Foundation Models and Application Building
Model development is the layer most associated with traditional ML engineering and has three responsibilities: modeling and training, dataset engineering, and inference optimization.
Two Builders, Two Different Workdays
Imagine two people who both want to ship an AI-powered product. The first has spent years studying how learning algorithms are designed and how they are adjusted until they perform well. The second has never studied those topics, but knows how to connect a capable pre-built model to a product that users can actually touch. Both are doing AI work, yet their daily tasks look very different.
The difference between these two builders is tied to a layer of the AI stack called model development. Understanding what belongs in that layer, and what does not, shows you where specialized machine learning knowledge is genuinely needed and where it is optional. Before reading on, pause and ask yourself which of the two builders is doing work that involves creating a model, and which is doing work that involves using one.
Where Model Development Sits in the Stack
Model development is the layer most often linked with traditional machine learning engineering. It is the place where models themselves are created and shaped, as opposed to the layers where existing models are wired into applications.
Model development carries three main responsibilities. The first is modeling and training, which covers designing a model, teaching it from data, and refining it afterward. The second is dataset engineering, which concerns the data that feeds learning. The third is inference optimization, which concerns making a trained model run efficiently when it is used to produce predictions or outputs.
A useful way to hold these three responsibilities in mind is to picture a tree. Model development is the trunk, and its three responsibilities are the main branches. Modeling and training is the branch with the most internal structure, which is why it receives the detailed treatment in the rest of this article. The diagram below shows how the branches relate to one another.
Modeling and Training as a Sequence of States
Modeling and training is defined as the process of coming up with a model architecture, training it, and fine-tuning it. Those three verbs describe a sequence of states that a model passes through. The key idea is that the model changes at each step. Before any work begins, there is no model at all. Once a builder settles on an architecture, there is a model structure that has not yet learned anything from data. Training then adjusts that structure so it reflects patterns in the data. Fine-tuning comes after this and refines the already-trained model further.
Notice that the three steps are not three unrelated tasks. Each stage changes what the model is and how well it does its job. That is why the source treats them as one connected process. Choosing an architecture decides what the model can represent, training fits it to data, and fine-tuning adjusts its behavior again after the first training has happened.
Following One Model Through Its States
Tracking a single model across its lifecycle
A team has a model that starts as a design choice. How does its status change as it moves through modeling and training, and why do the stages count as one process?
- 1Stage one: the model exists only as a design choice
At this point the builder has selected an architecture. The structure exists on paper, but it has not absorbed any training data, so it cannot yet make useful predictions.
- 2Stage two: the model has absorbed training
After training, the same model can make predictions, though perhaps imperfectly. The architecture is unchanged in kind, but its internal state now reflects patterns from the data.
- 3Stage three: the model has been fine-tuned
Fine-tuning adjusts the trained model again. Its behavior changes, so the model that users eventually receive is not the same as the one that finished initial training.
- 4Why the stages form one process
Each stage builds on the state left by the previous one. Skipping or reordering them would produce a different model, which is why they are treated as a single connected sequence.
The same model passes through design, training, and fine-tuning. Each step changes what the model is and how well it performs, so the three steps belong to one modeling and training process.
What Makes Modeling and Training Demanding
Doing modeling and training well demands more than following steps. It requires understanding how a model learns in the first place. Practitioners need to know about concepts such as gradient descent, loss functions, and regularization. The source explains that these ideas describe how training nudges a model toward better behavior and how that process can be controlled.
This is the reason modeling and training is the branch of model development that carries the most internal structure. Following the steps tells you what to do. Understanding the learning process tells you why a step works, and what to adjust when it does not.
Algorithms Versus Architectures
Developing ML models calls for specialized knowledge, and the source identifies two broad areas. One is knowing which families of ML algorithms exist and what each is for. The other is knowing which neural network architectures exist and how they are organized. These two areas are related but distinct, and both feed into modeling and training along with the understanding of how learning happens.
Tools That Carry Out the Work
Modeling and training work is carried out with dedicated software tools. The source names three: Google's TensorFlow, Hugging Face's Transformers, and Meta's PyTorch. These belong to the same category of tooling used to design, train, and fine-tune models, so a builder moving between them is still working within the modeling and training responsibility.
When Foundation Models Remove the Need for Deep ML Skill
Foundation models are pre-trained models that can be used as the base for building AI applications. Because someone has already performed the heavy modeling and training work, an application builder can start from a model that already exists. The source notes that with the availability of foundation models, ML knowledge is no longer a must-have for building AI applications.
This changes who can take part in AI application work. A builder who never intends to design an architecture or study gradient descent can still deliver a working product by relying on a foundation model. The source describes meeting many successful application builders who have no interest in those topics.
ML knowledge remains valuable even when it is not required. The source gives two reasons. First, it expands the set of tools a builder can use. Second, it helps with troubleshooting when a model does not behave as expected. A builder who understands the underlying learning process can diagnose problems that a purely hands-off builder may find opaque.
Mistakes Beginners Make About Model Development
Common mistake Treating model development and application development as the same work
What it looks likeAssuming that wiring a pre-built model into a product involves designing its architecture.
Why it failsModel development is the layer where models are created and shaped. Application work connects existing models to products.
Do this insteadAsk whether the task creates or shapes a model, or whether it uses a model that already exists.
Common mistake Thinking training is the final step
What it looks likeStopping once a model has finished training and assuming its behavior is settled.
Why it failsThe source describes fine-tuning as a further stage that refines the already-trained model.
Do this insteadThink of modeling and training as architecture, then training, then fine-tuning.
Common mistake Using ML algorithms and neural network architectures as if they were the same thing
Why it failsThe source names them as two broad areas of modeling knowledge, each with its own content.
Do this insteadKeep the two areas separate when deciding what you need to learn.
Common mistake Assuming ML knowledge is either fully required or completely useless
Why it failsThe source says it is no longer a must-have for building AI applications, but it remains valuable for expanding tools and troubleshooting.
Checking Your Understanding of the Three Responsibilities
A team is deciding which part of model development a new task belongs to. For each task, name the responsibility it falls under: modeling and training, dataset engineering, or inference optimization. Task A: deciding on a neural network architecture for a new problem. Task B: making a trained model produce outputs quickly when it is used. Task C: preparing the data that will feed learning.
Hints
- Modeling and training covers designing a model, teaching it from data, and refining it afterward.
- Dataset engineering concerns the data that feeds learning.
- Inference optimization concerns running a trained model efficiently when it is used.
Key Takeaways
- Model development is the layer most associated with traditional ML engineering, and it has three responsibilities: modeling and training, dataset engineering, and inference optimization.
- Modeling and training is the process of coming up with a model architecture, training it, and fine-tuning it. Each stage changes what the model is and how well it performs.
- ML algorithms and neural network architectures are two separate areas of modeling knowledge, and both support modeling and training.
- Foundation models mean ML knowledge is no longer a must-have for building AI applications, although it remains valuable for expanding tools and troubleshooting problems.