[DesireCourse.Net] Udemy - Deploy Machine Learning & NLP Models with Dockers (DevOps)

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[DesireCourse.Net] Udemy - Deploy Machine Learning & NLP Models with Dockers (DevOps)

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文件大小： 2.15G
已经下载：1243次
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收录时间：2021-03-20
最近下载：2025-09-23

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文件列表

  • 7. Building NLP based Text Clustering application/5. Preparing the excel output.mp4 114.1 MB
  • 7. Building NLP based Text Clustering application/3. Converting unstructured to structured data.mp4 100.3 MB
  • 8. API for image recognition with deep learning/4. Building the deep learning model.mp4 97.7 MB
  • 7. Building NLP based Text Clustering application/2. Stemming & Lemmatization for cleaner text.mp4 95.3 MB
  • 8. API for image recognition with deep learning/3. Preparing the input images.mp4 93.9 MB
  • 6. Building a production grade Docker application/5. Running and debugging a docker container in production.mp4 90.2 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/8. Flasgger for autogenerating UI.mp4 89.3 MB
  • 7. Building NLP based Text Clustering application/4. KMeans Clustering.mp4 86.2 MB
  • 5. Writing and building the Dockerfile/8. Running the Random Forest model on Docker.mp4 82.6 MB
  • 8. API for image recognition with deep learning/7. Flask API wrapper for making predictions.mp4 82.1 MB
  • 7. Building NLP based Text Clustering application/8. Final output with charts.mp4 80.1 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/7. Providing file input to Flask API.mp4 79.4 MB
  • 7. Building NLP based Text Clustering application/7. Finding top keywords for kmeans clusters.mp4 78.6 MB
  • 5. Writing and building the Dockerfile/7. Building the docker image.mp4 78.4 MB
  • 8. API for image recognition with deep learning/2. Visualizing the input images.mp4 71.7 MB
  • 7. Building NLP based Text Clustering application/6. Making the output Downloadable.mp4 68.7 MB
  • 6. Building a production grade Docker application/3. Configuring the WSGI file.mp4 65.5 MB
  • 6. Building a production grade Docker application/4. Writing a production grade Dockerfile.mp4 64.4 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/5. Exposing the Random Forest model as a Flask API.mp4 56.9 MB
  • 3. Flask basics/5. POST request with Flask.mp4 50.3 MB
  • 5. Writing and building the Dockerfile/6. Writing the Dockerfile.mp4 48.2 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/3. Training the Random Forest model.mp4 48.0 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/6. Testing the API model.mp4 38.4 MB
  • 3. Flask basics/3. Simple Flask API to add two numbers.mp4 38.2 MB
  • 8. API for image recognition with deep learning/6. Generating test images.mp4 35.9 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/4. Pickling the Random Forest model.mp4 35.5 MB
  • 3. Flask basics/4. Taking user input with GET requests.mp4 35.1 MB
  • 8. API for image recognition with deep learning/5. Training and saving the trained deep learning model.mp4 32.5 MB
  • 3. Flask basics/6. Using Flask in the context of Machine Learning.mp4 31.7 MB
  • 5. Writing and building the Dockerfile/4. WORKDIR, RUN and CMD commands.mp4 31.0 MB
  • 6. Building a production grade Docker application/1. Introduction.mp4 27.8 MB
  • 6. Building a production grade Docker application/2. Overall Architecture.mp4 24.8 MB
  • 5. Writing and building the Dockerfile/3. COPY and EXPOSE commands.mp4 22.8 MB
  • 5. Writing and building the Dockerfile/5. Preparing the flask scripts for dockerizing.mp4 22.6 MB
  • 2. Docker basics/1. Why docker.mp4 21.4 MB
  • 8. API for image recognition with deep learning/8. Summary.mp4 19.3 MB
