{ "source_file": "0930", "source_platform": "Medium", "author": "Ransaka Ravihara", "title_listed": "Machine Learning on Snowflake", "keywords": "Snowflake,Snowpark,ML", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/machine-learning-on-snowflake-ea6c559af2d/", "publish_date": "2022-12", "corpus_stage": "预训练/SFT", "notes": "", "id": "63c7f9f88b5a", "collected_at": "2026-10-01T23:34:52", "title": "Machine Learning on Snowflake | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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In this article, we will explore the capabilities of its' snowpark python...\n[Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\nDecember 26, 20229 min read\n# How to Build Machine Learning Models on Snowflake\n### Snowflake is one of the leading data platforms out there. In this article, we will explore the capabilities of its' snowpark Python library\n![Photo by Aaron Burden on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/0ceNRNk7_QSHaS2ol-scaled.jpg)\nPhoto by [Aaron Burden](https://unsplash.com/@aaronburden?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nThroughout this article, you will learn how to use,\n 1. snowpark-python functionalities for primary data preprocessing\n 2. Train and deploy machine learning models in Snowflake\n 3. Define UDFs in a pythonic way and deploy them in snowflake\n\n\n![Topics covered in this article | Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/1zOTKNeFOfEDF48XAIwR4YA.png)\nTopics covered in this article | Image by author\nIf you are willing to follow along with the tutorial, you should have an [Anaconda integration-enabled snowflake account](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda). Otherwise, you must sign up for a free [snowflake trial account](https://signup.snowflake.com/) and configure it as described [here](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda).\nAt first glance, snowpark is a machine learning and data science framework that offers the power of SQL within Python flexibility. Sometimes, this is similar to the Apache spark framework. However, this provides a pervasive framework for our machine learning and data science projects. Before trying anything in this article, you should establish a connection between python and Snowflake. You can refer to [my code repo](https://github.com/Ransaka/ML-on-Snowflake) for code samples. Let's create a database connection.\npython\n\n```\nfrom snowflake.snowpark.session import Session\n\naccountname = \"********\" # your accountname\nusername = \"**********\" #your snowflake username\npassword = \"*************\" #snowflake password\n\nconnection_parameters = {\n \"account\": accountname,\n \"user\": username,\n \"password\": password,\n \"role\": \"ACCOUNTADMIN\"\n}\n\ndef snowflake_connector():\n try:\n session = Session.builder.configs(connection_parameters).create()\n print(\"connection successful!\")\n except:\n raise ValueError(\"error while connecting with db\")\n return session\n\n#define a session\nsession = snowflake_connector()\n```\n\nNow we can start the primary data preprocessing part. Instead of preprocessing with Pandas DataFrame, I will do this with the snowpark side. Here I'm going to use the [COVID-19](https://www.kaggle.com/datasets/meirnizri/covid19-dataset) Dataset, which is available in the Kaggle under [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). I have already loaded this Dataset as a Snowflake table. Since it's not the primary goal of this article, I'm skipping that part. You can load the Dataset as described in this [article's GitHub repo](https://github.com/Ransaka/ML-on-Snowflake). Let's read the table.\npython\n\n```\nsnowpark_df = session.table(\"COVID19_RECORDS\")\n\nprint(type(snowpark_df) # snowflake.snowpark.table.Table\nprint(f\"Size of the table object: {(sys.getsizeof(snowpark_df)/1e6)} MB\")\n#'Size of the table object: 4.8e-05 MB'\n```\n\nAbove `snowpark_df` is a lazily-evaluated table; hence It won't consume much memory like pandas data frames. But we can apply any transformations aggregations and much more as we did with pandas.