Deploying AI & Machine Learning Models for Business | Python

Docker standardizes machine learning workflows by building once and deploying everywhere, isolating environments to avoid version and hardware differences and ensure reproducible results from offline to production.

Compare Docker containers to virtual machines and explain how OS virtualization enables efficient resource utilization. Highlight dynamic resource allocation on a single server and the benefits over traditional hardware virtualization.

Provide isolation and portability for machine learning environments, enabling reproducible outputs across operating systems and hardware. Show how Kaggle uses Docker to enable isolated Python, R, Julia, and Scala workloads.

Bridge DevOps and data science by operationalizing machine learning with analytics ops, Docker containers, and real-time deployments for business applications.

Explore the purpose and value of talkers in data science and machine learning, and preview how they enable deploying machine learning applications.

Explore the basics of Flask as a tool through hands-on exercises, guiding you to code along with Spyder IDE to familiarize yourself with the package.

Set up a Flask project by importing Flask, running the script from the terminal, and resolving issues with pip install Flask when needed.

Expose a two-number add function as a Flask API, wrap it in a Flask app, run the server, and stringify the response to fix int errors.

Learn how to take user input in Flask using the request object and get request query parameters A and B, cast to integers, add them, and return the result.

Learn how to implement post requests in flask to keep inputs out of the url, using request.form and postman to test body parameters securely.

Expose machine learning predictions by building a flask api that accepts get and post requests, retrieves inputs like house size and price, and returns the prediction.

Build a machine learning model and expose it as a Flask API. Create a classification model and deliver a black-box service for other applications to send data and receive predictions.

Build a random forest classifier to predict iris flower types using four features: sepal length, sepal width, petal length, and petal width from the iris dataset built into scikit-learn.

Train a random forest classifier on the iris dataset with scikit-learn, perform a train-test split, evaluate accuracy, and prepare the model to be exposed as an API.

Pickle the trained random forest with Python's pickle as a binary file to load for predictions later without retraining, then expose the model via an API for external use.

Exposing a trained random forest as a Flask API by loading a pickle model, accepting sepal and petal inputs via request arguments, and returning predictions using relative paths for reproducibility.

Test and troubleshoot a machine learning API by running a Flask predictor, fixing binary pickle read errors, and feeding iris data to obtain a response from the predict endpoint.

Learn how to accept file input in a Flask API by uploading a CSV, reading with pandas, and returning predictions via post requests.

Learn to use Flasgger to auto generate a user interface for Flask APIs, exposing machine learning models as APIs with a clean interface for business users.

Containerize AI and ML models with Docker and deploy across local, Linux, servers, and cloud. Experiment with different models and datasets, enable file outputs, and SQLite database read/write.

Learn to dockerize machine learning apps by building a Dockerfile and using key commands such as from, copy, expose, work directory, run, and cmd to build an image.

Learn how the from command sets a base image for a Dockerfile, forming the container foundation with continuum io anaconda and libraries like scikit-learn, numpy, scipy, flask.

Learn to transfer files from the host file system into a Docker image using the copy command and expose ports, such as 5000, for Flask apps.

Set the working directory, run installation commands, and configure a persistent CMD in a Dockerfile to build a reproducible, isolated environment for deploying Python apps such as a Flask server.

Prepare the flask scripts for dockerizing by building a dockerfile. Containerize the app, use relative paths for loading the pickle, and test the swagger input predict function.

Write a Dockerfile to containerize a Flask app using the continuum io/anaconda base image, copy the Flask demo and pickle, install from requirements.txt, and expose port 5000.

Build a docker image for a Flask demo using essential docker commands. Resolve common issues such as docker file naming, login errors, and creating a requirements.txt to install Flask.

Learn how to build and run a docker image for a random forest api, use port mapping to expose the service, and debug Python version issues.

Review the end-to-end machine learning deployment pipeline—from training a model and pickle to a dockerized Flask app with Swagger—and ensure production readiness with a production-grade web server like nginx.

Learn to deploy a Flask (or Django) app using Docker and Apache with WSGI, bridging the Python app to production servers and handling concurrent requests.

Configure a wsgi file to bridge Apache and a Flask app in a dockerized setup, updating the requirements and ensuring the Python environment and app object are correctly exposed.

Build a production-grade Dockerfile by extending a base image, exposing port 8000, installing from requirements.txt, and hosting a Flask app on Apache via mod_wsgi.

Build a docker image for a flask app, run the container in production with Apache and mod_wsgi, and debug using docker logs, docker ps, and docker exec to verify endpoints.

Build a clustering application that converts unstructured data into structured data, applies NLP cleanup, and uses unsupervised k-means clustering. Dockerize, wrap with a Flask API, and prepare for production deployment.

Learn to cleanse text by handling nulls, normalizing white spaces, and applying stemming and lemmatization to produce standardized input for numeric analysis.

Build a text clustering workflow that converts unstructured data into structured features by cleansing text, dynamically handling column names, and using count vectorizer to create a document-term matrix for clustering.

Shows how to perform k-means clustering on text data by selecting clusters, fitting and predicting assignments, and exposing outputs via a Flask API that accepts CSV input.

Prepare an Excel output by writing to a BytesIO binary object using an XLSX writer, encode as utf-8, name the sheet clusters, and optionally zip multiple outputs for API delivery.

Configure a zip download for deploying ai and machine learning outputs by sending a memory file as an attachment named cluster_output.zip, setting content-disposition and cross-origin headers, and validating with Postman.

Extract top keywords for kmeans clusters by mapping cluster centers to feature names, ranking words by weights, and compiling per-cluster top ten keywords for insights and reporting.

Pivot by cluster to count documents, rename the column to size, and reset the index for a clear cluster report. Then visualize cluster sizes with an Excel chart.

Containerize an end-to-end unstructured text clustering app with a Flask API using Docker, aiming for a production-grade deployment and GitHub sharing.

Build a deep learning model and expose it via a Flask API to enable image recognition predictions; combine Docker and Flask for a scalable, deployable solution for users.

Import Keras and numpy, configure the backend and image ordering, and set a reproducible seed. Prepare grayscale 28x28 input, normalize by 255, and one-hot encode labels for a convolutional network.

Build a baseline convolutional neural network with Keras to classify 28 by 28 grayscale images using 3 by 3 conv layers, max pooling, and a softmax output for 10 classes.

train a convolutional neural network with Keras, normalize inputs, apply one hot encoding to outputs, save the trained model, and load it later to make predictions with a TensorFlow backend.

Load a saved h5 model with Keras, generate and save random MNIST test images as PNG files via a test script, and validate predictions through the Flask app front end.

Load the deep learning model and build a Flask API to accept image uploads for predictions. Document the endpoint with Swagger, process 28x28 grayscale images, and return the predicted digit.

Expose a trained deep learning model as a Flask-powered API, enabling mobile and desktop apps to obtain predictions. Deploy the API with Docker to standardize the environment.