  • 7. Building NLP based Text Clustering application/1. Introduction.mp4 19.0 MB
  • 7. Building NLP based Text Clustering application/9. Summary.mp4 17.3 MB
  • 2. Docker basics/3. Importance of docker containers in machine learning.mp4 15.5 MB
  • 5. Writing and building the Dockerfile/2. Base Image & FROM command.mp4 15.5 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/9. Summary.mp4 14.2 MB
  • 2. Docker basics/2. What are docker containers.mp4 12.6 MB
  • 2. Docker basics/4. Where devops meets data science.mp4 12.2 MB
  • 1. Course Overview/1. Introduction.mp4 11.0 MB
  • 3. Flask basics/2. Setting up a Flask Project.mp4 9.6 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/2. API & Dataset Overview.mp4 8.0 MB
  • 1. Course Overview/3. Skills Checklist.mp4 7.8 MB
  • 1. Course Overview/2. I have a model. Now what.mp4 6.4 MB
  • 8. API for image recognition with deep learning/1. Introduction.mp4 5.3 MB
  • 1. Course Overview/4. Learning Goals.mp4 4.5 MB
  • 3. Flask basics/1. Introduction.mp4 4.3 MB
  • 4. Exposing a Random Forest Machine Learning service as an API/1. Introduction.mp4 4.1 MB
  • 5. Writing and building the Dockerfile/1. Introduction.mp4 2.3 MB
  • 2. Docker basics/5. Summary.mp4 2.2 MB
  • 1. Course Overview/1.1 Course Overview.pdf.pdf 963.0 kB
  • 2. Docker basics/1.1 Docker basics.pdf.pdf 853.5 kB
  • 7. Building NLP based Text Clustering application/3. Converting unstructured to structured data.mp4.jpg 79.3 kB
  • 2. Docker basics/2. What are docker containers.mp4.jpg 71.7 kB
  • 6. Building a production grade Docker application/3.1 Docker deployment.zip.zip 11.8 kB
  • 8. API for image recognition with deep learning/4. Building the deep learning model.vtt 10.3 kB
  • 6. Building a production grade Docker application/5. Running and debugging a docker container in production.vtt 10.3 kB
  • 7. Building NLP based Text Clustering application/2. Stemming & Lemmatization for cleaner text.vtt 10.1 kB
  • 7. Building NLP based Text Clustering application/5. Preparing the excel output.vtt 9.9 kB
  • 7. Building NLP based Text Clustering application/3. Converting unstructured to structured data.vtt 9.1 kB
  • 5. Writing and building the Dockerfile/2.1 Docker sample.zip.zip 9.1 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/8. Flasgger for autogenerating UI.vtt 8.4 kB
  • 5. Writing and building the Dockerfile/8. Running the Random Forest model on Docker.vtt 8.3 kB
  • 5. Writing and building the Dockerfile/6. Writing the Dockerfile.vtt 8.3 kB
  • 5. Writing and building the Dockerfile/7. Building the docker image.vtt 7.9 kB
  • 8. API for image recognition with deep learning/3. Preparing the input images.vtt 7.8 kB
  • 6. Building a production grade Docker application/3. Configuring the WSGI file.vtt 7.6 kB
  • 7. Building NLP based Text Clustering application/4. KMeans Clustering.vtt 7.2 kB
  • 6. Building a production grade Docker application/4. Writing a production grade Dockerfile.vtt 7.1 kB
  • 8. API for image recognition with deep learning/7. Flask API wrapper for making predictions.vtt 6.5 kB
  • 8. API for image recognition with deep learning/2. Visualizing the input images.vtt 6.4 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/7. Providing file input to Flask API.vtt 5.8 kB
  • 7. Building NLP based Text Clustering application/8. Final output with charts.vtt 5.7 kB
  • 7. Building NLP based Text Clustering application/7. Finding top keywords for kmeans clusters.vtt 5.7 kB
  • 7. Building NLP based Text Clustering application/6. Making the output Downloadable.vtt 5.2 kB
  • 3. Flask basics/5. POST request with Flask.vtt 5.2 kB
  • 6. Building a production grade Docker application/2. Overall Architecture.vtt 4.4 kB
