Understanding AI Trainer Roles

Artificial intelligence (AI) trainers are essential to AI development. They specialize in machine learning (ML), a subset of AI that attempts to replicate human intelligence. Trainers teach ML models how to respond to data sets accurately and effectively. Explore more about AI training, your responsibilities as an AI trainer, and the industry's future. 

What is an AI trainer?

AI trainers teach AI how to interpret user inputs and establish resolutions to inputs. Trainers transform incoherent data into high-quality data sets that allow AI to generate accurate responses. AI trainers ensure AI reaches its full potential and provide the AI with the tools necessary to produce helpful and effective responses to users. 

Responsibilities and tasks

When training AI, you’ll perform tasks such as data preparation, data labeling, and AI performance evaluation to ensure the AI provides accurate outcomes. 

1. Prepare data and evaluate AI performance.

As an AI trainer, you are responsible for preparing training data for ML models to ensure the information provided by the AI is accurate and unbiased. You then show the AI samples of training data and check the AI’s output to determine if it is correct. Afterward, you re-annotate the data if the AI’s output is incorrect. In this role, it's important to consistently assess the effectiveness of AI models by continuously testing AI systems to ensure they’re functioning properly. 

2. Label data sets.

You’re responsible for labeling and structuring data sets when training AI. This data plays a vital role in the development of ML models. The data labels help you determine what data you should pull from and enable the ML model to make accurate predictions about future data sets. 

Required skills and qualifications

Various skills and qualifications, including an educational background in data analytics and ML expertise, are essential for excelling in AI training.

Essential skills

Key skills you’ll use to succeed as an AI trainer include:

• Data analysis: Ability to filter through mass amounts of data

• Technical knowledge: AI, software, and messaging applications expertise 

• Writing skills: Necessary when working with chatbots and virtual agents

• Machine learning: Enables computers to learn from data and make predictions accordingly

• Cloud computing: Interfacing between the user and the cloud system

Educational background

An educational background in data analytics can help you secure a position in AI training. AI training organizations often require a bachelor’s degree in data analytics or data management. If you need educational options other than attending college, you can expand your education with specialization courses and advanced AI courses.

Tools and technologies

You’ll need a variety of tools and technologies to implement AI training. As an AI trainer, you’ll utilize data annotation tools to add informative labels to a data set, making it easier for the ML model to process the data. 

Data annotation tools

Some popular platforms and software utilized for data annotation and training include:

• SuperAnnotate: Object detection, image captioning, question answering, and more

• Label Studio: Image labeling,  customizable labeling interface, and more 

• Amazon SageMaker: SQL analytics, data processing, model development, and more

• Labelbox: Model evaluation, supervised fine-tuning, red teaming, and more

Machine learning frameworks

You can train ML models utilizing natural language processing (NLP) libraries such as Tensorflow or PyTorch. If you want to learn how to train models yourself, TensorFlow’s website offers an in-depth, step-by-step tutorial on implementing ML training.

Model training involves testing a model on various data sets, interpreting the model’s response, and determining what data to change to improve the model’s output. PyTorch implements the same processes on a different interface. They also provide you with a step-by-step tutorial on how to implement ML training. 

Career opportunities

Various career opportunities are available, and more AI training jobs will become prevalent as AI evolves. 

Potential job titles

Some potential job titles in the AI training industry include: 

• Technological AI trainer: Train AI models to maximize model reliability and function. Collaborate with data scientists to improve AI systems and models.

• AI specialist: Create, test, and deploy AI models. Collaborate with data scientists to interpret problems and data for AI systems. 

• UX writer: Evaluate content and make it more readily interpretable. Reduce confusion and improve the user interface. 

• AI chatbot specialist: Create and maintain chatbot systems to enhance chatbot functionality and customer support. Collaborate with teams to align chatbot content. 

Read more: 6 Artificial Intelligence (AI) Jobs to Consider

Industry demand

The US Bureau of Labor Statistics (BLS) predicts that jobs for computer and information research scientists, under which AI trainers fall in, will grow 26 percent in the decade spanning 2023 to 2033 [1]. This faster-than-average growth indicates that the AI industry is in very high demand compared to other industries.  

AI trainers are necessary in various fields, including health care, tech, and finance. In health care, accuracy in AI chatbots is important so patients don’t receive incorrect information or erroneous medical diagnoses. In technological and financial industries, AI trainers are important to maintain accuracy regarding interactions between customers and chatbots, ensuring seamless and beneficial experiences. AI doesn’t always provide accurate outputs, so AI trainers must evaluate and verify each output for quality and accuracy. Subject matter experts are sometimes necessary to provide these verifications.

Challenges in AI training

Training AI can come with various challenges, including: 

• Data quality issues: As an AI trainer, you must determine the reliability of the data to ensure the accuracy of outputs, which requires extensive research of sources.

• Bias in training data: If you were to train the AI on biased data, the AI model would produce biased information. 

• Data security risks: Oftentimes, data within AI models contains sensitive and confidential information, such as financial information, which may be exposed if not encrypted properly. 

Evolving technologies

The tools and technologies necessary to implement AI training also evolve as AI technologies evolve. AI training requires high-performance hardware to handle massive amounts of data and various software tools, frameworks, and systems to implement training tasks. This hardware and software may be costly, so organizations must prepare a budget for all the resources required to properly handle the data within ML models. 

As AI advances in industries such as autonomous driving, health care, and finance, the risk of negative consequences due to improper training increases. It’s crucial for AI trainers to ensure that the decision-making process is seamless so people can trust and effectively oversee AI outcomes. 

Learn more about AI trainer roles with Coursera

AI training is a growing industry that has great potential for the future of ML model development. Explore more about ML and learn how to build and train models with Stanford’s Machine Learning Specialization on Coursera. You could also learn how to leverage NLP libraries for model training and discover more about AI with IBM’s AI Foundations for Everyone Specialization.