What is AI Engineering? Skills, Tools, and Process

Artificial intelligence (AI) is swiftly changing the way industries operate, and AI engineering is at the heart of this transformation. But what exactly is AI engineering? How does it differ from related fields like data science?

And most importantly, how can you prepare for a future in this exciting discipline?

In this guide, we'll share the essentials of AI engineering, from the skills you’ll need to the tools you'll rely on. Read on for a full introduction to this emerging field.

What Is AI engineering?

AI engineering is the process of designing, developing, and deploying AI systems that solve complex problems, improve efficiency, or enhance user experiences. While many see AI as magic, it’s grounded in hard work.

In fact, it actually involves a meticulous engineering effort that combines machine learning (ML), software development, data science, and systems engineering.

AI engineers create intelligent applications like recommendation systems, virtual assistants (Alexa and Siri), fraud detection tools for banks, and even self-driving car systems.

AI engineering requires not just an understanding of AI algorithms but also the ability to bridge theory with practical implementation. This discipline brings AI models out of research labs and integrates them into the real world, where scalability, reliability, and performance are paramount.

Key skills for AI engineers

To succeed as an AI engineer, you’ll need a diverse set of technical and interpersonal skills.

Here are some of the most valuable ones:

1. Strong programming skills 

A solid grasp of programming is essential in AI engineering. Python tops the list due to its robust libraries for AI, such as TensorFlow, PyTorch, and scikit-learn. R, Java, and C++ are also helpful for specific AI applications.

Programming skills are crucial not only for writing code but also for debugging and optimizing algorithms.

(Related reading: programming languages.)

2. Machine learning expertise 

AI engineering is deeply tied to machine learning. Therefore, AI engineers must have a deep understanding of all aspects of machine learning.

This includes:

• Supervised and unsupervised learning
• Neural networks
• Deep learning
• Reinforcement learning

3. Data handling skills 

Since AI models thrive on good quality data, an AI engineer must be proficient in data preprocessing, cleaning, and visualization. They should also be familiar with diverse data storage solutions like SQL, NoSQL databases, and cloud-based data lakes.

4. Mathematics and statistics 

AI engineers use linear algebra, calculus, and statistics to develop and validate models. These mathematical foundations underpin concepts like optimization algorithms and probability distributions.

For example, a linear regression model uses matrix multiplication and optimization techniques to find the best-fit line for a given dataset. Understanding these mathematical concepts helps AI engineers make informed decisions when choosing and designing models.

In addition, knowledge of statistics is essential for evaluating the performance of AI models. It also involves understanding concepts like bias-variance tradeoff and overfitting.

Having knowledge of statistics is crucial in assessing AI models using evaluation metrics like:

• Accuracy
• Precision
• Recall
• F1 score

AI engineers must be comfortable using tools like Python libraries (NumPy, Pandas) and statistical software (R, SAS) to perform statistical analysis.

5. DevOps knowledge 

AI engineers often need to deploy models into production environments. Familiarity with DevOps practices, cloud platforms (e.g., AWS, Google Cloud), and containerization tools like Docker is a big plus.

For example, AI engineers may use DevOps knowledge to package and deploy their models using Docker containers, making it easier for them to be deployed in various environments.

They may also utilize cloud platforms like AWS or Google Cloud to host their data lakes and run their models at scale. This allows for faster processing speeds and the ability to handle larger datasets.

6. Problem-solving and communication 

AI engineers work closely with cross-functional teams and stakeholders. Strong problem-solving abilities, combined with clear communication, help in aligning technical solutions with business needs.

Some people AI engineers work with include:

• Data scientists and data engineers who provide the data needed for training models
• Software developers who incorporate AI into applications and systems
• Product managers who communicate business needs and requirements
• IT specialists who assist with cloud infrastructure and deployment

AI engineers need to be able to explain technical concepts to non-technical stakeholders, as well as actively listen to understand their needs and concerns.

Tools and technologies in AI engineering

AI engineers rely on a wide range of tools to build intelligent systems. Being in a technical role, most AI engineers should have expertise in a wide range of relevant tools.

Here are some of the most vital ones:

• Frameworks for machine learning: TensorFlow, PyTorch, Keras, and scikit-learn provide the building blocks for training and evaluating models.
• Natural language processing (NLP) tools: Tools like SpaCy and Hugging Face are invaluable for working with text data.
• Cloud services: Platforms such as Azure, AWS SageMaker, and Google Vertex AI Platform enable scalable AI development and deployment.
• Big data tools: Apache Hadoop and Apache Spark are essential for processing and analyzing large datasets.
• Version control: Git tools like GitHub and GitLab help AI teams manage code and collaborate efficiently.

An AI engineer must stay updated with evolving tools as the landscape of AI technology changes rapidly.

(Related reading: AI engineer role.)

AI engineering vs. data science

Although similar, AI engineering and data science overlap but differ in scope and focus.

Here's a table summary of the differences:

While a data scientist analyzes raw data, an AI engineer takes insights and weaves them into functional systems.

The AI engineering lifecycle

Building a reliable AI solution isn’t just about choosing the right algorithm. In fact, it involves an entire lifecycle of its own.

