15. Deploy and Serve a Feature List

Deploy and Serve a Feature List for Model Predictions¶

Once you have a feature list ready, it's essential to serve these features effectively to make real-time or batch predictions using your machine learning model.

In this section, we'll take the feature list we previously crafted and explore two primary ways to serve its values:

REST API: Ideal for real-time predictions where you need instantaneous results. For instance, in applications where user interactions require immediate feedback based on model predictions.

Batch Processing: Best suited for scenarios where you have a bulk of data and don't require instant results.

Important Note for FeatureByte Enterprise Users¶

In Catalogs with Approval Flow enabled, moving features to production-ready status involves a comprehensive approval process.

This includes several evaluations, such as checking the feature's compliance with default cleaning operations and the feature job setting of its source tables. It also involves confirming the status of these tables and backtesting the feature job setting to prevent future training-serving inconsistencies. Additionally, essential details of the feature, particularly its feature definition file, are shared and subjected to a thorough review.

Activate Catalog¶

In [1]:

import featurebyte as fb
from datetime import datetime

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Credit Default Dataset SDK Tutorial"
catalog = fb.Catalog.activate(catalog_name)

import featurebyte as fb from datetime import datetime # Set your profile to the tutorial environment fb.use_profile("tutorial") catalog_name = "Credit Default Dataset SDK Tutorial" catalog = fb.Catalog.activate(catalog_name)

17:09:46 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
17:09:46 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
17:09:46 | INFO     | Using profile: staging
INFO    :featurebyte:Using profile: staging
17:09:46 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
INFO    :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml
17:09:46 | INFO     | Active profile: staging (https://staging.featurebyte.com/api/v1)
INFO    :featurebyte:Active profile: staging (https://staging.featurebyte.com/api/v1)
17:09:46 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
17:09:46 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
17:09:47 | INFO     | Catalog activated: Credit Default Dataset SDK Tutorial
INFO    :featurebyte.api.catalog:Catalog activated: Credit Default Dataset SDK Tutorial
16:13:11 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:13:11 | INFO     | No catalog activated.
16:13:11 | INFO     | Catalog activated: Grocery Dataset Tutorial

List feature lists in Catalog¶

In [2]:

catalog.list_feature_lists()

catalog.list_feature_lists()

Out[2]:

	id	name	num_feature	status	deployed	readiness_frac	online_frac	tables	entities	primary_entity	created_at
0	68ef63333e2dac375b630de7	40 features for Credit Default	40	DRAFT	False	0.0	0.0	[NEW_APPLICATION, CLIENT_PROFILE, BUREAU, INST...	[New Application, Client]	[New Application]	2025-10-15T09:02:51.346000

Get a feature list from Catalog¶

In [3]:

feature_list_name = "40 features for Credit Default"
simple_feature_list = catalog.get_feature_list(feature_list_name)

feature_list_name = "40 features for Credit Default" simple_feature_list = catalog.get_feature_list(feature_list_name)

Loading Feature(s) |████████████████████████████████████████| 40/40 [100%] in 0.

Deploy feature list¶

In [4]:

deployment_name = "Loan Application: simple feature list with 40 features"
# Create a deployment
deployment = simple_feature_list.deploy(
  deployment_name=deployment_name,
  make_production_ready=True,
  use_case_name="Loan Default by client",
)

deployment_name = "Loan Application: simple feature list with 40 features" # Create a deployment deployment = simple_feature_list.deploy( deployment_name=deployment_name, make_production_ready=True, use_case_name="Loan Default by client", )

Done! |████████████████████████████████████████| 100% in 9.1s (0.11%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         

In [5]:

# Enable deployment
deployment.enable()

# Enable deployment deployment.enable()

Done! |████████████████████████████████████████| 100% in 4:03.5 (0.00%/s)       
Done! |████████████████████████████████████████| 100% in 1:49.2 (0.01%/s)       

In [6]:

# Check that the deployment is enabled
catalog.list_deployments()

# Check that the deployment is enabled catalog.list_deployments()

Out[6]:

	id	name	feature_list_name	feature_list_version	num_feature	enabled
0	68ef64e5c712940cada35ec3	Loan Application: simple feature list with 40 ...	40 features for Credit Default	V251015	40	True

In [7]:

# Check status of the feature list
print("simple_feature_list.status:", simple_feature_list.status)

# Check status of the feature list print("simple_feature_list.status:", simple_feature_list.status)

simple_feature_list.status: DEPLOYED

Create view of recent applications¶

Let's create a view of the last 24 hours applications.

