Udemy - Machine Learning Deep Learning model deployment (3.2023)

Creation Time	Sept. 3, 2025, 8:51 p.m.
Last Access Time	Oct. 25, 2025, 3:54 a.m.
File Size	2.8 GB
Keywords	2023 - Deep Udemy Machine 3 Learning deployment model
Total Requests	283
Total Files	155
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2 - Building evaluating and saving a Model/7 - Building and evaluating a Classification Model.mp4 253.5 MB
7 - Deploying models on browser using JavaScript and TensorFlowjs/40 - JavaScript crash course optional.mp4 203.1 MB
10 - MLOps and MLflow/52 - Tracking Model training experiments with MLfLow.mp4 199.0 MB
2 - Building evaluating and saving a Model/6 - Python NumPy Pandas Matplotlib crash course.mp4 179.2 MB
4 - Creating a REST API for the Machine Learning Model/14 - Hosting the Machine Learning REST API on the Cloud.mp4 117.5 MB
10 - MLOps and MLflow/56 - Running MLFlow on Databricks.mp4 106.5 MB
8 - Model as a mathematical formula Model as code/45 - Model as code.mp4 93.8 MB
6 - Deploying NLP models for Twitter sentiment analysis/28 - Creating and saving text classifier and tfidf models.mp4 87.5 MB
5 - Deploying Deep Learning Models/20 - Building and deploying PyTorch models.mp4 82.4 MB
4 - Creating a REST API for the Machine Learning Model/16 - Serverless Machine Learning API using Cloud Functions.mp4 75.6 MB
10 - MLOps and MLflow/53 - MLflow autologging.mp4 71.2 MB
5 - Deploying Deep Learning Models/24 - Creating a REST API using TensorFlow Model Server.mp4 69.5 MB
6 - Deploying NLP models for Twitter sentiment analysis/30 - Deploying tfidf and text classifier models for Twitter sentiment analysis.mp4 68.7 MB
5 - Deploying Deep Learning Models/22 - Saving loading TensorFlow Keras models.mp4 63.5 MB
9 - Models in Database/46 - Storing and retrieving models from a database using Colab Postgres and psycopg2.mp4 60.0 MB
5 - Deploying Deep Learning Models/21 - Creating a REST API for the PyTorch Model.mp4 59.0 MB
10 - MLOps and MLflow/51 - Installing MLflow on Windows and Mac.mp4 56.6 MB
6 - Deploying NLP models for Twitter sentiment analysis/33 - Twitter sentiment analysis with PyTorch REST API.mp4 52.3 MB
6 - Deploying NLP models for Twitter sentiment analysis/27 - tfidf model for converting text to numeric values.mp4 51.5 MB
4 - Creating a REST API for the Machine Learning Model/12 - Creating a REST API for the Model.mp4 49.8 MB
8 - Model as a mathematical formula Model as code/44 - Deriving formula from a Linear Regression Model.mp4 46.0 MB
6 - Deploying NLP models for Twitter sentiment analysis/36 - Serving TensorFlow models serverless.mp4 42.8 MB
6 - Deploying NLP models for Twitter sentiment analysis/32 - Creating a REST API for the PyTorch NLP model.mp4 42.6 MB
4 - Creating a REST API for the Machine Learning Model/11 - Flask REST API Hello World.mp4 42.6 MB
6 - Deploying NLP models for Twitter sentiment analysis/31 - Creating a text classifier using PyTorch.mp4 41.5 MB
6 - Deploying NLP models for Twitter sentiment analysis/26 - Converting text to numeric values using bagofwords model.mp4 41.3 MB
9 - Models in Database/47 - Creating a local model store with PostgreSQL.mp4 41.1 MB
3 - Deploying the Model in other environments/9 - Predicting locally with deserialized Pickle objects.mp4 37.7 MB
4 - Creating a REST API for the Machine Learning Model/17 - Creating a REST API on Google Colab.mp4 35.5 MB
6 - Deploying NLP models for Twitter sentiment analysis/35 - Creating a REST API for TensforFlow models using Flask.mp4 32.0 MB
7 - Deploying models on browser using JavaScript and TensorFlowjs/43 - Deploying Keras model on a web page using TensorFlowjs.mp4 31.7 MB
10 - MLOps and MLflow/55 - Running MLflow on Colab.mp4 31.0 MB
