HeyJaredData Science & Machine Learning 101
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- Indexed issues, last 90 days
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- Sep 29, 2026
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How To Start A Career In Data, In 2026 (opens the original)
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When I wrote the original version of this guide in 2022, the career path was pretty straightforward.Learn SQL. Learn Python and R. Build some projects. Get an internship. Become a Data Analyst. Move into Data Science or Machine Learning Engineering.As of today, I cannot in good conscience, give students that exact roadmap anymore.Data Science is not dead. The U.S. Bureau of Labor Statistics still projects Data Scientist employmen
The Death of Prompt Engineering (opens the original)
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In 2023, “prompt engineer” briefly looked like it might become a real profession.Anthropic advertised a Prompt Engineer and Librarian role paying between $175,000 and $335,000 per year. The job involved figuring out the best ways to instruct AI models, documenting those methods, and building a library of prompts that other people could reuse.At that time, this actually made sense.<a class="image-link image2 is-viewable-img" href="https://substackcdn.com/image/fetch/$s_!gWC0!,f_auto,q_auto:good,f
AI Inference (opens the original)
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Once an AI workflow works, the next problem is usually making it fast and cheap enough to use regularly. That, is an inference problem.Inference is the process of using a trained model to produce an output. Every time you send a prompt to GPT, Claude, Gemini, or another model and receive a response, inference is happening.For most people using AI at work, you do not need to understand GPU architecture or build your own inference server. The provider handles that.What you do control is:which mode
Synthetic Data for your workplace LLM (opens the original)
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Most teams working with LLMs eventually run into the same problem, they know what they want the model to do, but they do not have enough examples of it.Maybe you want an internal AI assistant to classify support tickets, write investment summaries, review contracts, generate SQL, or follow a specific workflow. You have 200 good examples, but you need more coverage before you can properly evaluate or fine-tune the system.This is where data augmentation and synthetic data become useful.The importa
Dataset Engineering for Workplace LLMs (opens the original)
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A generic model such as GPT or Claude already knows how to write, summarize, classify, reason, and generate code. What it does not know is how your organization wants those tasks performed.It does not automatically know your preferred report structure, internal terminology, escalation rules, risk categories, compliance requirements, or definition of a good answer. Those behaviors must be demonstrated through instructions, examples, and evaluation.That is where dataset engineering comes in.<a cla
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