Mariogerard
AI/ML Basics for Tech Managers: A Beginner’s Guide
Mariogerard, Snowflake, Arizona, United States, 85937
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. Introduction to ML
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.” Is ML the Right Tool for the Job?
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. Framing the ML Problem Like a Leader
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.
Engineering Paths in ML
From a leadership perspective, ML teams fall into three broad categories. Based on experience at Amazon, here is a definition: Infrastructure/Core Platforms : Build scalable data ingestion, training, deployment, and monitoring services (e.g., SageMaker, Vertex AI). Key trait: heavy engineering, lighter ML; the product is the infrastructure. ML Operations (MLOps) : Implement and manage ML tools for real-world use cases; handle data ingestion, labeling, deployment, and monitoring of models. Key trait: engineering-heavy, deeply tied to ML use case. Integrated AI/ML Engineering : Embedded engineers who productionize models, run experiments, and fine-tune models. Key trait: high collaboration with scientists and deeper ML literacy. Regardless of category, every ML-owning team touches MLOps. Many teams use cloud-native ML tools or GenAI services. Understanding your category helps architectural, resourcing, and roadmap decisions. Further detail will come in future posts. Behind the Scenes: ML Data Pipelines
Data pipelines for training and inference are essential and should be prioritized early. Start simple with a few features and scale if justified. A typical ML pipeline spans data ingestion, cleaning, transformation, feature engineering, storage, and delivery to training environments, often requiring orchestration across batch and real-time systems. Common tools include Apache Kafka or Kinesis, Airflow or Prefect, Spark or Pandas, and storage like S3, Snowflake, or BigQuery. Data cleaning, labeling, and handling schema changes are frequently underestimated. Design with observability, reusability, and fault tolerance from day one to support ongoing model trustworthiness. Below is a high-level view of tool categories and example tools (non-exhaustive): Category
AWS
Azure
Google Cloud
OCI ETL / Orchestration
AWS Glue, Step Functions; Azure Data Factory; Cloud Dataflow; Cloud Composer; OCI Data Integration ML Workflow Management
Amazon SageMaker Pipelines; Azure ML Pipelines; Vertex AI Pipelines; OCI Data Science Pipelines Data Processing
EMR (Spark); Azure Databricks; Synapse; Dataproc; OCI Data Flow Storage
S3; Azure Blob Storage; Google Cloud Storage; OCI Object Storage Notes on Usage
Modular, Microsoft integration, unified AI+data stack, BigQuery synergy, enterprise tools The team also needs to implement metrics to monitor model performance, inference server issues, data distribution shifts, and other ML lifecycle risks. Trained models exist for various use-cases and can be reused if features and labels match. If they don’t, predictions may be poor. Model metrics will be covered in future posts. ML Basics: Types of ML Solutions
If you know the value or category to predict, you will likely use supervised learning. If you want to learn about segmentations or groupings, use unsupervised learning. To create new content, use Generative AI. Machine learning has two main phases:
Training
and
Inference . Training computes model parameters; inference uses the trained model on live data. Training builds the model; inference deploys it. Supervised learning finds connections that produce the correct answer and outputs a numerical value or category. Evaluation uses a separate labeled dataset. In real-world deployment, predictions are called inferences on unlabelled data. Regression models output numerical values (e.g., prices, times). Classification models predict categories. Unsupervised learning produces patterns; clustering groups data without predefined labels. Reinforcement learning optimizes actions to maximize rewards in an environment. Generative AI models produce content and often rely on pre-trained models that are customized for specific tasks. Customization methods include distillation, parameter-efficient tuning, and prompt engineering. Understanding Data in Supervised Learning
Data can be text, numbers, images, audio, etc. Features are inputs used to predict labels. Labels are the correct answers. A good dataset is large, diverse, and has reliable labels. Features should be highly predictive and available at prediction time. Feature selection and evaluation are important for model performance. Proxy labels are used when exact labels are unavailable and can have downsides if the proxy does not reflect the target outcome. Conclusion
You don’t need to be an ML expert to lead ML work as a software manager, TPM, or product manager. Build trust with engineers, reduce risk in your roadmap, and unlock insights by understanding the fundamentals. Start small, ask sharp questions, and use this series as your blueprint. Ready to rock your TPM interview? A detailed interview prep guide with tips and strategies is available. Contact details and author bio are provided for context.
