{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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How many years of software engineering experience do you have?How many years of data science experience do you have?Write reusable scripts for data cleaning and mergingMake datasets available on shared infrastructureUse versioning for data, model, configurations and training scriptsContinuously monitor the behaviour of deployed modelsLog production predictions with the model’s version and input dataStore models in a single format for ease of useEquip with web interface, package image, provide REST APIProvide simple API for serving batch and real-time requestsIntegration with existing data infrastructureQuerying, visualising and understanding metrics and event loggingAllow experimentation with the inference codeKeep the model’s API and documentation togetherParallelise feature extractionCache predictionsAsync support for top-down chaining models
031Strongly agreeAgreeStrongly agreeNeither agree nor disagreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeNeither agree nor disagreeStrongly agree
162Neither agree nor disagreeAgreeNeither agree nor disagreeAgreeAgreeNeither agree nor disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly agreeStrongly disagree
215Strongly disagreeDisagreeDisagreeStrongly disagreeDisagreeStrongly disagreeStrongly disagreeNeither agree nor disagreeStrongly disagreeDisagreeNeither agree nor disagreeAgreeStrongly disagreeStrongly disagreeDisagree
333AgreeAgreeDisagreeNeither agree nor disagreeAgreeNot applicableNot applicableNeither agree nor disagreeAgreeDisagreeAgreeStrongly agreeNot applicableNeither agree nor disagreeAgree
417Neither agree nor disagreeDisagreeNeither agree nor disagreeDisagreeStrongly disagreeNot applicableNot applicableDisagreeStrongly disagreeDisagreeDisagreeNeither agree nor disagreeStrongly disagreeStrongly agreeNot applicable
560Strongly agreeStrongly agreeAgreeNeither agree nor disagreeAgreeStrongly agreeAgreeStrongly agreeAgreeStrongly agreeDisagreeStrongly agreeAgreeStrongly agreeNot applicable
622DisagreeNeither agree nor disagreeAgreeStrongly agreeNeither agree nor disagreeDisagreeStrongly agreeDisagreeDisagreeAgreeNeither agree nor disagreeNeither agree nor disagreeNot applicableDisagreeStrongly agree
712DisagreeNeither agree nor disagreeDisagreeAgreeDisagreeStrongly disagreeStrongly agreeStrongly agreeStrongly disagreeDisagreeAgreeStrongly disagreeNot applicableStrongly disagreeStrongly disagree
801Strongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeAgreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeDisagreeStrongly disagree
971Strongly agreeStrongly agreeAgreeStrongly agreeAgreeAgreeStrongly agreeStrongly disagreeStrongly agreeNot applicableAgreeAgreeStrongly agreeStrongly agreeAgree
\n", "
" ], "text/plain": [ " How many years of software engineering experience do you have? \\\n", "0 3 \n", "1 6 \n", "2 1 \n", "3 3 \n", "4 1 \n", "5 6 \n", "6 2 \n", "7 1 \n", "8 0 \n", "9 7 \n", "\n", " How many years of data science experience do you have? \\\n", "0 1 \n", "1 2 \n", "2 5 \n", "3 3 \n", "4 7 \n", "5 0 \n", "6 2 \n", "7 2 \n", "8 1 \n", "9 1 \n", "\n", " Write reusable scripts for data cleaning and merging \\\n", "0 Strongly agree \n", "1 Neither agree nor disagree \n", "2 Strongly disagree \n", "3 Agree \n", "4 Neither agree nor disagree \n", "5 Strongly agree \n", "6 Disagree \n", "7 Disagree \n", "8 Strongly disagree \n", "9 Strongly agree \n", "\n", " Make datasets available on shared infrastructure \\\n", "0 Agree \n", "1 Agree \n", "2 