Hi @AmanJain1008,

Thank you for posting your question in the Databricks Community.

Could you kindly check whether you are able to reproduce the issue with the below code examples:

# Import Libraries
import pandas as pd
import numpy as np
import mlflow
import mlflow.sklearn

# Load Data
data = {'Feature1': np.random.rand(10), 'Feature2':np.random.rand(10), 'Label': np.random.randint(0,2,10)}
df = pd.DataFrame(data)

# Separating features and target variable
X = df[['Feature1','Feature2']]
y = df['Label']

import tensorflow as tf
# enabling autologging for TensorFlow
mlflow.tensorflow.autolog()

# Start an MLflow run with a specified run name
with mlflow.start_run(run_name="sample_run_2") as run:
  
  # Specify evaluations results and new process parameter values
  eval_result = 4
  new = 4

  # Log the evaluations results and new process parameter values
  mlflow.log_param("eval_result", eval_result)
  mlflow.log_param("new", new)
  
  # Train a model using TensorFlow
  model = tf.keras.Sequential([
      tf.keras.layers.Dense(10, input_shape=(X.shape[1],), activation='relu'),
      tf.keras.layers.Dense(1, activation='sigmoid'),
  ])
  
  model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  model.fit(X, y, epochs=10, batch_size=1)