Reading time - 4 mins
1. Technical ML Section
Comparison between from scratch ad-hoc development of ML pipelines vs Open-Source ML Pipeline Frameworks.
The full article is HERE (reading time - 6 mins).
2. Career ML Section
Tips on writing LinkedIn headline: what TO and NOT TO write to get more profile views and job interview requests.
1. Technical ML Section:
(For a more detailed discussion, read the full blog article!)
🤔 What is an ML Pipeline?
An ML pipeline is a structured workflow of different data and ML model operations:
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Data Preprocessing
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Feature Engineering
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Model Training
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Inference
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Data Postprocessing
Typically, an ML pipeline is a Directed Acyclic Graph (DAG) in which nodes represent tasks (or steps), and edges define data flow (see the figure below).
👉 In this newsletter, we explore the two main pipeline-building approaches: Ad-hoc vs. Framework-based.
🔹 Ad-hoc Pipelines – Custom-built pipelines, developed from scratch, tailored with a unique configuration and preferences by a Data Scientist / ML Engineer.
🔹 Framework-Based Pipelines – Standardized framework solutions like Kedro, MetaFlow, and Flyte that provide built-in tools for quick pipeline DAG setup, visualization, orchestration, dependency tracking, and logging.
Both approaches have Pros and Cons.
The detailed discussion of them is in the full article. Below is the comparison summary.
🗒️ Comparison Summary
✅ Use Ad-hoc Pipelines When:
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The project is small and experimental.
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There is no immediate need for scaling or maintenance.
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Full control over pipeline design is required specific for a use case.
✅ Use Framework-based Pipelines When:
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The project is large, complex, and expected to grow.
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Standardization, maintainability, and collaboration are priorities.
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There is a need for robust orchestration, logging, monitoring & long-term support.
2. Career ML Section
LinkedIn Headline is the part of your profile which appears right after your profile photo.
This is one of the most important part of your profile. Here is mine.
🤔 Why is the headline important?
Because of 3 main reasons:
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Keywords in headline are used in the HR search engine
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Headline is what people see without viewing your profile. Headline makes the first impression about you.
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Headline quickly tells people if you can help them or not.
👇 Here are 3 tips that will make your headline stand out.
✅ Tip 1: Don’t write “Data Scientist @ XYZ company“
Your company is NOT what you have to represent. In the headline, you have to represent YOURSELF. This means you have to highlight your expertise.
✅ Tip 2: Use keywords to highlight your expertise, DS domain, years of experience & how you can help (if you provide services)
For example, for a mid-level Data Scientist it can be:
Data Scientist | Time Series Forecasting | MLOps | ML in Retail & Logistics | 4 years of experience
This is very clear. Compare it with:
Data Scientist @ Whatever Limited LLC
It tells nearly nothing about YOU and what YOU can do for a page visitor.
✅ Tip 3: Make it 2 lines max
Having the headline of 2 lines, you make it much more readable for a human eye.
It should be straight to the point and highlight your real expertise, not include every ML-relevant keyword. Here, less is more.
That is it for this week!
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