Disagree \n", "3 Agree \n", "4 Disagree \n", "5 Strongly agree \n", "6 Neither agree nor disagree \n", "7 Neither agree nor disagree \n", "8 Disagree \n", "9 Strongly agree \n", "\n", " Use versioning for data, model, configurations and training scripts \\\n", "0 Strongly agree \n", "1 Neither agree nor disagree \n", "2 Disagree \n", "3 Disagree \n", "4 Neither agree nor disagree \n", "5 Agree \n", "6 Agree \n", "7 Disagree \n", "8 Strongly disagree \n", "9 Agree \n", "\n", " Continuously monitor the behaviour of deployed models \\\n", "0 Neither agree nor disagree \n", "1 Agree \n", "2 Strongly disagree \n", "3 Neither agree nor disagree \n", "4 Disagree \n", "5 Neither agree nor disagree \n", "6 Strongly agree \n", "7 Agree \n", "8 Disagree \n", "9 Strongly agree \n", "\n", " Log production predictions with the model’s version and input data \\\n", "0 Strongly agree \n", "1 Agree \n", "2 Disagree \n", "3 Agree \n", "4 Strongly disagree \n", "5 Agree \n", "6 Neither agree nor disagree \n", "7 Disagree \n", "8 Strongly disagree \n", "9 Agree \n", "\n", " Store models in a single format for ease of use \\\n", "0 Strongly agree \n", "1 Neither agree nor disagree \n", "2 Strongly disagree \n", "3 Not applicable \n", "4 Not applicable \n", "5 Strongly agree \n", "6 Disagree \n", "7 Strongly disagree \n", "8 Disagree \n", "9 Agree \n", "\n", " Equip with web interface, package image, provide REST API \\\n", "0 Strongly agree \n", "1 Disagree \n", "2 Strongly disagree \n", "3 Not applicable \n", "4 Not applicable \n", "5 Agree \n", "6 Strongly agree \n", "7 Strongly agree \n", "8 Agree \n", "9 Strongly agree \n", "\n", " Provide simple API for serving batch and real-time requests \\\n", "0 Strongly agree \n", "1 Strongly disagree \n", "2 Neither agree nor disagree \n", "3 Neither agree nor disagree \n", "4 Disagree \n", "5 Strongly agree \n", "6 Disagree \n", "7 Strongly agree \n", "8 Strongly disagree \n", "9 Strongly disagree \n", "\n", " Integration with existing data infrastructure \\\n", "0 Strongly agree \n", "1 Disagree \n", "2 Strongly disagree \n", "3 Agree \n", "4 Strongly disagree \n", "5 Agree \n", "6 Disagree \n", "7 Strongly disagree \n", "8 Disagree \n", "9 Strongly agree \n", "\n", " Querying, visualising and understanding metrics and event logging \\\n", "0 Strongly agree \n", "1 Strongly disagree \n", "2 Disagree \n", "3 Disagree \n", "4 Disagree \n", "5 Strongly agree \n", "6 Agree \n", "7 Disagree \n", "8 Strongly disagree \n", "9 Not applicable \n", "\n", " Allow experimentation with the inference code \\\n", "0 Strongly agree \n", "1 Disagree \n", "2 Neither agree nor disagree \n", "3 Agree \n", "4 Disagree \n", "5 Disagree \n", "6 Neither agree nor disagree \n", "7 Agree \n", "8 Disagree \n", "9 Agree \n", "\n", " Keep the model’s API and documentation together \\\n", "0 Strongly agree \n", "1 Strongly disagree \n", "2 Agree \n", "3 Strongly agree \n", "4 Neither agree nor disagree \n", "5 Strongly agree \n", "6 Neither agree nor disagree \n", "7 Strongly disagree \n", "8 Strongly disagree \n", "9 Agree \n", "\n", " Parallelise feature extraction Cache predictions \\\n", "0 Strongly agree Neither agree nor disagree \n", "1 Disagree Strongly agree \n", "2 Strongly disagree Strongly disagree \n", "3 Not applicable Neither agree nor disagree \n", "4 Strongly disagree Strongly agree \n", "5 Agree Strongly agree \n", "6 Not applicable Disagree \n", "7 Not applicable Strongly disagree \n", "8 Disagree Disagree \n", "9 Strongly agree Strongly agree \n", "\n", " Async support for top-down chaining models \n", "0 Strongly