6 - Deploying NLP models for Twitter sentiment analysis/37 - Serving PyTorch models serverless.mp4 30.0 MB
10 - MLOps and MLflow/57 - Tracking PyTorch experiments with MLflow.mp4 29.7 MB
10 - MLOps and MLflow/48 - Machine Learning Operations MLOps.mp4 29.4 MB
5 - Deploying Deep Learning Models/25 - Converting a PyTorch model to TensorFlow format using ONNX.mp4 29.2 MB
4 - Creating a REST API for the Machine Learning Model/13 - Signing up for a Google Cloud free trial.mp4 25.7 MB
5 - Deploying Deep Learning Models/19 - Understanding Deep Learning Neural Network.mp4 22.4 MB
2 - Building evaluating and saving a Model/5 - Creating a Spyder development environment.mp4 21.3 MB
3 - Deploying the Model in other environments/10 - Using the Model in Google Colab environment.mp4 21.1 MB
7 - Deploying models on browser using JavaScript and TensorFlowjs/41 - Adding TensforFlowjs to a web page.mp4 20.6 MB
10 - MLOps and MLflow/54 - MLflow REST APIs.mp4 19.2 MB
2 - Building evaluating and saving a Model/8 - Saving the Model and the Scaler.mp4 18.5 MB
1 - Introduction/4 - Types of Machine Learning.mp4 15.3 MB
1 - Introduction/1 - Introduction.mp4 14.7 MB
4 - Creating a REST API for the Machine Learning Model/18 - Postman REST client.mp4 12.0 MB
10 - MLOps and MLflow/58 - Deploying Models with MLflow.mp4 11.0 MB
7 - Deploying models on browser using JavaScript and TensorFlowjs/39 - Installing Visual Studio Code and Live Server.mp4 9.5 MB
6 - Deploying NLP models for Twitter sentiment analysis/29 - Creating a Twitter developer account.mp4 9.4 MB
6 - Deploying NLP models for Twitter sentiment analysis/34 - Creating a text classifier using TensorFlow.mp4 7.7 MB
7 - Deploying models on browser using JavaScript and TensorFlowjs/42 - Basic tensor operations using TensorFlowjs.mp4 7.6 MB
1 - Introduction/3 - How do we create a Model.mp4 7.6 MB
10 - MLOps and MLflow/49 - MLflow Introduction.mp4 7.0 MB
5 - Deploying Deep Learning Models/23 - Understanding Docker containers.mp4 5.8 MB
6 - Deploying NLP models for Twitter sentiment analysis/35 - tf-nlp-flask-rest.zip 5.1 MB
6 - Deploying NLP models for Twitter sentiment analysis/36 - tf-serverless.zip 5.1 MB
10 - MLOps and MLflow/50 - MLflow tracking concepts.mp4 4.7 MB
1 - Introduction/2 - What is a Model.mp4 4.1 MB
4 - Creating a REST API for the Machine Learning Model/15 - Deleting the VM instance.mp4 3.5 MB
7 - Deploying models on browser using JavaScript and TensorFlowjs/38 - TensorFlowjs introduction.mp4 3.3 MB
6 - Deploying NLP models for Twitter sentiment analysis/30 - twitter-sentiment-analysis.zip 34.3 KB
6 - Deploying NLP models for Twitter sentiment analysis/28 - text-classifier.zip.zip 29.8 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/40 - javascript-crash-course.zip 27.3 KB
6 - Deploying NLP models for Twitter sentiment analysis/33 - twitter-pytorch-rest.zip 24.0 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/40 - JavaScript crash course optional.vtt 21.2 KB
5 - Deploying Deep Learning Models/22 - tf-serving-save-export.zip 21.1 KB
6 - Deploying NLP models for Twitter sentiment analysis/37 - pytorch-serverless.zip 20.2 KB
2 - Building evaluating and saving a Model/6 - Python NumPy Pandas Matplotlib crash course.vtt 19.0 KB
6 - Deploying NLP models for Twitter sentiment analysis/31 - text-classifier-pytorch.zip 18.4 KB
2 - Building evaluating and saving a Model/7 - Building and evaluating a Classification Model.vtt 15.0 KB
4 - Creating a REST API for the Machine Learning Model/14 - Hosting the Machine Learning REST API on the Cloud.vtt 12.4 KB
4 - Creating a REST API for the Machine Learning Model/16 - Serverless Machine Learning API using Cloud Functions.vtt 12.1 KB