The AI engineering lifecycle involves several distinct stages:

Phase 1: Problem definition 

The initial stage involves defining a well-defined and specific problem that the AI solution will aim to solve. This requires a deep understanding of the subject matter, as well as knowledge of available data sources and potential limitations.

AI engineers may collaborate with stakeholders and domain experts to align technical goals with business objectives.

Phase 2: Data collection and preparation 

In this next stage, AI engineers will gather large volumes of reliable, high-quality data. They will then work on cleaning, labeling, and preprocessing the data.

This step involves the use of tools and techniques such as:

• Data wrangling
• Feature engineering
• Exploratory data analysis

The goal is to ensure that the data is in a format that can be easily consumed by AI algorithms.

This process can be the most time-consuming of all due to the importance of having clean and accurate data.

Once the data has been preprocessed, it is then labeled appropriately. Labeling involves assigning categories or tags to the data to make it more understandable for AI algorithms.

For example, if the dataset contains images of different animals, each image would be labeled with the corresponding animal name.

Phase 3: Model development 

Once the data is all prepared and ready, the proposed AI model will begin development. This involves selecting the appropriate algorithms and techniques to train the model on the prepared data. The type of AI model used will depend on the problem at hand and the type of data available.

Some common types of AI models include:

• Supervised learning: This type of model involves feeding labeled data into an algorithm, allowing it to learn how to make predictions or classifications based on certain features.
• Unsupervised learning: With this model, the algorithm is given unlabeled data and tasked with finding patterns or relationships within the data without any guidance.
• Reinforcement learning: In this type of model, an agent learns through interacting with an environment and receiving feedback in the form of rewards or punishments.
• Decision trees: These models are based on a hierarchical structure where decisions are made at each node, leading to a final prediction or classification.
• Random forests: A type of ensemble learning method, random forests combine multiple decision trees to make more accurate predictions. Each tree in the forest is trained on a different subset of the data.
• Support vector machines (SVM): This algorithm involves finding the best boundary or hyperplane that separates different classes in the data. It works well with high-dimensional data and can handle non-linear relationships.
• Neural networks: Inspired by the human brain, neural networks consist of interconnected nodes that process input data and output them as predictions. They can learn and adapt through backpropagation, perfect for complex problems.

AI engineers will experiment with algorithms, hyperparameters, and architectures to ensure the right fit.

Phase 4: Model training and validation 

AI model training is a crucial step in the development of AI systems. It involves feeding the data into the algorithm and allowing it to learn from the data. The process of training involves finding the best parameters or weights for the algorithm that will enable it to make accurate predictions on new, unseen data.

During model training, it is essential to split the available data into two sets: the training set and the validation set. The training set is used to train the model, while the validation set is used to evaluate its performance.

This prevents overfitting, where an AI model becomes too specialized in learning from a particular dataset and fails when given new data.

Some ways to validate data include:

• Cross-validation: This involves dividing the data into k subsets and using each subset to train and evaluate the model multiple times. This helps in reducing the bias towards a particular subset.
• Holdout method: In this method, a portion of the available data is held out as a validation set while the rest is used for training. The model is then evaluated on the validation set.
• Leave-one-out cross-validation (LOOCV): LOOCV involves using all but one data point to train the model and evaluating its performance on the remaining data point. This process is repeated for all data points, and the results are averaged to get an overall performance measure.
• Stratified cross-validation: This method divides the data into k folds while ensuring that each fold contains a similar distribution of target classes as the original dataset. This approach is particularly useful for imbalanced datasets where one class may dominate the others.
• Time series cross-validation: For time series data, it is essential to use a specialized form of cross-validation to account for the temporal nature of the data. In this approach, earlier time periods are used for training, while more recent periods are used for validation.
• Nested cross-validation: In some cases, multiple rounds of cross-validation may be necessary to find an optimal model or hyperparameter values. In nested cross-validation, an outer loop performs k-fold cross-validation on the entire dataset to determine the best model or parameter values, while an inner loop performs k-fold cross-validation on a subset of the data to fine-tune the chosen model.

Phase 5: Deployment 

After training and validation, if an AI model meets desired accuracy levels, it can then be deployed for real-world use. However, AI engineers will have to monitor the model's performance and retrain it with new data periodically.

Once the AI model is deployed, it can be pushed to production. This often involves containerization (e.g., using Docker) and scalability testing.

Challenges in AI engineering

AI engineering is an exciting new field, yet it is highly demanding. Here are some common challenges and how to address them:

• Data quality: Low-quality data can lead to unreliable models. Implement robust data validation and preprocessing pipelines.
• Bias and ethics: AI models may unintentionally learn biases from training data. Careful data auditing and transparent methodologies are needed to prevent such biases.
• Scalability: Prototypes may work well in controlled environments but fail when scaled. Cloud platforms like AWS or Google Cloud can help manage this complexity.
• Keeping pace with innovation: Since AI technology has been evolving rapidly, AI engineers must invest in constant learning and skill development.

Final words

AI engineering is an evolving field that's having a huge impact on many of our lives. The field is advancing rapidly, but that also makes it full of potential for AI engineers. With the right skills and knowledge, you can make an impact in one of the most dynamic professions of our time.