In [8]:

# Get view of new applications
application_view = catalog.get_view("NEW_APPLICATION")

# Get view of new applications application_view = catalog.get_view("NEW_APPLICATION")

In [9]:

from datetime import datetime, timedelta, timezone
import pandas as pd
date_time_now_utc = datetime.now(timezone.utc).replace(tzinfo=None)
now_less_24h = date_time_now_utc - timedelta(hours=24)
cond = application_view.application_time > pd.Timestamp(now_less_24h)
recent_application_view = application_view[cond]

from datetime import datetime, timedelta, timezone import pandas as pd date_time_now_utc = datetime.now(timezone.utc).replace(tzinfo=None) now_less_24h = date_time_now_utc - timedelta(hours=24) cond = application_view.application_time > pd.Timestamp(now_less_24h) recent_application_view = application_view[cond]

In [10]:

recent_application_view.preview()

recent_application_view.preview()

Out[10]:

	SK_ID_CURR	ClientID	CONTRACT_TYPE	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	TYPE_SUITE	REGION_POPULATION_RELATIVE	application_time	...	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100168	100168	Cash loans	157500.0	266652.0	16443.0	202500.0	Unaccompanied	0.011657	2025-10-14 20:45:39	...	0	0	0	0	0.0	0.0	0.0	0.0	0.0	4.0
1	104058	104058	Cash loans	157500.0	521568.0	22054.5	360000.0	Unaccompanied	0.020246	2025-10-15 04:21:09	...	0	0	0	0	0.0	0.0	0.0	0.0	2.0	0.0
2	110958	110958	Cash loans	90000.0	183784.5	16983.0	148500.0	Family	0.020246	2025-10-15 02:45:32	...	0	0	0	0	0.0	0.0	0.0	0.0	1.0	0.0
3	114842	114842	Cash loans	67500.0	119893.5	12586.5	103500.0	Unaccompanied	0.008230	2025-10-14 23:25:12	...	0	0	0	0	1.0	0.0	0.0	0.0	0.0	1.0
4	115933	115933	Cash loans	112500.0	364896.0	28957.5	315000.0	Unaccompanied	0.009549	2025-10-15 08:53:52	...	0	0	0	0	0.0	0.0	0.0	0.0	0.0	1.0
5	121321	121321	Cash loans	135000.0	601470.0	26622.0	450000.0	Spouse, partner	0.007120	2025-10-14 15:07:07	...	0	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN
6	122609	122609	Cash loans	112500.0	260640.0	26838.0	225000.0	Unaccompanied	0.009549	2025-10-15 03:58:16	...	0	0	0	0	0.0	0.0	0.0	0.0	0.0	2.0
7	128760	128760	Cash loans	67500.0	254799.0	20259.0	193500.0	Unaccompanied	0.006305	2025-10-14 10:35:34	...	0	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN
8	129135	129135	Cash loans	225000.0	746280.0	42970.5	675000.0	Unaccompanied	0.008575	2025-10-14 21:03:11	...	0	0	0	0	NaN	NaN	NaN	NaN	NaN	NaN
9	134654	134654	Cash loans	76500.0	74628.0	8167.5	67500.0	Unaccompanied	0.030755	2025-10-14 12:34:18	...	0	0	0	0	0.0	0.0	0.0	0.0	1.0	2.0

10 rows × 111 columns

In [11]:

# Check primary entity of the deployed feature list to obtain its serving name
simple_feature_list.primary_entity

# Check primary entity of the deployed feature list to obtain its serving name simple_feature_list.primary_entity

Out[11]:

[<featurebyte.api.entity.Entity at 0x3008442c0>
 {
   'name': 'New Application',
   'created_at': '2025-10-15T08:58:25.635000',
   'updated_at': '2025-10-15T08:58:27.217000',
   'description': None,
   'serving_names': [
     'SK_ID_CURR'
   ],
   'catalog_name': 'Credit Default Dataset SDK Tutorial'
 }]

Compute batch feature table¶

In [12]:

# Get deployment
deployment = catalog.get_deployment(deployment_name)

# Get deployment deployment = catalog.get_deployment(deployment_name)

In [13]:

# Compute batch features
batch_features = deployment.compute_batch_feature_table(
    batch_request_table=recent_application_view[["SK_ID_CURR"]],
    batch_feature_table_name = 
    f"{feature_list_name} with Latest Applications latest 24 hours at "
    + date_time_now_utc.strftime("%Y%m%d:%H%M"),
    point_in_time=date_time_now_utc,
)

# Compute batch features batch_features = deployment.compute_batch_feature_table( batch_request_table=recent_application_view[["SK_ID_CURR"]], batch_feature_table_name = f"{feature_list_name} with Latest Applications latest 24 hours at " + date_time_now_utc.strftime("%Y%m%d:%H%M"), point_in_time=date_time_now_utc, )

