AI​/ML s Tech Managers: Beginner’s Guide

AI/ML Basics for Tech Managers: A Beginner’s Guide

Last updated on August 25th, 2025 at 03:34 pm

For TPMs, SDMs, and CTOs exploring AI/ML strategy without getting lost in the math

In today’s tech landscape, Artificial Intelligence (AI) is an architectural shift that redefines how systems behave and scale. As a technical leader, your job isn’t to build the model yourself, but to understand its constraints, opportunities, and implications to contribute meaningfully to strategy and roadmap decisions. Without a foundational grasp of Machine Learning (ML), you’re flying blind when it comes to trade-offs, technical debt, and roadmap prioritization.

I learned this firsthand while leading engineering and programs in Amazon’s Robotics org, where teams trained convolutional neural networks to understand items through computer vision, enabling robots to handle items, then led Amazon’s social content teams to vet creator content before publication. Two different ML approaches and applications. Despite having no AI/ML background when I joined Amazon, I ramped up quickly—understanding concepts such as supervised learning, classification, regression, model training pipelines, inference latency, and data labeling workflows.

Managers and TPMs supporting ML teams need more than surface-level understanding. Start simple while ramping up on your team’s specific use-cases.

Artificial Intelligence is a broad field focused on building machines or systems that can simulate human intelligence. Machine Learning is a subset of AI that enables machines to learn from data without being explicitly programmed.

This article aims to give you a high-level understanding and introduce foundational ML terms to quickly classify what kind of solution you are assessing from an executive leader/manager/TPM perspective.

Machine Learning is the process of training a model to make useful predictions or generate content from data. A model is a mathematical relationship derived from input data that the ML system uses to make predictions.

In ML, large amounts of data are fed to a model so it learns the mathematical relationships that lead to certain outcomes. Traditional programming uses data and algorithms to produce results; ML creates the algorithms using data and results. Training is required to make predictions.

“Machine Learning is a subfield of computer science that gives computers the ability to learn without being programmed.”

Determine if an ML approach is beneficial before collecting data and training a model. Ask these questions:

• What is the product goal? Specify the real-world outcome you want to achieve (e.g., predict rain, summarize a review, recommend content, generate a logo).
• What kind of solution fits best? Decide between a predictive ML solution, a generative AI solution, or a non-ML solution based on your use-case.
• Can a non-ML benchmark set the bar? Try solving without ML first to set cost, quality, and speed expectations. The ML solution should meet or exceed these.
• Do you have the right data? Ensure the dataset is large, diverse, has high-predictive-power features, correct labels, and features available at prediction time.

If ML is a good fit, focus on:

• Define the outcome
  :
  What should the model do? Examples: generate a business logo, predict weather, detect fraud, or recommend content.
• Identify the model output
  :
  Number, category, natural language, image/video/audio content.
• Understand the problem constraints
  :
  If outcomes vary by thresholds, determine whether thresholds are static or dynamic and ensure labels reflect these thresholds.
• Set success metrics
  :
  Align metrics with business outcomes (e.g., user engagement, efficiency, cost reduction). Differentiate model evaluation metrics from business KPIs.
• Estimate ROI
  :
  Assess whether retraining costs are justified by business impact.

From a leadership perspective, ML teams fall into three broad categories. Based on experience at Amazon, here is a definition: