevant, outdated, inaccurate, or full of spelling errors. Bad data can spoil your AI model by introducing bias and corrupting your algorithms with skewed results.
All costs involving the administration of your organization or enterprise, tangibles, and intangibles constitute management expenses which are quite often the most expensive.
What next after Data Sourcing?
Once you have the dataset in your hand, the next step is to annotate or label it. After all the complex tasks, what you have is clean raw data. The machine still cannot understand the data you have because it is not annotated. This is where the remaining portion of the real challenge starts.
Like we mentioned, a machine needs data in a format that it can understand. This is exactly what data annotation does. It takes raw data and adds layers of labels and tags to help a module understand every single element in the data accurately.
For instance, in a text, data labeling will tell an AI system the grammatical syntax, parts of speech, prepositions, punctuations, emotion, sentiment and other parameters involved in machine comprehension. This is how chatbots understand human conversations better and only when they do that they can mimic human interactions better through their responses as well.
As inevitable as it sounds, it is also extremely time-consuming and tedious. Regardless of the scale of your business or its ambitions, the time taken to annotate data is huge.
This is primarily because your existing workforce needs to dedicate time out of their everyday schedule to annotate data if you don’t have data annotation specialists. So, you need to summon your team members and assign this as an additional task. The more it gets delayed, the longer it takes to train your AI models.
Though there are free tools for data annotation, that does not take away the fact that this process is time consuming.
That’s where data annotation vendors like Shaip come in. They bring in a dedicated team of data annotation specialists with them to only focus on your project. They offer you solutions in the way you want for your needs and requirements. Besides, you can set a timeframe with them and demand work to be completed in that specific timeline.
One of the major benefits is in the fact that your in-house team members can continue to focus on what matters more for your operations and project while experts do their job of annotating and labeling data for you.
With outsourcing, optimum quality, minimal time and maximum precision can be ensured.
That was everything on AI training data. From understanding what training data is to exploring free resources and benefits of data annotation outsourcing, we discussed them all. Once again, protocols and policies are still flaky in this spectrum and we always recommend you getting in touch with AI training data experts like us for your needs.
From sourcing, de-identifying to data annotation, we would assist you with all your needs so you can only work on building your platform. We understand the intricacies involved in data sourcing and labeling. That’s why we reiterate the fact that you could leave the difficult tasks to us and make use of our solutions.
Reach out to us for all your data annotation needs today.
Frequently Asked Questions (FAQ)
If you want to create intelligent systems, you need to feed in cleaned, curated, and actionable information for facilitating supervised learning. The labeled information is termed AI training data and comprises market metadata, ML algorithms, and anything that helps with decision making.
Every AI-powered machine has capabilities restricted by its historical stead. This means the machine can only predict the desired outcome if it has been trained previously with comparable data sets. Training data helps with supervised training with the volume directly proportional to the efficiency and accuracy of the AI models.
Disparate training datasets are necessary to train specific Machine Learning algorithms, for helping the AI-powered setups take important decisions with the contexts in mind. For instance, if you plan on adding Computer Vision functionality to a machine, the models need to be trained with annotated images and more market datasets. Similarly, for NLP prowess, large volumes of speech collection act as training data.
There is no upper limit to the volume of training data required to train a competent AI model. Larger the data volume better will be the model’s ability to identify and segregate elements, texts, and contexts.
While there is a lot of data available, not every chunk is suitable for training models. For an algorithm to work at its best, you would need comprehensive, consistent, and relevant data sets, which are uniformly extracted but still diverse enough to cover a wide range of scenarios. Regardless of the data, you plan on using, it is better to clean and annotate the same to improved learning.
If you have a particular AI model in mind but the training data is not quite enough, you must first remove outliers, pair in transfer and iterative learning setups, restrict functionalities, and make the setup open-source for the users to keep adding data for training the machine, progressively, in time. You can even follow approaches concerning data augmentation and transfer learning to make the most of restricted datasets.
Open datasets can always be used for gathering training data. However, if you seek exclusivity for training the models better you can rely on external vendors, free sources like Reddit, Kaggle, and more, and even Data Scraping for selectively mining insights from profiles, portals, and documents. Regardless of the approach, it is necessary to format, reduce, and clean the procured data before using.