agree \n", "1 Strongly disagree \n", "2 Disagree \n", "3 Agree \n", "4 Not applicable \n", "5 Not applicable \n", "6 Strongly agree \n", "7 Strongly disagree \n", "8 Strongly disagree \n", "9 Agree " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "best_practices = pd.read_csv(\"Best practices assessment.csv\")\n", "best_practices" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "m = {\n", " \"Strongly agree\": 4 / 4,\n", " \"Agree\": 3 / 4,\n", " \"Neither agree nor disagree\": 2 / 4,\n", " \"Disagree\": 1 / 4,\n", " \"Strongly disagree\": 0 / 4,\n", "}\n", "\n", "best_practices_dicts = [v.to_dict() for _, v in best_practices.iterrows()]\n", "\n", "best_practice_score_ds = []\n", "best_practice_score_se = []\n", "\n", "for d in best_practices_dicts:\n", " ds_experience = int(d[\"How many years of data science experience do you have?\"])\n", " del d[\"How many years of data science experience do you have?\"]\n", "\n", " se_experience = int(\n", " d[\"How many years of software engineering experience do you have?\"]\n", " )\n", " del d[\"How many years of software engineering experience do you have?\"]\n", "\n", " scores = [m[v] for v in d.values() if v != \"Not applicable\"]\n", " score = sum(scores) / len(scores)\n", " best_practice_score_ds.append((ds_experience, score))\n", " best_practice_score_se.append((se_experience, score))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.5440527060340572, 0.10399880919437814)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "\n", "best_practice_score_ds = sorted(best_practice_score_ds)\n", "plt.scatter(\n", " [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]\n", ")\n", "stats.pearsonr(\n", " [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.6722543326178704, 0.03321124881554773)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "best_practice_score_se = sorted(best_practice_score_se)\n", "\n", "\n", "plt.scatter(\n", " [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se]\n", ")\n", "stats.pearsonr(\n", " [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se]\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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I believe the use of GreatAI improves the quality of AI deployments.I believe the use of GreatAI would increase my productivity.I believe the use of GreatAI can lead to robust and trustworthy deployments.Overall, I found GreatAI useful when working with AI.I found the GreatAI easy to learn.I found it is easy to employ GreatAI in practice.I found it is easy to integrate GreatAI into an existing project.Overall, I found GreatAI easy to use.Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.Overall, I intend to use the GreatAI in my personal or professional projects.
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" ], "text/plain": [ " I believe the use of GreatAI improves the quality of AI deployments. \\\n", "0 7 \n", "1 7 \n", "2 4 \n", "3 6 \n", "4 7 \n", "5 6 \n", "6 6 \n", "7 7 \n", "8 4 \n", "9 7 \n", "\n", " I believe the use of GreatAI would increase my productivity. \\\n", "0 7 \n", "1 7 \n", "2 4 \n", "3 7 \n", "4 7 \n", "5 6 \n", "6 6 \n", "7 6 \n", "8 5 \n", "9 7 \n", "\n", " I believe the use of GreatAI can lead to robust and trustworthy deployments. \\\n", "0 7 \n", "1 6 \n", "2 5 \n", "3 6 \n", "4 6 \n", "5 6 \n", "6 6 \n", "7 6 \n", "8 5 \n", "9 7 \n", "\n", " Overall, I found GreatAI useful when working with AI. \\\n", "0 7 \n", "1 7 \n", "2 6 \n", "3 7 \n", "4 7 \n", "5 6 \n", "6 4 \n", "7 6 \n", "8 5 \n", "9 7 \n", "\n", " I found the GreatAI easy to learn. \\\n", "0 