Derive insights from the images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more.

AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs. Build your own custom machine learning model in minutes.

Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services

BigQuery ML lets you create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by letting SQL practitioners build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data. Supported algorithms on BQML are:

Regression & Classification

• Linear & Logistic Regression: for forecasting; for example, the sales of an item on a given day. Labels are real-valued (they cannot be +/- infinity or NaN).

• Binary logistic regression
  for classification; for example, determining whether a customer will make a purchase. Labels must only have two possible values.

• Multiclass logistic regression for classification.
  These models can be used to predict multiple possible values such as whether an input is "low-value," "medium-value," or "high-value." Labels can have up to 50 unique values. In BigQuery ML, multiclass logistic regression training uses a multinomial classifier with a cross-entropy loss function.

• Boosted Trees
  Boosted Tree for creating XGBoost based classification and regression models.

• Deep Neural Networks
  Deep Neural Network (DNN) for creating TensorFlow-based Deep Neural Networks for classification and regression models.

• Wide-and-Deep Networks

• AutoML Table Models
  Used to create best-in-class models without feature engineering or model selection. AutoML Tables searches through a variety of model architectures to decide the best model.

Clustering

• K-Means:
  K-means clustering for data segmentation; for example, identifying customer segments. an unsupervised learning technique, so model training does not require labels nor split data for training or evaluation

Dimensionality Reduction

• PCA:

• Autoencoder:
  Used for creating Tensorflow-based BigQuery ML models with the support of sparse data representations. The models can be used in BigQuery ML for tasks such as unsupervised anomaly detection and non-linear dimensionality reduction.

Collaborative Filtering

• Matrix Factorization
  Used for creating product recommendation systems. You can create product recommendations using historical customer behavior, transactions, and product ratings and then use those recommendations for personalized customer experiences.

Time Series Forecasting

• ARIMA+
  Time series for performing time-series forecasts. You can use this feature to create millions of time series models and use them for forecasting. The model automatically handles anomalies, seasonality, and holidays.

Importing Models

• TensorFlow Models:
  TensorFlow model importing. This feature lets you create BigQuery ML models from previously trained TensorFlow models, then perform prediction in BigQuery ML.