Done! |████████████████████████████████████████| 100% in 1:31.4 (0.01%/s)       
Done! |████████████████████████████████████████| 100% in 12.1s (0.08%/s)        

In [14]:

# List observation tables
catalog.list_batch_feature_tables()

# List observation tables catalog.list_batch_feature_tables()

Out[14]:

	id	name	feature_store_name	batch_request_table_name	shape	created_at
0	68ef65e5c712940cada35ec4	40 features for Credit Default with Latest App...	playground	None	[114, 41]	2025-10-15T09:15:42.879000

In [15]:

# Convert to pandas 
batch_features.to_pandas()

# Convert to pandas batch_features.to_pandas()

Downloading table |████████████████████████████████████████| 114/114 [100%] in 0

Out[15]:

	SK_ID_CURR	NEW_APPLICATION_EXT_SOURCE_2	NEW_APPLICATION_EXT_SOURCE_3	NEW_APPLICATION_EXT_SOURCE_1	NEW_APPLICATION_Credit-Goods_Difference	NEW_APPLICATION_AMT_ANNUITY_To_AMT_CREDIT	NEW_APPLICATION_DAYS_EMPLOYED	CLIENT_GENDER	CLIENT_Max_of_Active_Cr_active_BureauReportedCredits_AMT_CREDIT_SUM_DEBT_To_AMT_CREDIT_SUMs_104w	CLIENT_EDUCATION_TYPE	...	CLIENT_Installments_AMT_PAYMENTs_by_PriorApplication_PAYMENT_TYPE_24cMo	CLIENT_Time_To_Latest_Approved_Contract_status_PriorApplication_last_due_1st_version_timestamp_104w	NEW_APPLICATION_FLAG_DOCUMENT_3	NEW_APPLICATION_FLOORSMAX_MODE	CLIENT_Max_of_Installments_Days_Difference_Actual_vs_Scheduleds_24cMo	CLIENT_Max_of_Installments_PriorApplication_AMT_ANNUITY_To_AMT_CREDITs_6cMo	CLIENT_Std_of_Credit_card_monthly_balance_records_CNT_DRAWINGS_ATM_CURRENTs_24cMo	CLIENT_PriorApplications_AMT_CREDITs_by_PriorApplication_NFLAG_INSURED_ON_APPROVAL_52w	CLIENT_Std_of_BureauReportedCredits_Available_Credits_26w	CLIENT_Max_of_Consumer_credit_Cr_type_BureauReportedCredits_AMT_CREDIT_SUM_DEBT_To_AMT_CREDIT_SUMs_52w
0	100168	0.708939	0.746300	NaN	64152.0	0.061665	-13294.0	F	NaN	Higher education	...	{'Cash through the bank': 2649137.94}	-675.480218	1	0.2083	-2.0	0.037060	NaN	None	NaN	NaN
1	104058	0.498816	0.535276	0.626733	161568.0	0.042285	NaN	F	NaN	Secondary / secondary special	...	{'Cash through the bank': 40839.795}	216.203462	0	NaN	-9.0	NaN	NaN	None	0.0000	0.000000
2	110958	0.049740	NaN	0.226673	35284.5	0.092407	-778.0	F	0.750972	Higher education	...	{'Cash through the bank': 90639.36}	338.269863	1	NaN	-10.0	NaN	NaN	None	0.0000	0.750972
3	114842	0.495776	0.236611	0.472694	16393.5	0.104981	-824.0	F	0.000000	Secondary / secondary special	...	{'Cash through the bank': 81448.42499999999}	131.408983	1	NaN	-8.0	0.103480	NaN	None	33750.0000	0.000000
4	115933	0.731233	0.728141	0.518943	49896.0	0.079358	-1817.0	F	NaN	Secondary / secondary special	...	{'Cash through the bank': 82994.67}	-8.985924	1	NaN	-7.0	0.099134	NaN	{'1': 76108.5}	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
109	439334	0.491310	0.821443	0.385260	0.0	0.056085	-2390.0	M	NaN	Secondary / secondary special	...	None	NaN	1	NaN	NaN	NaN	NaN	None	NaN	NaN
110	439448	0.599798	0.466864	0.694343	38493.0	0.052120	-291.0	M	1.000000	Secondary / secondary special	...	{'Cash through the bank': 109877.85}	-232.870878	1	0.1667	3.0	0.107731	NaN	{'1': 47970.0, '__MISSING__': 0.0}	25390.0125	0.925269
111	443090	0.700517	0.280790	NaN	0.0	0.079300	-1311.0	M	0.858035	Secondary / secondary special	...	{'Cash through the bank': 422228.52}	NaN	0	NaN	-1.0	NaN	NaN	None	56787.9975	0.858035
112	447504	0.614169	0.616122	0.769972	0.0	0.048100	NaN	F	0.380025	Secondary / secondary special	...	{'XNA': 122001.75}	-27.126306	0	NaN	-16.0	0.095977	NaN	{'1': 127116.0}	39820.5000	0.291717
113	453837	0.601172	0.754406	NaN	30411.0	0.061923	NaN	F	NaN	Secondary / secondary special	...	{'Cash through the bank': 797126.3999999999}	130.112444	1	0.0417	0.0	0.038090	NaN	{'__MISSING__': 0.0}	NaN	NaN

