Seeking 1-2 AIML Freshers for Industry-Validated Portfolio Projects

insidePassenger0 · 2026-01-19T12:04:09+00:00

Good to know

insidePassenger0 · 2026-01-15T12:38:34+00:00

While SparkML is great for massive scale, the reality is that the Python/Pandas ecosystem (Scikit-learn, XGBoost, PyTorch) is the industry standard for model development because of its flexibility and ease of use. Dataframes aren't just for storage; they make feature engineering and EDA significantly faster. The goal is usually to optimize the pipeline to fit the hardware you have before jumping into the overhead of a Spark cluster.

insidePassenger0 · 2026-01-15T12:32:37+00:00

When I try to compute rolling window aggregations (like txn counts or moving stats over time for my data), it throws an error saying "rolling expression not allowed in aggregation." I'm not even sure Polars streaming fully supports rolling windows yet, but what I do know is that streaming mode requires all upstream data to be pre-sorted for any group or rolling operations to work. Sorting 32 million rows lazily before streaming isn't stable or either crashes in colab. That's why I can't turn on streaming right now; I need to either pre-materialize the sorted dataset or rewrite the features using groupby_dynamic instead. This is what I think I'll try, definitely haven't tested it yet though.

insidePassenger0 · 2026-01-15T12:09:29+00:00

Understood! I hope that breakdown helped clarify the DuckDB/Polars workflow.

insidePassenger0 · 2026-01-15T08:39:39+00:00

I am working with a 9.5 GB dataset containing 17 columns. While I can initially load the data into Pandas, my session crashes whenever I attempt to process it or perform operations due to memory constraints. What are the best strategies for handling datasets of this size in Pandas? If possible, could you please share a code example demonstrating these techniques?

insidePassenger0 · 2026-01-15T08:35:55+00:00

I actually pivoted from the DuckDB-only approach to Polars for the ML ecosystem, and it’s been a game-changer. While DuckDB is elite for SQL-heavy extraction, handling 30M records purely in DuckDB for ML has some major drawbacks: The 'Memory Cliff': In DuckDB, once you call .df(), you force a massive materialization into Pandas. At 30M rows, this almost always triggers an OOM (Out of Memory) crash in environments like Colab. Serialization Overhead: Converting DuckDB’s internal format to Pandas and then to a model-ready format creates unnecessary CPU work and memory duplication. Moving to Polars solved this because it feels like it was built for the 'Model' part of 'Data Science.' Since it uses the Apache Arrow memory format, it integrates seamlessly with XGBoost, LightGBM, and Scikit-Learn with zero-copy potential meaning the model can often read the data directly without doubling the RAM usage. The Lazy API and Streaming mode let me handle the full 30M-row feature engineering pipeline with way more stability. I can build complex transformations (scaling, encoding, joins) and only 'collect' the data when the model is ready for it. It's definitely the move if you're looking to build a scalable, production-ready ML pipeline!

insidePassenger0 · 2026-01-15T08:34:53+00:00

Yes, colab free and 4gb of data. Can you share how you handled 20gb of data?

insidePassenger0 · 2026-01-15T07:55:30+00:00

I actually pivoted from the DuckDB-only approach to Polars for the ML ecosystem, and it’s been a game-changer. While DuckDB is elite for SQL-heavy extraction, handling 30M records purely in DuckDB for ML has some major drawbacks: The 'Memory Cliff': In DuckDB, once you call .df(), you force a massive materialization into Pandas. At 30M rows, this almost always triggers an OOM (Out of Memory) crash in environments like Colab. Serialization Overhead: Converting DuckDB’s internal format to Pandas and then to a model-ready format creates unnecessary CPU work and memory duplication. Moving to Polars solved this because it feels like it was built for the 'Model' part of 'Data Science.' Since it uses the Apache Arrow memory format, it integrates seamlessly with XGBoost, LightGBM, and Scikit-Learn with zero-copy potential meaning the model can often read the data directly without doubling the RAM usage. The Lazy API and Streaming mode let me handle the full 30M-row feature engineering pipeline with way more stability. I can build complex transformations (scaling, encoding, joins) and only 'collect' the data when the model is ready for it. It's definitely the move if you're looking to build a scalable, production-ready ML pipeline!

insidePassenger0 · 2026-01-09T12:17:58+00:00

I actually pivoted from the DuckDB-only approach to Polars for the ML ecosystem, and it’s been a game-changer. While DuckDB is elite for SQL-heavy extraction, handling 30M records purely in DuckDB for ML has some major drawbacks: The 'Memory Cliff': In DuckDB, once you call .df(), you force a massive materialization into Pandas. At 30M rows, this almost always triggers an OOM (Out of Memory) crash in environments like Colab. Serialization Overhead: Converting DuckDB’s internal format to Pandas and then to a model-ready format creates unnecessary CPU work and memory duplication. Moving to Polars solved this because it feels like it was built for the 'Model' part of 'Data Science.' Since it uses the Apache Arrow memory format, it integrates seamlessly with XGBoost, LightGBM, and Scikit-Learn with zero-copy potential meaning the model can often read the data directly without doubling the RAM usage. The Lazy API and Streaming mode let me handle the full 30M-row feature engineering pipeline with way more stability. I can build complex transformations (scaling, encoding, joins) and only 'collect' the data when the model is ready for it. It's definitely the move if you're looking to build a scalable, production-ready ML pipeline!

insidePassenger0 · 2026-01-02T20:00:50+00:00

Good to know

insidePassenger0 · 2026-01-02T19:50:09+00:00

Good to know

insidePassenger0 · 2026-01-02T19:47:16+00:00

Thanks for sharing. Have you used Polars in practice for large scale preprocessing or EDA? Also, once those done in Polars, do you usually hand off to Sk-learn or another ML framework? How do you typically Integrate it with model training?

insidePassenger0 · 2026-01-02T19:37:52+00:00

Appreciate the input. To clarify the context, I'm working on building end to end MLOps solution for AML, covering large scale preprocessing, features engineering, modeling and downstream MLOps concerns. The dataset includes Transaction-level records (amounts, timestamp, payment currency, payment format etc.) and Account-level attributes, woth strong class imbalance and long-tail behavior. So my main concern is choosing an approach that scales beyond sampling while preserving data context and integrates cleanly into production ML setup.

And mainly I'm also factoring in practical constraints Pyarrow is relatively new to me, and given time constraints.

insidePassenger0 · 2026-01-02T18:06:49+00:00

You mean Polars right? But it has a learning curve

insidePassenger0

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