6 - Deploying NLP models for Twitter sentiment analysis/28 - Creating and saving text classifier and tfidf models.vtt 11.5 KB
5 - Deploying Deep Learning Models/20 - save-export-reload-pytorch-models.zip 11.3 KB
5 - Deploying Deep Learning Models/20 - Building and deploying PyTorch models.vtt 11.1 KB
9 - Models in Database/46 - Storing and retrieving models from a database using Colab Postgres and psycopg2.vtt 10.8 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/43 - tfjs4.zip 10.3 KB
8 - Model as a mathematical formula Model as code/44 - Deriving formula from a Linear Regression Model.vtt 10.2 KB
10 - MLOps and MLflow/56 - Running MLFlow on Databricks.vtt 9.7 KB
10 - MLOps and MLflow/52 - Tracking Model training experiments with MLfLow.vtt 9.3 KB
6 - Deploying NLP models for Twitter sentiment analysis/34 - text-classifier-tensorflow.zip 8.7 KB
10 - MLOps and MLflow/53 - MLflow autologging.vtt 7.8 KB
8 - Model as a mathematical formula Model as code/45 - Model as code.vtt 7.7 KB
10 - MLOps and MLflow/57 - mlflow-pytorch.zip 7.6 KB
5 - Deploying Deep Learning Models/25 - pytorch-create-save-onnx.zip 7.5 KB
10 - MLOps and MLflow/48 - Machine Learning Operations MLOps.vtt 7.1 KB
5 - Deploying Deep Learning Models/22 - Saving loading TensorFlow Keras models.vtt 6.4 KB
9 - Models in Database/46 - models-in-db.zip 6.4 KB
6 - Deploying NLP models for Twitter sentiment analysis/36 - Serving TensorFlow models serverless.vtt 6.4 KB
5 - Deploying Deep Learning Models/24 - Creating a REST API using TensorFlow Model Server.vtt 6.1 KB
10 - MLOps and MLflow/55 - running-mlflow-on-colab.zip 5.7 KB
5 - Deploying Deep Learning Models/19 - Understanding Deep Learning Neural Network.vtt 5.7 KB
9 - Models in Database/47 - Creating a local model store with PostgreSQL.vtt 5.5 KB
6 - Deploying NLP models for Twitter sentiment analysis/26 - Converting text to numeric values using bagofwords model.vtt 5.3 KB
6 - Deploying NLP models for Twitter sentiment analysis/30 - Deploying tfidf and text classifier models for Twitter sentiment analysis.vtt 5.1 KB
4 - Creating a REST API for the Machine Learning Model/11 - Flask REST API Hello World.vtt 5.1 KB
4 - Creating a REST API for the Machine Learning Model/17 - Creating a REST API on Google Colab.vtt 4.9 KB
1 - Introduction/4 - Types of Machine Learning.vtt 4.8 KB
4 - Creating a REST API for the Machine Learning Model/17 - colab-rest-api.zip 4.8 KB
6 - Deploying NLP models for Twitter sentiment analysis/27 - tfidf model for converting text to numeric values.vtt 4.7 KB
3 - Deploying the Model in other environments/10 - Using the Model in Google Colab environment.vtt 4.7 KB
2 - Building evaluating and saving a Model/8 - Saving the Model and the Scaler.vtt 4.4 KB
6 - Deploying NLP models for Twitter sentiment analysis/33 - Twitter sentiment analysis with PyTorch REST API.vtt 4.2 KB
4 - Creating a REST API for the Machine Learning Model/12 - Creating a REST API for the Model.vtt 4.0 KB
6 - Deploying NLP models for Twitter sentiment analysis/31 - Creating a text classifier using PyTorch.vtt 3.6 KB
10 - MLOps and MLflow/51 - Installing MLflow on Windows and Mac.vtt 3.4 KB
5 - Deploying Deep Learning Models/23 - Understanding Docker containers.vtt 3.2 KB
5 - Deploying Deep Learning Models/25 - Converting a PyTorch model to TensorFlow format using ONNX.vtt 3.2 KB
3 - Deploying the Model in other environments/9 - Predicting locally with deserialized Pickle objects.vtt 3.2 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/43 - Deploying Keras model on a web page using TensorFlowjs.vtt 3.2 KB
5 - Deploying Deep Learning Models/24 - tf-model-serving.zip 3.0 KB
6 - Deploying NLP models for Twitter sentiment analysis/37 - Serving PyTorch models serverless.vtt 3.0 KB
