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Compare Machine Learning and AI Engineering

This skill explores the distinctions and overlaps between Machine Learning and AI Engineering, emphasizing the different workflows and applications of each. It covers the machine learning pipeline, including data ingestion, preparation, modeling, training, evaluation, and deployment, and highlights the importance of data cleaning. The skill also delves into the role of data science and data analysis in understanding and preparing data for machine learning and AI applications. Additionally, it introduces pseudocode as a tool for expressing system logic before coding, and challenges learners to apply these concepts through practical exercises.

Full skill from AI & Machine Learning. Preview the training IT teams trust.

Taught by Jonathan BarriosData Science, Machine Learning & AI
CBT Nuggets expert instructor

Updated

1h 4m

Skill 2 of 30 in AI & Machine Learning

What you'll learn

  • Compare machine learning and AI engineering workflows, including model building versus deploying and steering a foundation model
  • Identify the stages of a machine learning pipeline, from raw data collection and cleaning through modeling, training, and evaluation
  • Explain how AI engineers use system prompts, RAG, citations, and guardrails to reduce hallucinations and confident misinformation
  • Identify data cleaning issues such as imbalanced classes, inconsistent labels, duplicates, and data leakage between training and testing data
  • Write pseudocode that maps out model logic in plain English before writing framework-specific code
  • Contrast software 1.0, 2.0, and 3.0 approaches to a spam classifier, from deterministic rules to training and prompting

Free 64-minute video lesson with a full transcript and knowledge checks. No account needed to watch.

Introduction

In this skill, we examine the distinction between Machine Learning and AI Engineering as two related but separate ways of working within Artificial Intelligence (AI). We analyze how model training and data-driven prediction differ from building applications on top of existing foundation models, including the role of prompts and external data. We also clarify how data science, data analysis, and data cleaning fit into this space and establish a consistent way to recognize which approach is being used in a given workflow.

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Knowledge Check

Which scenario best represents AI Engineering rather than Machine Learning?

Machine Learning Pipeline Overview

In this video, we examine the Machine Learning Pipeline as a high-level view of how ML systems are structured. We understand how workflow steps like data ingestion, preparation, modeling, training, evaluation, and deployment each connect. We then analyze how each stage influences the others and why it's important to handle each with precision.

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Knowledge Check

What is a key reason "Evaluation" is required in a Machine Learning Pipeline?

Machine Learning vs AI Engineering Workflows

In this video, we examine how Machine Learning and AI Engineering approach problems. We clarify the distinction between training models to learn from data and building systems that guide the behavior of existing foundation models. We analyze how these different approaches require different workflows, from model training and evaluation to application design and system behavior.

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Knowledge Check

What is a key difference between Machine learning and AI engineering?

Machine Learning and AI Engineering in Practice

In this video, we further define machine learning and take a closer look at why and how it is different from AI Engineering. Using various examples, we dive deeper into how ML learns patterns from data and how AI Engineering builds systems around model behavior. We analyze the way each of these approaches can be used to solve different problems.

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Knowledge Check

Which task is most closely associated with AI Engineering rather than Machine Learning?

Data Analysis, Data Science, and Data Cleaning

In this video, we examine how data analysis and data science fit into the broader Machine Learning and AI landscape. We clarify the distinction between exploring and understanding data, designing experiments, and building models, and when each approach can be used correctly. We analyze why well-defined data is critical and how issues in the data always affect everything that follows.

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Knowledge Check

What is a critical first step in ensuring the success of Machine Learning Models?

Pseudocode Crash Course

In this video, we examine "Pseudocode" as a way to express system logic in plain language before writing traditional code. We clarify how pseudocode applies across rule-based systems, machine learning workflows, and AI engineering systems. We analyze how breaking a system into inputs, decisions, and outputs is best practice for structured thinking before implementation of code generally.

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Knowledge Check

Pseudocode removes the need to understand how a system works because the code handles the logic automatically.

Challenge 🎉

Congrats on making it to the challenge. In this challenge, we apply pseudocode to a single problem by solving it using three different approaches: rule-based logic, ML, and GenAI. The task focuses on classifying emails as spam or not spam while comparing how the logic and workflow change across each approach. Complete the pseudocode for each system and answer the question below before moving onto the next skill.

Colab Notebook:

Pseudocode Challenge

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Knowledge Check

Machine Learning is always the best solution when working with all data-driven problems.

Frequently asked questions

What is the difference between machine learning and AI engineering?

Machine learning focuses on building a model: training data and labels, model improvements, evaluation metrics, and inference or predictions. AI engineering focuses on deploying and steering a foundation model that has already been built, with attention to monitoring, system behavior, predictable output, and integration into a website, assistant, or internal tool. This lesson walks a team member through both workflows side by side so the team shares one mental model of where each discipline starts.

What are the stages of a machine learning pipeline taught in this lesson?

The lesson traces the pipeline from raw data collection and cleaning through modeling, training, and evaluation, using the earlier hamburger and hot dog binary classifier as a simplified example. It explains that early focus in the course stays on data preparation, modeling, training, and evaluation to build foundational intuition before frameworks like PyTorch and TensorFlow, while AI engineering work centers on deployment, monitoring, and system behavior.

How do AI engineers reduce hallucinations in an internal workplace assistant?

The lesson explains how system prompts, RAG, citations, and guardrails work together: citations show the model is referencing actual policy or documentation, and guardrails let the system gracefully manage cases where the model does not behave as expected. For an IT Director planning internal AI tools, this gives the team a shared approach to avoiding confident misinformation.

What data cleaning problems should a team watch for before training a model?

The lesson covers imbalanced classes, inconsistent labels such as spam written in different cases or labeled as junk, duplicate records from export problems, and data leakage between training and testing data. The trainer uses examples like 1,000 hot dog images versus 100 hamburger images to show how an imbalanced dataset skews predictions.

Why does this lesson use pseudocode and a spam classifier before framework code?

Writing model logic in plain English first lets a learner separate logic from syntax, which changes from framework to framework, so they can troubleshoot when performance is closer to 50 percent than 85 percent. The spam classifier challenge contrasts software 1.0 deterministic rules, software 2.0 training on labeled emails, and software 3.0 steering a model through prompting, including a bonus system prompt example.

How long is this lesson and who on an IT team is it designed for?

This lesson runs 1 hour 4 minutes and is part of the 26.9-hour beginner course Introduction to Machine Learning and AI Engineering. The course is built for programmers, developers, data analysts, IT practitioners, and DevOps engineers with basic coding experience, and Training Managers can assign it as a foundation before approving production AI work.

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