7 \n", "1 5 \n", "2 6 \n", "3 7 \n", "4 4 \n", "5 6 \n", "6 5 \n", "7 4 \n", "8 7 \n", "9 5 \n", "\n", " I found it is easy to employ GreatAI in practice. \\\n", "0 7 \n", "1 6 \n", "2 5 \n", "3 6 \n", "4 5 \n", "5 6 \n", "6 7 \n", "7 5 \n", "8 2 \n", "9 6 \n", "\n", " I found it is easy to integrate GreatAI into an existing project. \\\n", "0 7 \n", "1 7 \n", "2 4 \n", "3 7 \n", "4 5 \n", "5 6 \n", "6 6 \n", "7 3 \n", "8 3 \n", "9 5 \n", "\n", " Overall, I found GreatAI easy to use. \\\n", "0 7 \n", "1 6 \n", "2 4 \n", "3 6 \n", "4 5 \n", "5 6 \n", "6 6 \n", "7 5 \n", "8 3 \n", "9 6 \n", "\n", " Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future. \\\n", "0 7 \n", "1 7 \n", "2 5 \n", "3 7 \n", "4 6 \n", "5 6 \n", "6 7 \n", "7 5 \n", "8 3 \n", "9 7 \n", "\n", " Overall, I intend to use the GreatAI in my personal or professional projects. \n", "0 7 \n", "1 7 \n", "2 4 \n", "3 6 \n", "4 6 \n", "5 6 \n", "6 7 \n", "7 6 \n", "8 3 \n", "9 7 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tam = pd.read_csv(\"Technology acceptance model questionnaire.csv\")\n", "tam" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.8813771517996869, array([0.688, 0.967]))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pingouin\n", "\n", "pu = [\n", " \"I believe the use of GreatAI improves the quality of AI deployments.\",\n", " \"I believe the use of GreatAI would increase my productivity.\",\n", " \"I believe the use of GreatAI can lead to robust and trustworthy deployments.\",\n", " \"Overall, I found GreatAI useful when working with AI.\",\n", "]\n", "pingouin.cronbach_alpha(tam[pu])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.7729220222793487, array([0.403, 0.937]))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "peou = [\n", " \"I found the GreatAI easy to learn.\",\n", " \"I found it is easy to employ GreatAI in practice.\",\n", " \"I found it is easy to integrate GreatAI into an existing project.\",\n", " \"Overall, I found GreatAI easy to use.\",\n", "]\n", "pingouin.cronbach_alpha(tam[peou])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.9538950715421304, array([0.814, 0.989]))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itu = [\n", " \"Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.\",\n", " \"Overall, I intend to use the GreatAI in my personal or professional projects.\",\n", "]\n", "pingouin.cronbach_alpha(tam[itu])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pu peou itu\n", "0 7.00 7.00 7.0\n", "1 6.75 6.00 7.0\n", "2 4.75 4.75 4.5\n", "3 6.50 6.50 6.5\n", "4 6.75 4.75 6.0\n", "5 6.00 6.00 6.0\n", "6 5.50 6.00 7.0\n", "7 6.25 4.25 5.5\n", "8 4.75 3.75 3.0\n", "9 7.00 5.50 7.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tam[\"pu\"] = tam[pu].mean(1)\n", "tam[\"peou\"] = tam[peou].mean(1)\n", "tam[\"itu\"] = tam[itu].mean(1)\n", "tam[[\"pu\", \"peou\", \"itu\"]]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.5515422017785757, 0.09838124227663879)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.pearsonr(tam[\"peou\"], tam[\"pu\"])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.8066270322592023, 0.004809023073123024)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.pearsonr(tam[\"peou\"], tam[\"itu\"])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.7880605510627579, 0.006774486564715021)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.pearsonr(tam[\"pu\"], tam[\"itu\"])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.4 ('.env': venv)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad" } } }, "nbformat": 4, "nbformat_minor": 2 }