Various ML tasks, currently covering:

• Classification
• Regression
• Clustering (k-means)
• Time-series Analysis (AR Models)
• Dimensionality Reduction (Autoencoding)
• Sequence Models (RNN and LSTMs)
• Image Analysis (CNN for Image Classification)
• Text Analysis (Text Classification with embeddings, CNN, and RNN)

How to use canned estimators to train ML models.

How to implement custom estimators (model_fn & EstimatorSpec).

A standard metadata-driven approach to build the model feature_column(s) including:

• numerical features
• categorical features with vocabulary,
• categorical features hash bucket, and
• categorical features with identity

Data input pipelines (input_fn) using:

• tf.estimator.inputs.pandas_input_fn,
• tf.train.string_input_producer, and
• tf.data.Dataset APIs to read both .csv and .tfrecords (tf.example) data files
• tf.contrib.timeseries.RandomWindowInputFn and WholeDatasetInputFn for time-series data
• Feature preprocessing and creation as part of reading data (input_fn), for example, sin, sqrt, polynomial expansion, fourier transform, log, boolean comparisons, euclidean distance, custom formulas, etc.

A standard approach to prepare wide (sparse) and deep (dense) feature_column(s) for Wide and Deep DNN Liner Combined Models

The use of normalizer_fn in numeric_column() to scale the numeric features using pre-computed statistics (for Min-Max or Standard scaling)

The use of weight_column in the canned estimators

Implicit Feature Engineering as part of defining feature_colum(s), including:

• crossing
• embedding
• indicators (encoding categorical features), and
• bucketization

How to use the tf.contrib.learn.experiment APIs to train, evaluate, and export models

Howe to use the tf.estimator.train_and_evaluate function (along with trainSpec & evalSpec) train, evaluate, and export models

How to serve exported model (export_savedmodel) using csv and json inputs