Bigquery automl

Bigquery automl DEFAULT

AutoML Tables uses AI to complete the data prep, feature engineering, model selection and hyperparameter tuning steps of a data science workflow. It allows your entire team to automatically build and deploy state-of-the-art machine learning models on structured data to predict numerical or categorical outcomes. Using this Block, Looker developers can add these advanced analytical capabilities right into new or existing Explores, no data scientists required.

Using this Block, you can integrate Looker with BigQuery ML and AutoML Tables to get the benefit of advanced analytics without needing to be an expert in data science. Start with your problem: What is the outcome you want to achieve? What kind of data is the target column? Depending on your answers, this Block will create an auto-classification or auto-regression model to solve your use case:

  • A binary classification model predicts a binary outcome (one of two classes). Use this for yes or no questions, for example, predicting whether a customer will make a purchase.
  • A multi-class classification model predicts one class from three or more discrete classes. Use this to categorize things, like segmenting defect types in a manufacturing process.
  • A regression model predicts a continuous value. Use this to predict customer spend or future return rates.

This Block gives business users the ability to make predictions (categorical or numerical) from a new or existing Explore. Explores created with this Block can be used to create multiple classification and regression models, evaluate them, and access their predictions in dashboards or custom analyses.

Learn more in the associated AutoML Tables Beginner's Guide.

This Block can be installed via the Looker Marketplace.

Sours: https://looker.com/platform/blocks/data-tool/bigquery-ml-classification-and-regression

The best of both worlds: Calling Auto ML from BigQuery

We now have a new model_type in BigQuery ML. Besides linear models, DNN, and boosted trees, we can now employ the big kahuna (Auto ML Tables)directly from BigQuery SQL.

Training

The model here trains the model on Stack Overflow titles and tags and predicts whether a post will have an accepted answer:

CREATE OR REPLACE MODEL advdata.so_answered_automl
OPTIONS(MODEL_TYPE = 'automl_classifier', budget_hours=2.0, INPUT_LABEL_COLS=['answered'])
ASSELECT
title,
REPLACE(tags, '|', ' ') AS tags,
IF(accepted_answer_id IS NULL, False, True) AS answered
FROM `bigquery-public-data`.stackoverflow.posts_questions
WHERE REGEXP_CONTAINS(tags, 'google')

Because Auto ML tokenizes text based on whitespace, we do a bit of preprocessing in SQL, to convert the | character to spaces, and then pass the data onto Auto ML.

Predictions

Two hours later (note the budget hours: the higher the budget, the better the model, typically, although if the model is not improving, Auto ML will cut the training short), we have a trained model.

We can do batch predictions with this model just as if it was a native BigQuery ML model:

SELECT * FROM ML.PREDICT(MODEL advdata.so_answered_automl,(
SELECT 'How to reverse string in BigQuery' AS title, 'google-cloud-bigquery' AS tags
UNION ALL
SELECT 'Check long string contains term' AS title, 'google-cloud-storage' AS tags
))

The result is:

This seems to make sense. The clear question tagged with the right product is more likely to be answered than a vague sentence tagged with the wrong product.

Why this is so awesome

What this adds to the general ML toolkit on Google Cloud is that we can now bring the power of SQL to carry out data preparation and then hand it off to Auto ML to do neural architecture search to build a sophisticated model, even a model that includes Natural Language Processing.

So we now have the ease-of-data preparation offered by BigQuery tied to the best-of-breed model accuracy offered by Auto ML Tables. Of course, we also get the data engineering capabilities that BigQuery offers, such as scheduled queries and large-scale batch predictions. What’s not to like?

Enjoy!

Sours: https://towardsdatascience.com/the-best-of-both-worlds-calling-auto-ml-from-bigquery-9dfd433a45d6
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Introduction

BigQuery ML (BQML) enables users to create and execute machine learning models in BigQuery by using SQL queries.

AutoML Tables lets you automatically build, analyze, and deploy state-of-the-art machine learning models using your own structured data, and explain prediction results. It’s useful for a wide range of machine learning tasks, such as asset valuations, fraud detection, credit risk analysis, customer retention prediction, analyzing item layouts in stores, solving comment section spam problems, quickly categorizing audio content, predicting rental demand, and more. (This blog post gives more detail on many of its capabilities).

Recently, BQML added support for AutoML Tables models (in Beta). This makes it easy to train Tables models on your BigQuery data using standard SQL, directly from the BigQuery UI (or API), and to evaluate and use the models for prediction directly via SQL as well.

In this post, we’ll take a look at how to do this, and show a few tips as well.

About the dataset and modeling task

The Cloud Public Datasets Program makes available public datasets that are useful for experimenting with machine learning. We’ll use data that is essentially a join of two public datasets stored in BigQuery: London Bike rentals and NOAA weather data, with some additional processing to clean up outliers and derive additional GIS and day-of-week fields. The table we’ll use is here: .

Using this dataset, we’ll build a regression model to predict the of a bike rental based on information about the start and end rental stations, the day of the week, the weather on that day, and other data. (If we were running a bike rental company, we could use these predictions—and their explanations—to help us anticipate demand and even plan how to stock each location).

Specifying training, eval, and test datasets

AutoML Tables will split the data you send it into its own training/test/validation sets.