\npython\n\n```\nsnowpark_df.schema.fields\n\n# [StructField('USMER', LongType(), nullable=True),\n# StructField('MEDICAL_UNIT', LongType(), nullable=True),\n# StructField('SEX', LongType(), nullable=True),\n# StructField('PATIENT_TYPE', LongType(), nullable=True),\n# StructField('DATE_DIED', StringType(), nullable=True),\n# StructField('INTUBED', LongType(), nullable=True),\n# StructField('PNEUMONIA', LongType(), nullable=True),\n# StructField('AGE', LongType(), nullable=True),\n# StructField('PREGNANT', LongType(), nullable=True),\n# StructField('DIABETES', LongType(), nullable=True),\n# StructField('COPD', LongType(), nullable=True),\n# StructField('ASTHMA', LongType(), nullable=True),\n# StructField('INMSUPR', LongType(), nullable=True),\n# StructField('HIPERTENSION', LongType(), nullable=True),\n# StructField('OTHER_DISEASE', LongType(), nullable=True),\n# StructField('CARDIOVASCULAR', LongType(), nullable=True),\n# StructField('OBESITY', LongType(), nullable=True),\n# StructField('RENAL_CHRONIC', LongType(), nullable=True),\n# StructField('TOBACCO', LongType(), nullable=True),\n# StructField('CLASIFFICATION_FINAL', LongType(), nullable=True),\n# StructField('ICU', LongType(), nullable=True)]\n```\n\nThere are 1,048,575 unique records and 21 columns in the Dataset. Let's do some fundamental analysis. First, let's define the target variable as follow. As per the description of the Dataset, the 1,2, and 3 values in `CLASSIFICATION_FINAL` the column represent the positive cases, and the rest represent the negative cases. Let's define a new column called `TARGET` by applying the above logic. The equivalent SQL logic will be,\nsql\n\n```\nSELECT\n \"USMER\",\n \"MEDICAL_UNIT\",\n \"SEX\",\n \"PATIENT_TYPE\",\n \"DATE_DIED\",\n \"INTUBED\",\n \"PNEUMONIA\",\n \"AGE\",\n \"PREGNANT\",\n \"DIABETES\",\n \"COPD\",\n \"ASTHMA\",\n \"INMSUPR\",\n \"HIPERTENSION\",\n \"OTHER_DISEASE\",\n \"CARDIOVASCULAR\",\n \"OBESITY\",\n \"RENAL_CHRONIC\",\n \"TOBACCO\",\n \"CLASIFFICATION_FINAL\",\n \"ICU\",\n CASE\n WHEN (\"CLASIFFICATION_FINAL\" < 4 :: INT) THEN 1 :: INT\n ELSE 0 :: INT\n END AS \"TARGET\"\nFROM\n COVID19_RECORDS\n```\n\nSince we are working with snowpark API, let's create this with snowpark.\npython\n\n```\nimport snowflake.snowpark.functions as F\n\nsnowpark_df.with_column('TARGET', F.when(F.col('CLASIFFICATION_FINAL')\n < 4, 1).otherwise(0))\n```\n\nLet's see our target distribution.\nscss\n\n```\nsnowpark_df\n.group_by(\"TARGET\").count().to_pandas().set_index(\"TARGET\")\n.plot.bar()\n\nplt.title(\"Target distribution\",fontweight='semibold')\nplt.show()\n```\n\n![Target Distribution | Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/1soGMsladnT8HxaSLFCgIrA.png)\nTarget Distribution | Image by author\nLet's create one more plot.\nscss\n\n```\nsnowpark_df\n.select('AGE').to_pandas()\n.plot.hist(bins=100,alpha=0.5)\n\nplt.title(\"Age distribution\",fontweight='semibold')\nplt.show()\n```\n\n![Age Distribution | Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/1zEq0762fLVascp54cE0rmg.png)\nAge Distribution | Image by author\nLet's find the relationship between the Age variable and the target variable.\npython\n\n```\nsnowpark_df = snowpark_df.with_column(\n \"AGE_BKT\",\n F.when(F.col(\"AGE\") < 21, \"YOUNG\").otherwise(\n F.when(F.col(\"AGE\") < 49, \"ADULT\").otherwise(\"OLD ADULT\")\n ),\n)\n\nage_bkt_df = snowpark_df.select(\n F.col(\"AGE_BKT\"),\n F.when((F.col(\"AGE_BKT\")=='YOUNG') & (F.col(\"TARGET\")==1),1).otherwise(0).as_(\"YOUNG_\"),\n F.when((F.col(\"AGE_BKT\")=='ADULT') & (F.col(\"TARGET\")==1),1).otherwise(0).as_(\"ADULT_\"),\n F.when((F.col(\"AGE_BKT\")=='OLD ADULT') & (F.col(\"TARGET\")==1),1).otherwise(0).as_(\"OLD_ADULT_\")\n)\n\nage_bkt_df.group_by(F.col(\"AGE_BKT\")).count().show()\n\n# -----------------------\n# |\"AGE_BKT\" |\"COUNT\" |\n# -----------------------\n# |OLD ADULT |342413 |\n# |ADULT |628554 |\n# |YOUNG |77608 |\n# -----------------------\n\nage_bkt_df.select(\n ((F.sum(\"YOUNG_\") * 100 ) / F.count(\"YOUNG_\")).as_(\"YOUNG % OF CASES\"),\n ((F.sum(\"ADULT_\") * 100) / F.count(\"ADULT_\")).as_(\"ADULT % OF CASES\"),\n ((F.sum(\"OLD_ADULT_\") * 100) / F.count(\"OLD_ADULT_\")).as_(\"OLD_ADULT % OF CASES\")\n).show()\n\n# --------------------------------------------------------------------\n# |\"YOUNG % OF CASES\" |\"ADULT % OF CASES\" |\"OLD_ADULT % OF CASES\" |\n# --------------------------------------------------------------------\n# |1.534463 |20.877858 |14.969745 |\n# --------------------------------------------------------------------\n```\n\nAfter completing our analysis, we can save the transformed Dataset as a new Snowflake table using the following way.\npython\n\n```\nsnowpark_df.write.save_as_table(\n table_name='COVID19_RECORDS_PROCESSED',\n mode='overwrite'\n)\n```\n\nAlright, now we have preprocessed Dataset. Let's start the model training phase.\npython\n\n```\n# read the table \ntrain_data = session.table(\"COVID19_RECORDS_PROCESSED\")\n\n#create the stage for storing the ML models\nsession.sql('CREATE OR REPLACE STAGE ML_MODELS').show()\n```\n\nWe can use two different approaches to train and deploy models in Snowflake.\n 1. We can train the model locally, upload it to a stage and load it from the stage when the UDF is called.\n 2. We can define SPROC, which can train the model and save the trained model into the Snowflake stage when the SPROC is called. Here we'll need a separate UDF for the inferencing part.\n\n\nIn this article, we will explore both methods above.\n### Train the model locally, upload it to a stage and load it from the stage\n![Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/1swC4aXf4M_hgkUT7Fe7r2Q.png)\nImage by author\nFirst, we have to define the function for training the model locally.\npython\n\n```\ndef train_model_locally(train:snowflake.snowpark.table.Table):\n from sklearn.tree import DecisionTreeClassifier\n\n #convert into pd dataframes\n\n train = train.to_pandas()\n\n xtrain,ytrain = train.drop('TARGET',axis=1),train['TARGET']\n\n model = DecisionTreeClassifier()\n model.fit(xtrain,ytrain)\n\n return model\n\n#let's train the DT model\nmodel = train_model_locally(train_data_sf)\n\n#save the model\nimport joblib\njoblib.dump(model, 'predict_risk_score.joblib')\n\n#upload into the ML_MODELS SNowfla\nsession.file.put(\n \"predict_risk_score.joblib\", \"@ML_MODELS\", auto_compress=False, overwrite=True\n)\n```\n\nSimilar to other machine learning pipelines, we need to define library dependencies.\npython\n\n```\nsession.clear_imports()\nsession.clear_packages()\n\n#Register above uploded model as import of UDF\nsession.add_import(\"@ML_MODELS/predict_risk_score.joblib\")\n\n#map packege dependancies\nsession.add_packages(\"joblib==1.1.0\", \"scikit-learn==1.1.1\", \"pandas==1.3.2\")\n```\n\nLet's define the UDF. Inside the UDF, it should load the model from the stage and then use it for the inferencing.\npython\n\n```\nfrom snowflake.snowpark.types import PandasSeries, PandasDataFrame\n\ndef read_file(filename):\n import joblib\n import sys\n import os\n\n #where all imports located at\n import_dir = sys._xoptions.get(\"snowflake_import_directory\")\n\n if import_dir:\n with open(os.path.join(import_dir, filename), 'rb') as file:\n m = joblib.load(file)\n return m\n\n#register UDF\n@F.udf(name = 'predict_risk_score', is_permanent = True, replace = True, stage_location = '@ML_MODELS')\ndef predict_risk_score(ds: PandasSeries[dict]) -> PandasSeries[float]:\n\n # later we will input train data as JSON object\n # hance, we have to convert JSON object as pandas DF\n df = pd.io.json.json_normalize(ds)[feature_cols]\n pipeline = read_file('predict_risk_score.joblib')\n return pipeline.predict_proba(df)[:,1]\n```\n\nNow we have successfully registered our UDF in Snowflake. You can verify it using the following way.