1 - Introduction/1 - Introduction.vtt 2.9 KB
6 - Deploying NLP models for Twitter sentiment analysis/32 - Creating a REST API for the PyTorch NLP model.vtt 2.9 KB
1 - Introduction/3 - How do we create a Model.vtt 2.8 KB
5 - Deploying Deep Learning Models/21 - Creating a REST API for the PyTorch Model.vtt 2.8 KB
10 - MLOps and MLflow/55 - Running MLflow on Colab.vtt 2.7 KB
6 - Deploying NLP models for Twitter sentiment analysis/35 - Creating a REST API for TensforFlow models using Flask.vtt 2.5 KB
3 - Deploying the Model in other environments/10 - use-model-colab.zip 2.5 KB
4 - Creating a REST API for the Machine Learning Model/13 - Signing up for a Google Cloud free trial.vtt 2.5 KB
2 - Building evaluating and saving a Model/5 - Creating a Spyder development environment.vtt 2.5 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/39 - Installing Visual Studio Code and Live Server.vtt 2.5 KB
10 - MLOps and MLflow/57 - Tracking PyTorch experiments with MLflow.vtt 2.4 KB
4 - Creating a REST API for the Machine Learning Model/18 - Postman REST client.vtt 2.3 KB
6 - Deploying NLP models for Twitter sentiment analysis/29 - Creating a Twitter developer account.vtt 2.3 KB
10 - MLOps and MLflow/49 - MLflow Introduction.vtt 2.2 KB
10 - MLOps and MLflow/50 - MLflow tracking concepts.vtt 2.1 KB
10 - MLOps and MLflow/58 - Deploying Models with MLflow.vtt 2.1 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/38 - TensorFlowjs introduction.vtt 2.0 KB
9 - Models in Database/47 - local-model-store.zip 1.9 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/41 - Adding TensforFlowjs to a web page.vtt 1.7 KB
6 - Deploying NLP models for Twitter sentiment analysis/34 - Creating a text classifier using TensorFlow.vtt 1.6 KB
1 - Introduction/2 - What is a Model.vtt 1.5 KB
10 - MLOps and MLflow/54 - MLflow REST APIs.vtt 1.5 KB
7 - Deploying models on browser using JavaScript and TensorFlowjs/42 - Basic tensor operations using TensorFlowjs.vtt 1.4 KB
10 - MLOps and MLflow/54 - mlflow-python-rest.zip 1.3 KB
10 - MLOps and MLflow/52 - ml-pipeline-mlflow.py.zip 1.3 KB
8 - Model as a mathematical formula Model as code/45 - model-as-code.zip 1.1 KB
2 - Building evaluating and saving a Model/7 - ml-classification.zip 1.1 KB
2 - Building evaluating and saving a Model/6 - python-np-pd-plt.zip 1.0 KB
6 - Deploying NLP models for Twitter sentiment analysis/32 - pytorch-nlp-rest.zip 1.0 KB
8 - Model as a mathematical formula Model as code/44 - linear-regression.zip 1.0 KB
10 - MLOps and MLflow/53 - ml-pipeline-mlflow-autolog.py.zip 984 bytes
4 - Creating a REST API for the Machine Learning Model/14 - classifier-rest-service-on-GCP.zip 898 bytes
10 - MLOps and MLflow/56 - mlflow-demo-2.zip 875 bytes
4 - Creating a REST API for the Machine Learning Model/15 - Deleting the VM instance.vtt 852 bytes
4 - Creating a REST API for the Machine Learning Model/16 - google-cloud-functions-serverless-ml.zip 851 bytes
4 - Creating a REST API for the Machine Learning Model/12 - classifier-rest-service.zip 822 bytes
5 - Deploying Deep Learning Models/21 - pytorch-flask.zip 777 bytes
4 - Creating a REST API for the Machine Learning Model/11 - flask-hello-world.zip 672 bytes
7 - Deploying models on browser using JavaScript and TensorFlowjs/42 - tfjs2.zip 642 bytes
10 - MLOps and MLflow/58 - mlflow-deploy.zip 635 bytes
7 - Deploying models on browser using JavaScript and TensorFlowjs/41 - tfjs1.zip 542 bytes
3 - Deploying the Model in other environments/9 - use-model.zip 337 bytes
2 - Building evaluating and saving a Model/7 - git-hub-link.txt 51 bytes

Udemy - Machine Learning Deep Learning model deployment (3.2023)

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