Key Technologies and Concepts in AI for Product Managers

For more that a decade I have been in Product for large Enterprise applications, built around prescriptive workflows between servers, mobile devices, and human operators. Each business has a “secret sauce” for their business processes that largely relied on rules-based engines to determine what happens next.

Recently I have been tasked with changing my perspective on the process and how to remove the human interaction, and instead leveraging technology to record what happened.

Transitioning from product management for a SaaS enterprise warehouse management system to a product management in AI is an exciting move, and it involves understanding both the technical and strategic aspects of AI.

I will kick off this series with this high-level overview and will be diving into each topic from a Product Management perspective over the coming weeks. Most content I found online was around using ChatGPT for research or building PowerPoint presentations. This will help you understand not just daily AI tools, but how to write features and stories to build AI capabilities into your current application.

Key Technologies and Concepts in AI

Machine Learning (ML)

  • Supervised Learning: Algorithms that learn from labeled data (e.g., classification and regression).
    Unsupervised Learning: Algorithms that find hidden patterns or intrinsic structures in input data (e.g., clustering and dimensionality reduction).
  • Reinforcement Learning: Algorithms that learn optimal actions through trial and error to maximize some notion of cumulative reward.

Deep Learning

  • Neural Networks: Understanding architectures like feedforward, convolutional, and recurrent neural networks.
  • Frameworks: Familiarity with popular frameworks such as TensorFlow, PyTorch, and Keras.
    Natural Language Processing (NLP)
  • Techniques for text processing, sentiment analysis, machine translation, and conversational AI.
    Models like BERT, GPT, and their applications in various NLP tasks.

Computer Vision

  • Image and video analysis, including object detection, image classification, and segmentation.
  • Technologies like convolutional neural networks (CNNs) and advancements in visual recognition.

Data Engineering and Management

  • Understanding data pipelines, ETL processes, and big data technologies like Hadoop, Spark, and Kafka.
  • Data quality, governance, and the importance of annotated datasets for training AI models.
    Cloud Platforms and AI Services
  • Knowledge of AI and ML services provided by major cloud platforms like AWS (SageMaker), Google Cloud (AI Platform), and Azure (Cognitive Services).

Ethics and Bias in AI

  • Awareness of ethical considerations, fairness, transparency, and bias in AI systems.
  • Regulatory frameworks and compliance standards relevant to AI.

AI in Production

  • Model deployment, monitoring, and maintenance.
  • MLOps: Best practices for continuous integration and deployment (CI/CD) in machine learning projects.

About the Author

The world changed for me about two months into my M.B.A. program. While I have always risen quickly to management roles, learning how to truly be a part of a business and its many systems began to really excite me. While I have loved taking out-dated processes and digitizing them for cost savings, I am excited to take my project mangement experience to greater heights and really contribute to a company's growth as the head of a team excited to drive change. -Justin Velthoen

Justin Velthoen

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