diff --git a/JenkinsfileMLflow b/JenkinsfileMLflow
new file mode 100644
index 0000000..f2fb8de
--- /dev/null
+++ b/JenkinsfileMLflow
@@ -0,0 +1,29 @@
+node {
+         stage('Preparation') {
+
+                properties([
+                    parameters([
+                        buildSelector(defaultSelector: lastSuccessful(),
+                            description: 'Which build to use for copying artifacts',
+                            name: 'BUILD_SELECTOR')
+                            ])
+                ]
+                )
+
+        }
+        stage('Clone repo') {
+                        docker.image("karopa/ium:31").inside {
+                            stage('Test') {
+                                checkout([$class: 'GitSCM', branches: [[name: '*/mlflow']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
+                                copyArtifacts fingerprintArtifacts: true, projectName: 's437622-training/master/', selector: buildParameter("BUILD_SELECTOR")
+                                sh '''
+                                    #!/usr/bin/env bash
+                                    chmod 777 mlflow_partner.sh
+                                    rm -r model
+                                    ./mlflow_partner.sh | tee output.txt
+                                '''
+                                archiveArtifacts 'output.txt'
+                            }
+                        }
+        }
+}
diff --git a/JenkinsfileNeural b/JenkinsfileNeural
index cf143c0..ff23b9c 100644
--- a/JenkinsfileNeural
+++ b/JenkinsfileNeural
@@ -16,7 +16,7 @@ node {
 
         }
         stage('Clone repo') {
-                   /* try {*/ docker.image("karopa/ium:31").inside {
+                   /* try {*/ docker.image("karopa/ium:32").inside {
                         stage('Test') {
                             checkout([$class: 'GitSCM', branches: [[name: '*/mlflow']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
                             copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
@@ -25,7 +25,6 @@ node {
                                 chmod 777 neural_network.sh
                                 rm -r model
                                 ./neural_network.sh $EPOCHS | tee output.txt
-                                mlflow run . -e main
                             '''
                             archiveArtifacts 'output.txt'
                             archiveArtifacts 'model/**/*.*'
diff --git a/mlflow_partner.py b/mlflow_partner.py
new file mode 100644
index 0000000..9b7f764
--- /dev/null
+++ b/mlflow_partner.py
@@ -0,0 +1,4 @@
+from mlflow import keras
+
+model = keras.load_model('saved_model.pb')
+predict = model.predict([13, 1, 1500, 1500])
\ No newline at end of file
diff --git a/neural_network.py b/neural_network.py
index 8f93e92..c179afb 100644
--- a/neural_network.py
+++ b/neural_network.py
@@ -20,6 +20,7 @@ mlflow.set_experiment("s434765")
 warnings.filterwarnings("ignore")
 np.random.seed(40)
 
+
 def normalize_data(data):
     return (data - np.min(data)) / (np.max(data) - np.min(data))
 
@@ -73,4 +74,7 @@ with mlflow.start_run() as run:
                                                                 "channel_title", "views", "likes", "dislikes",
                                                                 "comment_count"]).dropna()
     X_test = data.loc[:, data.columns == "views"].astype(int)
-    mlflow.keras.save_model(model, "model", registered_model_name="model", signature=signature, input_example=X_test)
+    mlflow.keras.log_model(model, "youtube_model", registered_model_name="youtube_model", input_example=X_test,
+                           signature=signature)
+    mlflow.keras.save_model(model, "youtube_model", registered_model_name="youtube_model", signature=signature,
+                            input_example=X_test)