114 rows × 41 columns

Downloading table |████████████████████████████████████████| 500/500 [100%] in 0

Out[15]:

	GROCERYCUSTOMERGUID	PRODUCTGROUP	CUSTOMER_Age_band	CUSTOMER_Latest_invoice_Amount	CUSTOMER_Count_of_invoice_14d	CUSTOMER_Avg_of_invoice_Amount_14d	CUSTOMER_Std_of_invoice_Amount_14d	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d	CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp
0	14f94723-4cae-4041-a11a-a044df1a5a5f	Fromages	60-64	14.28	NaN	NaN	NaN	1.000000	0.373275	NaN	1527.861052
1	d4559f7d-eb28-42c6-b47d-847de24952c2	Fromages	65-69	20.04	NaN	NaN	NaN	NaN	0.370545	NaN	2177.017718
2	1d363f8c-88f4-49eb-a107-4f1e2fbe6722	Fromages	25-29	73.16	NaN	NaN	NaN	NaN	0.781391	NaN	1118.766885
3	768e57df-7513-412b-a466-effd270ad6a6	Fromages	65-69	7.16	3.0	8.246667	4.395121	-0.644055	0.650126	NaN	1357.819107
4	48f6e550-ce63-457e-ad14-d511ee934661	Fromages	40-44	24.62	1.0	24.620000	0.000000	-0.219845	0.753619	NaN	475.681329
...	...	...	...	...	...	...	...	...	...	...	...
495	c1b3224a-5dbb-427d-87d3-3882c6bb1278	Fromages	50-54	6.69	NaN	NaN	NaN	NaN	0.104953	NaN	NaN
496	261d2979-0568-4544-8be0-32355049e565	Fromages	65-69	2.00	NaN	NaN	NaN	NaN	0.317982	NaN	NaN
497	825ee709-2f85-49e2-bec7-27bea06efe3a	Fromages	45-49	3.00	2.0	5.995000	2.995000	-0.990561	0.375129	NaN	NaN
498	ea68ce6b-b3c3-4ba5-a471-f11b1c5bd892	Fromages	45-49	4.50	NaN	NaN	NaN	NaN	NaN	NaN	NaN
499	b8b00613-2e48-4eed-8971-61718990a994	Fromages	45-49	10.00	2.0	6.000000	4.000000	1.264961	0.579119	NaN	NaN

500 rows × 11 columns

In [16]:

# download parquet file
batch_features.download()

# download parquet file batch_features.download()

Downloading table |████████████████████████████████████████| 114/114 [100%] in 0

Out[16]:

PosixPath('BATCH_FEATURE_TABLE_68ef65eb498f1c7d8ca81659.parquet')

Out[16]:

PosixPath('BATCH_FEATURE_TABLE_666959284c98806ac301afa0.parquet')

In [17]:

# delete if not needed any more
batch_features.delete()

# delete if not needed any more batch_features.delete()

Done! |████████████████████████████████████████| 100% in 9.2s (0.11%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         

Disable Deployment¶

In [18]:

deployment = catalog.get_deployment(deployment_name)

deployment = catalog.get_deployment(deployment_name)

In [19]:

# Disable deployment
deployment.disable()

# Disable deployment deployment.disable()

Done! |████████████████████████████████████████| 100% in 24.3s (0.04%/s)        
Done! |████████████████████████████████████████| 100% in 12.1s (0.08%/s)        

In [20]:

# The deployment is still part of the catalog but disabled
catalog.list_deployments()

# The deployment is still part of the catalog but disabled catalog.list_deployments()

Out[20]:

	id	name	feature_list_name	feature_list_version	num_feature	enabled
0	68ef64e5c712940cada35ec3	Loan Application: simple feature list with 40 ...	40 features for Credit Default	V251015	40	False

Concepts in this tutorial¶

• Feature list deployment
• REST API and batch serving
• Feature jobs

SDK reference for¶

• Deployment
• Batch Feature Table
• FeatureList.deploy()
• Deployment.enable()
• Deployment.get_online_serving_code()
• Deployment.compute batch feature_table()

In [ ]: