diff --git a/airflow_pipeline_example/Dockerfile b/airflow_pipeline_example/Dockerfile
deleted file mode 100644
index 52d388c..0000000
--- a/airflow_pipeline_example/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM debian:buster-slim
-
-MAINTAINER Theja Tulabandhula
-
-RUN apt-get update \
-  && apt-get install -y python3-pip python3-dev \
-  && cd /usr/local/bin \
-  && ln -s /usr/bin/python3 python
-
-RUN pip3 install pandas-gbq numpy
-
-RUN pip3 install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-COPY . .
-
-RUN chmod +x run_transient_pipeline.sh
-
-CMD ./run_transient_pipeline.sh
\ No newline at end of file
diff --git a/airflow_pipeline_example/airflow_install.sh b/airflow_pipeline_example/airflow_install.sh
deleted file mode 100755
index a190561..0000000
--- a/airflow_pipeline_example/airflow_install.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-# airflow needs a home, ~/airflow is the default,
-# but you can lay foundation somewhere else if you prefer
-# (optional)
-export AIRFLOW_HOME=~/airflow
-
-AIRFLOW_VERSION=2.1.4
-PYTHON_VERSION="$(python --version | cut -d " " -f 2 | cut -d "." -f 1-2)"
-# For example: 3.6
-CONSTRAINT_URL="https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt"
-# For example: https://raw.githubusercontent.com/apache/airflow/constraints-2.1.4/constraints-3.6.txt
-pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"
-
-# initialize the database
-airflow db init
-
-airflow users create \
-	    --username admin \
-	        --firstname Peter \
-		    --lastname Parker \
-		        --role Admin \
-			    --email spiderman@superhero.org
-
-# start the web server, default port is 8080
-airflow webserver --port 8080
-
-# start the scheduler
-# open a new terminal or else run webserver with ``-D`` option to run it as a daemon
-airflow scheduler
-
-# visit localhost:8080 in the browser and use the admin account you just
-# created to login. Enable the example_bash_operator dag in the home page
diff --git a/airflow_pipeline_example/recommend_airflow.py b/airflow_pipeline_example/recommend_airflow.py
deleted file mode 100644
index f65b1ba..0000000
--- a/airflow_pipeline_example/recommend_airflow.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# [START import_module]
-from datetime import timedelta
-
-# The DAG object; we'll need this to instantiate a DAG
-from airflow import DAG
-# Operators; we need this to operate!
-from airflow.operators.bash_operator import BashOperator
-from airflow.utils.dates import days_ago
-
-# [END import_module]
-
-# [START default_args]
-# These args will get passed on to each operator
-# You can override them on a per-task basis during operator initialization
-default_args = {
-    'owner': 'airflow',
-    'depends_on_past': False,
-    'start_date': days_ago(2),
-    'email': ['myself@theja.org'],
-    'email_on_failure': False,
-    'email_on_retry': False,
-    'retries': 1,
-    'retry_delay': timedelta(minutes=5),
-    # 'queue': 'bash_queue',
-    # 'pool': 'backfill',
-    # 'priority_weight': 10,
-    # 'end_date': datetime(2016, 1, 1),
-    # 'wait_for_downstream': False,
-    # 'dag': dag,
-    # 'sla': timedelta(hours=2),
-    # 'execution_timeout': timedelta(seconds=300),
-    # 'on_failure_callback': some_function,
-    # 'on_success_callback': some_other_function,
-    # 'on_retry_callback': another_function,
-    # 'sla_miss_callback': yet_another_function,
-    # 'trigger_rule': 'all_success'
-}
-# [END default_args]
-
-# [START instantiate_dag]
-dag = DAG(
-    'recommend-pipeline',
-    default_args=default_args,
-    description='Run the transient training pipeline',
-    schedule_interval=timedelta(days=1),
-)
-# [END instantiate_dag]
-
-t1 = BashOperator(
-    task_id='docker-pipeline-run',
-    bash_command='docker run recommend_pipeline',
-    dag=dag,
-)
-
-
-# [START documentation]
-dag.doc_md = __doc__
-
-t1.doc_md = """\
-#### Transient Pipeline
-Downloads movielens-100k, trains a recommendation model and saves top 10 recommendations to Google BigQuery.
-"""
-# [END documentation]
-
-
-t1
-# [END tutorial]
diff --git a/airflow_pipeline_example/recommend_pytorch_inf.py b/airflow_pipeline_example/recommend_pytorch_inf.py
deleted file mode 100644
index 5e43a88..0000000
--- a/airflow_pipeline_example/recommend_pytorch_inf.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from recommend_pytorch_train import MF
-from surprise import Dataset
-import numpy as np
-import torch
-import torch.nn as nn
-import pandas as pd
-import pprint
-
-
-def get_top_n(model, testset, trainset, uid_input, movies_df, n=10):
-
-    preds = []
-    try:
-        uid_input = int(trainset.to_inner_uid(uid_input))
-    except KeyError:
-        return preds
-
-    # First map the predictions to each user.
-    for uid, iid, _ in testset:  # inefficient
-        try:
-            uid_internal = int(trainset.to_inner_uid(uid))
-        except KeyError:
-            continue
-        if uid_internal == uid_input:
-            try:
-                iid_internal = int(trainset.to_inner_iid(iid))
-                movie_name = movies_df.loc[int(iid), 'name']
-                preds.append((iid, movie_name, float(
-                    model(torch.tensor([[uid_input, iid_internal]])))))
-            except KeyError:
-                pass
-    # Then sort the predictions for each user and retrieve the k highest ones
-    if preds is not None:
-        preds.sort(key=lambda x: x[1], reverse=True)
-        if len(preds) > n:
-            preds = preds[:n]
-    return preds
-
-
-def get_previously_seen(trainset, uid, movies_df):
-    seen = []
-    for (iid, _) in trainset.ur[int(uid)]:
-        try:
-            seen.append(movies_df.loc[int(iid), 'name'])
-        except KeyError:
-            pass
-        if len(seen) > 10:
-            break
-    return seen
-
-
-def main():
-    # Data
-    movies_df = pd.read_csv('../data/ml-1m/movies.dat', sep="::",
-                            header=None, engine='python')
-    movies_df.columns = ['iid', 'name', 'genre']
-    movies_df.set_index('iid', inplace=True)
-    data = Dataset.load_builtin('ml-1m')
-    trainset = data.build_full_trainset()
-    testset = trainset.build_anti_testset()
-
-    k = 100  # latent dimension
-    c_bias = 1e-6
-    c_vector = 1e-6
-
-    model = MF(trainset.n_users, trainset.n_items,
-               k=k, c_bias=c_bias, c_vector=c_vector)
-    model.load_state_dict(torch.load('../data/models/recommendation_model_pytorch.pkl'))
-    model.eval()
-
-    # Print the recommended items for sample users
-    sample_users = list(set([x[0] for x in testset]))[:4]
-
-    for uid in sample_users:
-
-        print('User:', uid)
-        print('\n')
-
-        print('\tSeen:')
-        seen = get_previously_seen(trainset, uid, movies_df)
-        pprint.pprint(seen)
-        print('\n')
-
-        print('\tRecommendations:')
-        recommended = get_top_n(model, testset, trainset, uid, movies_df, n=10)
-        pprint.pprint([x[1] for x in recommended])
-        print('\n')
-
-
-if __name__ == "__main__":
-    main()
diff --git a/airflow_pipeline_example/recommend_pytorch_to_database.py b/airflow_pipeline_example/recommend_pytorch_to_database.py
deleted file mode 100644
index f5e6ed2..0000000
--- a/airflow_pipeline_example/recommend_pytorch_to_database.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from recommend_pytorch_train import MF
-from recommend_pytorch_inf import get_top_n
-import torch
-import pandas as pd
-import surprise
-import datetime
-import time
-from google.oauth2 import service_account
-import pandas_gbq
-
-def get_model_from_disk():
-    start_time = time.time()
-
-    # data preload
-    data = surprise.Dataset.load_builtin('ml-1m')
-    trainset = data.build_full_trainset()
-    testset = trainset.build_anti_testset()
-    movies_df = pd.read_csv('./movies.dat',
-                            sep="::", header=None, engine='python')
-    movies_df.columns = ['iid', 'name', 'genre']
-    movies_df.set_index('iid', inplace=True)
-
-    # model preload
-    k = 100  # latent dimension
-    c_bias = 1e-6
-    c_vector = 1e-6
-    model = MF(trainset.n_users, trainset.n_items,
-               k=k, c_bias=c_bias, c_vector=c_vector)
-    model.load_state_dict(torch.load(
-        './recommendation_model_pytorch.pkl'))  # TODO: prevent overwriting
-    model.eval()
-
-    print('Model and data preloading completed in ', time.time()-start_time)
-
-    return model, testset, trainset, movies_df
-
-
-def get_predictions(model, testset, trainset, movies_df):
-
-    # save the recommended items for a given set of users
-    sample_users = list(set([x[0] for x in testset]))[:4]
-
-
-    df_list = []
-    for uid in sample_users:
-        recommended = get_top_n(model, testset, trainset, uid, movies_df, n=10)
-        df_list.append(pd.DataFrame(data={'uid':[uid]*len(recommended),
-                                    'recommended': [x[1] for x in recommended]},
-            columns=['uid','recommended']))
-
-    df = pd.concat(df_list, sort=False)
-    df['pred_time'] = str(datetime.datetime.now())
-    return df
-
-def upload_to_bigquery(df):
-    #Send predictions to BigQuery
-    #requires a credential file in the current working directory
-    table_id = "movie_recommendation_service.predicted_movies"
-    project_id = "authentic-realm-276822"
-    credentials = service_account.Credentials.from_service_account_file('../model-user.json')
-    pandas_gbq.to_gbq(df, table_id, project_id=project_id, if_exists = 'replace', credentials=credentials)
-
-if __name__ == '__main__':
-    model, testset, trainset, movies_df = get_model_from_disk()
-    df = get_predictions(model, testset, trainset, movies_df)
-    print(df)
-    upload_to_bigquery(df)
diff --git a/airflow_pipeline_example/recommend_pytorch_train.py b/airflow_pipeline_example/recommend_pytorch_train.py
deleted file mode 100644
index 72e3604..0000000
--- a/airflow_pipeline_example/recommend_pytorch_train.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# https://github.com/NicolasHug/Surprise
-# can be replaced by explicitly importing the movielens data
-from surprise import Dataset
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from sklearn.utils import shuffle
-
-class Loader():
-    current = 0
-
-    def __init__(self, x, y, batchsize=1024, do_shuffle=True):
-        self.shuffle = shuffle
-        self.x = x
-        self.y = y
-        self.batchsize = batchsize
-        self.batches = range(0, len(self.y), batchsize)
-        if do_shuffle:
-            # Every epoch re-shuffle the dataset
-            self.x, self.y = shuffle(self.x, self.y)
-
-    def __iter__(self):
-        # Reset & return a new iterator
-        self.x, self.y = shuffle(self.x, self.y, random_state=0)
-        self.current = 0
-        return self
-
-    def __len__(self):
-        # Return the number of batches
-        return int(len(self.x) / self.batchsize)
-
-    def __next__(self):
-        n = self.batchsize
-        if self.current + n >= len(self.y):
-            raise StopIteration
-        i = self.current
-        xs = torch.from_numpy(self.x[i:i + n])
-        ys = torch.from_numpy(self.y[i:i + n])
-        self.current += n
-        return (xs, ys)
-
-
-class MF(nn.Module):
-
-    def __init__(self, n_user, n_item, k=18, c_vector=1.0, c_bias=1.0):
-        super(MF, self).__init__()
-        self.k = k
-        self.n_user = n_user
-        self.n_item = n_item
-        self.c_bias = c_bias
-        self.c_vector = c_vector
-
-        self.user = nn.Embedding(n_user, k)
-        self.item = nn.Embedding(n_item, k)
-
-        # We've added new terms here:
-        self.bias_user = nn.Embedding(n_user, 1)
-        self.bias_item = nn.Embedding(n_item, 1)
-        self.bias = nn.Parameter(torch.ones(1))
-
-    def forward(self, train_x):
-        user_id = train_x[:, 0]
-        item_id = train_x[:, 1]
-        vector_user = self.user(user_id)
-        vector_item = self.item(item_id)
-
-        # Pull out biases
-        bias_user = self.bias_user(user_id).squeeze()
-        bias_item = self.bias_item(item_id).squeeze()
-        biases = (self.bias + bias_user + bias_item)
-
-        ui_interaction = torch.sum(vector_user * vector_item, dim=1)
-
-        # Add bias prediction to the interaction prediction
-        prediction = ui_interaction + biases
-        return prediction
-
-    def loss(self, prediction, target):
-
-        def l2_regularize(array):
-            loss = torch.sum(array**2)
-            return loss
-
-        loss_mse = F.mse_loss(prediction, target.squeeze())
-
-        # Add new regularization to the biases
-        prior_bias_user = l2_regularize(self.bias_user.weight) * self.c_bias
-        prior_bias_item = l2_regularize(self.bias_item.weight) * self.c_bias
-
-        prior_user = l2_regularize(self.user.weight) * self.c_vector
-        prior_item = l2_regularize(self.item.weight) * self.c_vector
-        total = loss_mse + prior_user + prior_item + prior_bias_user + prior_bias_item
-        return total
-
-
-def main():
-    # Data
-    data = Dataset.load_builtin('ml-1m')
-    trainset = data.build_full_trainset()
-    uir = np.array([x for x in trainset.all_ratings()])
-    train_x = test_x = uir[:, :2].astype(np.int64)  # for simplicity
-    train_y = test_y = uir[:, 2].astype(np.float32)
-
-    # Parameters
-    lr = 5e-3
-    k = 100  # latent dimension
-    c_bias = 1e-6
-    c_vector = 1e-6
-    batchsize = 1024
-    num_epochs = 40
-
-    model = MF(trainset.n_users, trainset.n_items,
-               k=k, c_bias=c_bias, c_vector=c_vector)
-    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
-
-
-    for epoch in range(num_epochs):
-        dataloader = Loader(train_x, train_y, batchsize=batchsize)
-        itr = 0
-        for batch in dataloader:
-            itr += 1
-            prediction = model(batch[0])
-            loss = model.loss(prediction, batch[1])
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-            if itr % 100 == 0:
-                print(f"epoch: {epoch}. iteration: {itr}. training loss: {loss}")
-
-    torch.save(model.state_dict(),
-               "../data/models/recommendation_model_pytorch.pkl")
-
-
-if __name__ == '__main__':
-    main()
diff --git a/airflow_pipeline_example/run_transient_pipeline.sh b/airflow_pipeline_example/run_transient_pipeline.sh
deleted file mode 100644
index b906195..0000000
--- a/airflow_pipeline_example/run_transient_pipeline.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/bash
-
-export GOOGLE_APPLICATION_CREDENTIALS=/model-user.json
-python recommend_pytorch_to_database.py
\ No newline at end of file
diff --git a/airflow_setup/setup.sh b/airflow_setup/setup.sh
new file mode 100755
index 0000000..75c469c
--- /dev/null
+++ b/airflow_setup/setup.sh
@@ -0,0 +1,10 @@
+AIRFLOW_VERSION=2.10.5
+
+# Extract the version of Python you have installed. If you're currently using a Python version that is not supported by Airflow, you may want to set this manually.
+# See above for supported versions.
+PYTHON_VERSION="$(python -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')"
+
+CONSTRAINT_URL="https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt"
+# For example this would install 2.10.5 with python 3.8: https://raw.githubusercontent.com/apache/airflow/constraints-2.10.5/constraints-3.8.txt
+
+pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"
\ No newline at end of file
diff --git a/cron_example/date_job_every_minute.txt b/cron_example/date_job_every_minute.txt
index 9b1c40b..f12ee39 100644
--- a/cron_example/date_job_every_minute.txt
+++ b/cron_example/date_job_every_minute.txt
@@ -19,3 +19,5 @@ Wed Feb 14 04:01:01 PM CST 2024
 Wed Feb 14 04:02:01 PM CST 2024
 Wed Feb 14 04:03:01 PM CST 2024
 Wed Feb 14 04:04:01 PM CST 2024
+Wed Feb 12 22:17:01 UTC 2025
+Wed Feb 12 22:18:01 UTC 2025
diff --git a/cron_example/run_this_job.sh b/cron_example/run_this_job.sh
index d6f79cc..59d381a 100755
--- a/cron_example/run_this_job.sh
+++ b/cron_example/run_this_job.sh
@@ -1 +1 @@
-date >> /home/theja/class/mlops-code-examples/cron_example/date_job_every_minute.txt
+date >> /home/theja/mlops-code-examples/cron_example/date_job_every_minute.txt
diff --git a/cron_example/run_this_job.txt b/cron_example/run_this_job.txt
index 3500d6b..8b3b3da 100644
--- a/cron_example/run_this_job.txt
+++ b/cron_example/run_this_job.txt
@@ -4,5 +4,5 @@ Step 1: open cron editor on the command line with `crontab -e`
 
 Step 2: insert the following line (replace localmachine with your username)
 
-* * * * * /home/localmachine/run_this_job.sh
+* * * * * /home/theja/mlops-code-examples/cron_example/run_this_job.sh
 
diff --git a/flask_examples/flask_example_regression/flask_simple_regression_service.py b/flask_examples/flask_example_regression/flask_simple_regression_service.py
index a5c0f33..6a8e578 100644
--- a/flask_examples/flask_example_regression/flask_simple_regression_service.py
+++ b/flask_examples/flask_example_regression/flask_simple_regression_service.py
@@ -1,5 +1,6 @@
 from flask import Flask, jsonify, request
 import flask
+import os
 
 def model(x):
     return 2*x+1
@@ -10,6 +11,19 @@ def model(x):
 def hello():
     return f"flask version: {flask.__version__}"
 
+"""
+Endpoint to make a prediction based on the input parameter 'x'.
+
+This function handles GET requests to the /mypredict route. It checks if the 
+'x' parameter is present in the request arguments. If 'x' is present, it attempts 
+to convert 'x' to a float and pass it to the model for prediction. The result is 
+returned as a JSON response containing the input and the prediction. If 'x' is 
+not present or an error occurs during processing, an error message is returned.
+
+Returns:
+    Response: A JSON response containing the input and prediction if successful, 
+              or an error message if 'x' is not provided or an error occurs.
+"""
 @app.route("/mypredict", methods=["GET"])
 def predict():
     # check if x is in the arguments
@@ -24,4 +38,7 @@ def predict():
 
 
 if __name__ == '__main__':
-    app.run()
+    host = os.getenv('FLASK_RUN_HOST', '127.0.0.1')
+    port = int(os.getenv('FLASK_RUN_PORT', 5000))
+
+    app.run(host=host, port=port)
diff --git a/flask_examples/flask_example_regression/requirements.txt b/flask_examples/flask_example_regression/requirements.txt
new file mode 100644
index 0000000..174be8e
--- /dev/null
+++ b/flask_examples/flask_example_regression/requirements.txt
@@ -0,0 +1 @@
+flask==3.1.0
\ No newline at end of file
diff --git a/mlflow_example/mlflow_example.py b/mlflow_example/mlflow_example.py
index 12c6e22..70bc9d0 100644
--- a/mlflow_example/mlflow_example.py
+++ b/mlflow_example/mlflow_example.py
@@ -1,11 +1,9 @@
 import mlflow
 from mlflow.models import infer_signature
-
-import pandas as pd
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from sklearn.metrics import accuracy_score
 
 
 # Load the Iris dataset
@@ -19,7 +17,7 @@
 # Define the model hyperparameters
 params = {
     "solver": "lbfgs",
-    "max_iter": 1000,
+    "max_iter": 10,
     "multi_class": "auto",
     "random_state": 8888,
 }
@@ -39,7 +37,7 @@
 mlflow.set_tracking_uri(uri="http://127.0.0.1:8080")
 
 # Create a new MLflow Experiment
-mlflow.set_experiment("MLflow Quickstart")
+mlflow.set_experiment("MLops Course")
 
 # Start an MLflow run
 with mlflow.start_run():
diff --git a/mlflow_example/requirements.txt b/mlflow_example/requirements.txt
index b86c37f..f2e9409 100644
--- a/mlflow_example/requirements.txt
+++ b/mlflow_example/requirements.txt
@@ -1 +1 @@
-mlflow==2.10.1
+mlflow==2.20.1