\npython\n\n```\nsession.sql(\"DESC FUNCTION PREDICT_RISK_SCORE()\").show()\n\n# ------------------------------------------------------------------------\n# |\"property\" |\"value\" |\n# ------------------------------------------------------------------------\n# |signature |() |\n# |returns |FLOAT |\n# |language |PYTHON |\n# |null handling |CALLED ON NULL INPUT |\n# |volatility |VOLATILE |\n# |body | |\n# | |import pickle |\n# | | |\n# | |func = pickle.loads(bytes.fromhex('800595050400... |\n# | |# The following comment contains the UDF source... |\n# | |# import pandas as pd |\n# | |# def read_file(filename): |\n# | |# import joblib |\n# | |# import sys |\n# | |# import os |\n# | |# |\n# | |# import_dir = sys._xoptions.get(\"snowflake... |\n# | |# if import_dir: |\n# | |# with open(os.path.join(import_dir, fi... |\n# | |# m = joblib.load(file) |\n# | |# return m |\n# | |# @F.udf(name = 'predict_risk_score', is_perman... |\n# | |# def predict_risk_score(*args) -> PandasSeries... |\n# | |# df = pd.DataFrame([args]) |\n# | |# pipeline = read_file('predict_risk_score.... |\n# | |# return pipeline.predict_proba(df)[:,1] |\n# | |# |\n# | |# func = predict_risk_score |\n# | |# |\n# | | *********RESULTS TRUNCATED************** |\n# ------------------------------------------------------------------------\n```\n\nLet's use UDF for inferencing.\npython\n\n```\n# `test_data_sf` is a fraction of `train_data`\ntest_data_sf.with_column(\n 'PREDICTION', \n predict_risk_score(F.object_construct('*')))\n.select(\"TARGET\",\"PREDICTION\").show(20)\n\n# ---------------------------------\n# |\"TARGET\" |\"PREDICTION\" |\n# ---------------------------------\n# |1 |0.8333333333333334 |\n# |1 |0.0 |\n# |1 |1.0 |\n# |1 |1.0 |\n# |1 |0.3333333333333333 |\n# |0 |0.0 |\n# |1 |0.4 |\n# |0 |0.5 |\n# |1 |0.421875 |\n# ---------------------------------\n\n#similary, you can use below SQL as well.\n\nselect\n target,\n predict_risk_score(object_construct(*)) as predictions\nfrom\n COVID19_RECORDS_PROCESSED\nlimit\n 100;\n```\n\n### Define train and inferencing procs/UDFs\nThis method will create a stored procedure for training the model and UDF for inferencing the model. You may refer to the diagram below for more insights.\n![Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/1KIEhq0KvXT8wBjOcdI6zQQ.png)\nImage by author\nLet's define the stored procedure. At first, we will implement the Python function, and we can convert it to the Snowflake stored procedure in later steps.\npython\n\n```\ndef train_dt_procedure(\n session: Session,\n training_table: str,\n feature_cols: list,\n target_col: str,\n model_name: str,\n) -> T.Variant:\n\n \"\"\"\n This will be our training procedure. Later we will register this as snowflake procedure.\n\n training_table: snowflake table name to be used for training task\n feature_cols: list of columns to be used in training\n target_col: target column to be used\n model_name: model name to used for model saving purpose\n\n \"\"\"\n\n #convert as pandas DF, rest of the steps similar to the local model training and saving.\n local_training_data = session.table(training_table).to_pandas()\n\n from sklearn.tree import DecisionTreeClassifier\n\n X = local_training_data[feature_cols]\n y = local_training_data[target_col]\n\n model = DecisionTreeClassifier()\n model.fit(X, y)\n\n #do what ever you want to do with model, even the hyperparameter tuning..\n # here I'll get feature importance\n feat_importance = pd.DataFrame(\n model.feature_importances_, feature_cols, columns=[\"FeatImportance\"]\n ).to_dict()\n\n from joblib import dump\n\n dump(model, \"/tmp/\" + model_name)\n session.file.put(\n \"/tmp/\" + model_name, \"@ML_MODELS\", auto_compress=False, overwrite=True\n )\n return feat_importance\n```\n\nLet's register the above Python function as a stored procedure.\npython\n\n```\nsproc_train_dt_model = session.sproc.register(\n func=train_dt_procedure, \n name='sproc_train_dt_model', \n is_permanent=True, \n replace=True, \n stage_location='@ML_MODELS', \n packages=[\n 'snowflake-snowpark-python',\n 'scikit-learn',\n 'joblib']\n)\n```\n\nNow we can use the procedure`SPROC_TRAIN_DT_MODEL()` as follows.