eval_config = tfma.EvalConfig(
		model_specs=[
			# This assumes a serving model with signature 'serving_default'. If
			# using estimator based EvalSavedModel, add signature_name: 'eval' and
			# remove the label_key.
			tfma.ModelSpec(label_key='Label',
			               model_type=constants.TF_GENERIC
			               )
		],
		metrics_specs=[
			tfma.MetricsSpec(
				# The metrics added here are in addition to those saved with the
				# model (assuming either a keras model or EvalSavedModel is used).
				# Any metrics added into the saved model (for example using
				# model.compile(..., metrics=[...]), etc) will be computed
				# automatically.
				metrics=[
					tfma.MetricConfig(class_name='ExampleCount'),
					tfma.MetricConfig(
						class_name='BinaryAccuracy',
						threshold=tfma.MetricThreshold(
							value_threshold=tfma.GenericValueThreshold(
								lower_bound={'value': 0.5}),
							change_threshold=tfma.GenericChangeThreshold(
								direction=tfma.MetricDirection.HIGHER_IS_BETTER,
								absolute={'value': -1e-10})))
				]
			)
		],
		slicing_specs=[
			# An empty slice spec means the overall slice, i.e. the whole dataset.
			tfma.SlicingSpec(),
			# Data can be sliced along a feature column. In this case, data is
			# sliced along feature column trip_start_hour.
			# tfma.SlicingSpec(feature_keys=['trip_start_hour'])
		])