Note: it’s also possible to specify the data split column as a BQML model creation option: . is one of the columns in the training data and should be either a timestamp or string column. See the documentation for more detail.

For BQML, we’ll split the data into a set to use for the training process, and reserve a ‘test’ dataset that AutoML training never sees. We don’t want to just grab a sequential slice of the table for each. There’s an easy way to accomplish this in a repeatable manner by using the Farm Hash algorithm, implemented as the BigQuery SQL function. We’d like to create a 90/10 split.

So, the query to generate training data will include a clause like this:

Similarly, the query to generate the ‘test’ set will use this clause:

Using this approach, we can build clauses with reproducible results that give us datasets with the split proportions we want.

Tables schema configuration and BQML

If you’ve used AutoML Tables, you may have noticed that after a dataset is ingested, it’s possible to adjust the inferred field (column) schema information. For example, you might have some fields with numeric values that you’d like to treat as categorical when you train your custom model. This is the case for our dataset, where we’d like to treat the numeric rental station IDs as categorical.

With BQML, it’s not currently possible to explicitly specify the schema for the model inputs, but for numerics that we want to treat as categorical, we can provide a strong hint by casting them to strings; then Tables will decide whether to treat such values as ‘text’ or ‘categorical’. So, in the SQL below, you’ll see that the , , and columns are all cast to strings.

Training the AutoML Tables model via BQML

To train the Tables model, we’ll pick the model type (since we want to predict , a numeric value. In the query, we’ll specify in the list (thus indicating the “label” column”, and set to , meaning that we’re budgeting one hour of training time. (The training process, which includes setup and teardown, etc., will typically take longer).

Here’s the resultant BigQuery query (to run it yourself, substitute your project id, dataset, and model name in the first line, then paste the query into the BigQuery UI query window in the Cloud Console):

Note the casts to and the use of as discussed above.

(If you’ve taken a look at the table, you might notice that the clause does not include the column. A previously-run AutoML Tables analysis of the global feature importance of the dataset fields indicated that had negligible impact, so we won’t use it for this model).

Evaluating your trained custom model

After the training has completed, you can view the evaluation metrics for your custom model, and also run your own evaluation query yourself. You can view the evaluation metrics generated during the training process by clicking on the model name in the BigQuery UI, then click on the “Evaluation” tab in the central panel. It will look something like this:

(At time of writing, some of this data is incomplete, but that will change soon).

The BigQuery SQL to run your own evaluation query for the trained model looks like this (again, substitute your own project, dataset, and model name):

Note that via the function, we’re using a different dataset for evaluation than we used for training. The evaluation results should look something like the following. The metrics will be a bit different from those above, since we’re using different data than AutoML Tables used for its eval split.

Did our schema hints help?

It’s interesting to check whether the schema hints (casting some of the numeric fields to strings) made a difference in model accuracy. To try this yourself, create another differently-named model as shown in the training section above, but for the clause, don’t include the casts to , e.g.:

Then, evaluate this second model (again substituting the details for your own project):

When I evaluated this second model, which kept the station IDs and day of week as numerics, the results showed that this model was somewhat less accurate:

Using your BQML AutoML Tables model for prediction

Once your model is trained, and you’ve ascertained it’s accurate enough, you can use it for prediction. Here’s an example of how to do that. Via the function, we’re drawing from our “test” split, but because the resultant dataset is large, we’re just grabbing a few rows (200) for the query below:

The prediction results will look something like this (click to see larger version):

Note that while the query above is “standalone”, you can of course access model prediction results as part of a larger BigQuery query too.

Summary

In this post, we showed how to train an AutoML Tables model using BQML, evaluate the model, and then use it for prediction— all from BigQuery. The BQML documentation has more detail on getting started, and resources for the other model types available through BQML.

Sours: https://amygdala.github.io/gcp_blog/ml/automl/bigquery/2020/07/14/bqml_tables.html
AutoML Tables

AutoML Tables is now generally available in BigQuery ML

Google’s cloud data warehouse, BigQuery, has enabled organizations around the world to accelerate their digital transformation and empower their data analysts to unlock actionable insights from their data. Using BigQuery ML, data analysts are able to create sophisticated machine learning models with just SQL and uncover predictive insights from their data much faster.  Today we are excited to announce the addition of the AutoML Tables model type to the list of supported ML models within BigQuery ML. The AutoML Tables model type, now generally available, integrates directly and seamlessly with our Vertex AI AutoML Tables offering, and enables teams to automatically build and deploy state-of-the-art machine learning models on structured data at massively increased speed and scale. BigQuery ML can improve AutoML models, as it transforms input variables into features for AutoML Tables by standardizing numeric columns, one-hot encoding non-numerical columns, extracting components from timestamp, and even expanding array and struct columns. It even does missing value imputation with approaches for numerical, categorial and timestamp columns. 

How does AutoML Tables build powerful, sophisticated models? Behind the scenes, AutoML does quite a lot of machine learning magic:

preprocesses the data

performs automatic feature engineering

model architecture search

model tuning

cross validation

automatic model selection and ensembling

Sours: https://dataintegration.info/automl-tables-is-now-generally-available-in-bigquery-ml

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CloudFest 2.0: BigQuery + AutoML

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