\npython\n\n```\ntrain_data = session.table(\"COVID19_RECORDS_PROCESSED\")\n\n#create train and test dataframes\ntrain_data_pd,test_data_pd = train_test_split(\n train_data_pd,\n stratify=train_data_pd['TARGET'],\n test_size=0.1\n)\n\n# writing as tempoary tables for mode training and inferencing part\nsession.write_pandas(\n train_data_pd,\n table_name=\"TRAIN_DATA_TMP\",\n auto_create_table=True,\n table_type=\"temporary\",\n)\nsession.write_pandas(\n test_data_pd,\n table_name=\"TEST_DATA_TMP\",\n auto_create_table=True,\n table_type=\"temporary\",\n)\n\ntrain_data_pd = train_data.to_pandas()\n\nfeature_cols = train_data.columns\nfeature_cols.remove('TARGET')\ntarget_col = 'TARGET'\nmodel_name = 'decisiontree.model' # How model should be saved in stage\n\nmodel_response = sproc_train_dt_model('TRAIN_DATA_TMP', \n feature_cols, \n target_col,\n model_name, \n session=session\n )\n\nprint(model_response)\n\n# {\n# \"FeatImportance\": {\n# \"AGE\": 0.4543249401305732,\n# \"ASTHMA\": 0.029003830541684678,\n# \"CARDIOVASCULAR\": 0.025649097586968667,\n# \"COPD\": 0.019300936592021863,\n# \"DIABETES\": 0.059273293874405074,\n# \"HIPERTENSION\": 0.05885196748765571,\n# \"INMSUPR\": 0.0232534703448427,\n# \"INTUBED\": 0.026365011429648998,\n# \"MEDICAL_UNIT\": 0.08804779552309593,\n# \"OBESITY\": 0.02991724846285235,\n# \"OTHER_DISEASE\": 0.026840169399286344,\n# \"PATIENT_TYPE\": 0,\n# \"PNEUMONIA\": 0.04225497414608237,\n# \"PREGNANT\": 0.012929499812685114,\n# \"RENAL_CHRONIC\": 0.015894267526361774,\n# \"SEX\": 0,\n# \"TOBACCO\": 0.028563364646896985,\n# \"USMER\": 0.059530132494938236\n# }\n# }\n\n#plot feature importance\nfeature_coefficients = pd.DataFrame(eval(model_response))\n\nfeature_coefficients\n.sort_values(by='FeatImportance',ascending=False)\n.plot\n.bar(y='FeatImportance', figsize=(12,5))\nplt.show()\n```\n\n![Feature Importance | Image by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/1sJY5i5mVl2inFLRwxE6XHg.png)\nFeature Importance | Image by author\nWe can define the UDF as follows. This function is similar to the previous one.\npython\n\n```\ndef udf_predict_risk_score(*args) -> float:\n import os\n import sys\n from joblib import load\n\n IMPORT_DIRECTORY_NAME = \"snowflake_import_directory\"\n import_dir = sys._xoptions[IMPORT_DIRECTORY_NAME]\n model_name = 'decisiontree.model'\n model = load(import_dir+model_name)\n\n #unlike previous JSON object, this will be a array, hence no need to\n # decode the input\n scored_data = model.predict(pd.DataFrame([args]))[0]\n\n return scored_data\n```\n\nFinally, registering the UDF.\npython\n\n```\nudf_risk_score_model = session.udf.register(\n func=udf_predict_risk_score, \n name=\"udf_risk_score_model\", \n stage_location='@ML_MODELS',\n input_types=[T.FloatType()]*len(feature_cols),\n return_type = T.FloatType(),\n replace=True, \n is_permanent=True, \n imports=['@ML_MODELS/decisiontree.model'],\n packages=['scikit-learn==1.1.1','pandas','joblib'], \n session=session\n)\n```\n\nAlright, it's time to get predictions for our validation dataset. Here I am doing it with Snowflake editor.\nsql\n\n```\nSELECT\n \"TARGET\",\n udf_risk_score_model(\n \"USMER\",\n \"MEDICAL_UNIT\",\n \"SEX\",\n \"PATIENT_TYPE\",\n \"INTUBED\",\n \"PNEUMONIA\",\n \"AGE\",\n \"PREGNANT\",\n \"DIABETES\",\n \"COPD\",\n \"ASTHMA\",\n \"INMSUPR\",\n \"HIPERTENSION\",\n \"OTHER_DISEASE\",\n \"CARDIOVASCULAR\",\n \"OBESITY\",\n \"RENAL_CHRONIC\",\n \"TOBACCO\"\n ) AS \"PREDICTION\"\nFROM\n COVID19_RECORDS_PROCESSED limit 100;\n```\n\n### Conclusion\nWhile snowpark offers a comprehensive platform for our machine learning tasks, it has a [few issues](https://docs.snowflake.com/en/developer-guide/snowpark/python/python-snowpark-training-ml.html#limitations-and-known-issues) at the time of writing this article. As an example, PyTorch still needs to be supported by a snowpark. Also, only selected packages are available in conda; if we want to use other packages, such as catboost, we must import them manually into our environment as described [here](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs.html#reading-files-from-a-udf).\nThanks for reading! Connect with me on [LinkedIn](https://www.linkedin.com/in/ransaka/).\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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