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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. Introduction to ML
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.” Is ML the Right Tool for the Job?
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. Framing the ML Problem Like a Leader
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.
Engineering Paths in ML
From a leadership perspective, ML teams fall into three broad categories. Based on experience at Amazon, here is a definition: Infrastructure/Core Platforms : Build scalable data ingestion, training, deployment, and monitoring services (e.g., SageMaker, Vertex AI). Key trait: heavy engineering, lighter ML; the product is the infrastructure. ML Operations (MLOps) : Implement and manage ML tools for real-world use cases; handle data ingestion, labeling, deployment, and monitoring of models. Key trait: engineering-heavy, deeply tied to ML use case. Integrated AI/ML Engineering : Embedded engineers who productionize models, run experiments, and fine-tune models. Key trait: high collaboration with scientists and deeper ML literacy. Regardless of category, every ML-owning team touches MLOps. Many teams use cloud-native ML tools or GenAI services. Understanding your category helps architectural, resourcing, and roadmap decisions. Further detail will come in future posts. Behind the Scenes: ML Data Pipelines
Data pipelines for training and inference are essential and should be prioritized early. Start simple with a few features and scale if justified. A typical ML pipeline spans data ingestion, cleaning, transformation, feature engineering, storage, and delivery to training environments, often requiring orchestration across batch and real-time systems. Common tools include Apache Kafka or Kinesis, Airflow or Prefect, Spark or Pandas, and storage like S3, Snowflake, or BigQuery. Data cleaning, labeling, and handling schema changes are frequently underestimated. Design with observability, reusability, and fault tolerance from day one to support ongoing model trustworthiness. Below is a high-level view of tool categories and example tools (non-exhaustive): Category
AWS
Azure
Google Cloud
OCI ETL / Orchestration
AWS Glue, Step Functions; Azure Data Factory; Cloud Dataflow; Cloud Composer; OCI Data Integration ML Workflow Management
Amazon SageMaker Pipelines; Azure ML Pipelines; Vertex AI Pipelines; OCI Data Science Pipelines Data Processing
EMR (Spark); Azure Databricks; Synapse; Dataproc; OCI Data Flow Storage
S3; Azure Blob Storage; Google Cloud Storage; OCI Object Storage Notes on Usage
Modular, Microsoft integration, unified AI+data stack, BigQuery synergy, enterprise tools The team also needs to implement metrics to monitor model performance, inference server issues, data distribution shifts, and other ML lifecycle risks. Trained models exist for various use-cases and can be reused if features and labels match. If they don’t, predictions may be poor. Model metrics will be covered in future posts. ML Basics: Types of ML Solutions
If you know the value or category to predict, you will likely use supervised learning. If you want to learn about segmentations or groupings, use unsupervised learning. To create new content, use Generative AI. Machine learning has two main phases:
Training
and
Inference . Training computes model parameters; inference uses the trained model on live data. Training builds the model; inference deploys it. Supervised learning finds connections that produce the correct answer and outputs a numerical value or category. Evaluation uses a separate labeled dataset. In real-world deployment, predictions are called inferences on unlabelled data. Regression models output numerical values (e.g., prices, times). Classification models predict categories. Unsupervised learning produces patterns; clustering groups data without predefined labels. Reinforcement learning optimizes actions to maximize rewards in an environment. Generative AI models produce content and often rely on pre-trained models that are customized for specific tasks. Customization methods include distillation, parameter-efficient tuning, and prompt engineering. Understanding Data in Supervised Learning
Data can be text, numbers, images, audio, etc. Features are inputs used to predict labels. Labels are the correct answers. A good dataset is large, diverse, and has reliable labels. Features should be highly predictive and available at prediction time. Feature selection and evaluation are important for model performance. Proxy labels are used when exact labels are unavailable and can have downsides if the proxy does not reflect the target outcome. Conclusion
You don’t need to be an ML expert to lead ML work as a software manager, TPM, or product manager. Build trust with engineers, reduce risk in your roadmap, and unlock insights by understanding the fundamentals. Start small, ask sharp questions, and use this series as your blueprint. Ready to rock your TPM interview? A detailed interview prep guide with tips and strategies is available. Contact details and author bio are provided for context.
#J-18808-Ljbffr