  • 6. Building a production grade Docker application/1. Introduction.vtt 4.4 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/5. Exposing the Random Forest model as a Flask API.vtt 4.4 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/3. Training the Random Forest model.vtt 4.4 kB
  • 5. Writing and building the Dockerfile/4. WORKDIR, RUN and CMD commands.vtt 3.9 kB
  • 7. Building NLP based Text Clustering application/2.1 text_cluster_api.py.py 3.7 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/6. Testing the API model.vtt 3.5 kB
  • 3. Flask basics/4. Taking user input with GET requests.vtt 3.5 kB
  • 5. Writing and building the Dockerfile/3. COPY and EXPOSE commands.vtt 3.5 kB
  • 3. Flask basics/3. Simple Flask API to add two numbers.vtt 3.4 kB
  • 5. Writing and building the Dockerfile/2. Base Image & FROM command.vtt 3.3 kB
  • 8. API for image recognition with deep learning/6. Generating test images.vtt 3.2 kB
  • 2. Docker basics/3. Importance of docker containers in machine learning.vtt 3.1 kB
  • 3. Flask basics/6. Using Flask in the context of Machine Learning.vtt 3.1 kB
  • 8. API for image recognition with deep learning/5. Training and saving the trained deep learning model.vtt 3.0 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/4. Pickling the Random Forest model.vtt 2.5 kB
  • 2. Docker basics/2. What are docker containers.vtt 2.3 kB
  • 8. API for image recognition with deep learning/8. Summary.vtt 2.3 kB
  • 7. Building NLP based Text Clustering application/1. Introduction.vtt 2.1 kB
  • 8. API for image recognition with deep learning/2.1 img_reco_train.py.py 2.0 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/9. Summary.vtt 1.9 kB
  • 1. Course Overview/3. Skills Checklist.vtt 1.8 kB
  • 2. Docker basics/1. Why docker.vtt 1.8 kB
  • 5. Writing and building the Dockerfile/5. Preparing the flask scripts for dockerizing.vtt 1.7 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/5.1 flask_predict_api.py.py 1.7 kB
  • 7. Building NLP based Text Clustering application/9. Summary.vtt 1.6 kB
  • 3. Flask basics/2. Setting up a Flask Project.vtt 1.5 kB
  • 1. Course Overview/2. I have a model. Now what.vtt 1.3 kB
  • 1. Course Overview/1. Introduction.vtt 1.1 kB
  • 8. API for image recognition with deep learning/6.1 img_reco_test.py.py 1.1 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/2. API & Dataset Overview.vtt 1.0 kB
  • 4. Exposing a Random Forest Machine Learning service as an API/2.1 flask_predict_train.py.py 1.0 kB
  • 2. Docker basics/4. Where devops meets data science.vtt 987 Bytes
  • 8. API for image recognition with deep learning/1. Introduction.vtt 773 Bytes
  • 5. Writing and building the Dockerfile/1. Introduction.vtt 753 Bytes
  • 1. Course Overview/4. Learning Goals.vtt 708 Bytes
  • 4. Exposing a Random Forest Machine Learning service as an API/1. Introduction.vtt 678 Bytes
  • 3. Flask basics/1. Introduction.vtt 625 Bytes
  • 1. Course Overview/Must Read.txt 540 Bytes
  • 3. Flask basics/2.1 flask1.py.py 428 Bytes
  • 2. Docker basics/5. Summary.vtt 382 Bytes
  • 7. Building NLP based Text Clustering application/3. Converting unstructured to structured data.txt 244 Bytes
  • 2. Docker basics/2. What are docker containers.txt 226 Bytes
  • 7. Building NLP based Text Clustering application/10. Dockerizing the text clustering app.html 164 Bytes
  • 8. API for image recognition with deep learning/9. Dockerizing the deep learning app.html 164 Bytes
  • 6. Building a production grade Docker application/6. Docker Quiz 1 – Basic Concepts, Commands.html 160 Bytes
  • 1. Course Overview/Visit Coursedrive.org.url 124 Bytes
  • [DesireCourse.Net].url 51 Bytes
  • [CourseClub.Me].url 48 Bytes

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