online-learning.py

import numpy as np
from sklearn import linear_model

n_samples, n_features = 1, 500
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
clf = linear_model.SGDRegressor()

import time

start_time = time.time()
clf.partial_fit(X, y)
elapsed_time = time.time() - start_time
print(elapsed_time)

is there any similar way to define a keras sequential model ?

is there any similar way to define a keras sequential model ?

yes u can use keras sequential api.Link :- https://keras.io/api/models/sequential/

have you tried with the RIVER library? what is your opinion on it?