docs: credit card fraud tutorial notebook update #555
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mikeldking
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Apr 11, 2023
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| "## 3. Compute Embeddings\n", | ||
| "\n", | ||
| "**NOTE: The use of GPUs is recommended for embedding generation. If you are running in Colab, we encourage upgrading to Colab Pro.** \n", | ||
| "Run the cell below if you have a CUDA-enabled GPU and want to compute embeddings for your tabular data from scratch; otherwise, skip this step to use the pre-computed embeddings downloaded with the rest of your data in step 2.\n", | ||
| "\n", | ||
| "The large language models that Arize's embedding generators use have already been trained in such a huge amount of data that the embeddings can capture relevant structure in your data without being fine-tuned." | ||
| "`EmbeddingGeneratorForTabularFeatures` represents each row of your DataFrame as a piece of text and computes an embedding for that text using a pre-trained large language model (in this case, \"distilbert-base-uncased\"). For example, if a row of your DataFrame represents a transaction in the state of California from a merchant named \"Leannon Ward\" with a FICO score of 616 and a merchant risk score of 23, `EmbeddingGeneratorForTabularFeatures` computes an embedding for the text: \"The state is CA. The merchant ID is Leannon Ward. The fico score is 616. The merchant risk score is 23...\"" |
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I'm not sure many people will be familiar with generating embeddings for a tabular use-case. maybe add a TLDR and link out to this? https://docs.arize.com/arize/embeddings/embeddings-for-tabular-data-multivariate-drift
mikeldking
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Apr 11, 2023
mikeldking
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Apr 11, 2023
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| "## 6. Load and View Exported Data\n", | ||
| "\n", | ||
| "View your most recently exported data as a DataFrame." |
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Some context as to why you would export data might help - e.g. contextualize it in the ML Ops lifecycle. I see it's below but it might be worth having a concrete example (finding a cohort that is in production but not training, an under-performing cluster, etc)
mikeldking
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Apr 11, 2023
fjcasti1
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Apr 18, 2023
* main: v0.0.13 fix: don't compile js/html if exists - unblock conda (#597) docs: credit card fraud tutorial notebook update (#555) docs: update quickstart notebook (#564) don't raise error during dimension type inference (#596) fix: Update pyproject.toml (#595) chore: change https to http for downloading fixtures and example datasets (#589) chore: Use pre commit for prettier and eslint (#588) ci: Create .github/dependabot.yml (#587) chore: create SECURITY.md (#586) chore: legal info (#583) fix: ignore non-vectors for embeddings (#584) chore: bump to typescript 5 (#585) v0.0.12 feat(embeddings): grid view improvements: sizes, multi-modal output (#565)
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