Deploy ML Model in Production with FastAPI and Docker

Discover the benefits of deploying machine learning models, including automation, real-time decision making, monitoring, scalability, and improved accuracy through feedback, plus packaging and container deployment for cloud-wide use.

Orchestrate data ingestion, quality checks, preparation, and model training with online testing; manage a production model registry and serve predictions via an http API on EC2, with S3 and GitHub.

Learn how to use A/B testing to compare model versions in production, allocate traffic between models A and B, monitor performance, and select the superior model based on conversions.

Learn to identify and mitigate bias and security risks in ML models before production. Navigate regulatory compliance such as GDPR and HIPAA while deploying from ideation to production.

Estimate ml ops production costs with the AWS calculator by configuring EC2 on-demand t3 nano, S3 storage, and data transfer in the North Virginia region.

Master AWS EC2 basics with Boto3 and AWS CLI, create key pairs and t2.micro instances, configure security groups, and learn code-driven automation for production with start, stop, and delete operations.

Connects to an EC2 instance and tests connectivity by adjusting inbound rules in the default security group, demonstrating how to open traffic from external networks for ML deployment.

Configure a custom security group's inbound rules with Python and boto3, adding a tcp rule for port 22 and a cidr range to allow ssh access to an EC2 instance.

Attach and detach security groups to an EC2 instance using python and boto3 by describing the instance and modifying its group IDs for production deployments.

Learn how to manage EC2 instances with Boto3 by starting, stopping, and deleting them, and implement a status monitoring loop that waits for instance state changes.

Learn to control S3 with the boto3 sdk in python to automate model uploads and downloads, enabling CI/CD for production deployments and bucket and folder management.

Learn to upload files to an S3 bucket using the boto3 sdk, including handling local paths, object naming, and renaming files for production deployment of ml models.

Discover how to download an entire S3 directory to a local path using boto3 paginator and list objects v2, organizing files for production deployment of ML models and data.

Learn to delete all files in an s3 bucket with boto3, listing objects and deleting them before removing the bucket in a machine learning ops pipeline with fastapi and docker.

Explore sentiment classification using a tiny Bert model to balance accuracy and production resource constraints, and learn to build, tokenize, save, and deploy models with Hugging Face transformers to S3.

Explore sentiment classification on IMDb reviews with a tiny BERT model and transformer architectures, using NSP and MLM pre-training plus fine-tuning for binary positive or negative sentiment.

Load imdb sentiment data with huggingface datasets, convert a pandas dataframe to a dataset, split into train and test sets for model training with transformers in production.

Use a transformers auto tokenizer to produce input IDs, attention masks, and token type IDs for a tiny bert model, with padding and truncation for fixed-length batches.

Implement a model evaluation function for sequence classification using the Hugging Face evaluate library, install it with pip, load the accuracy metric, and compute predictions against references.

Save a trained tiny bert sentiment analysis model with trainer.save_model, and prepare to upload it to S3. Use a text classification pipeline to run predictions and view probabilities.

Push your trained model to AWS S3 with boto3, creating or reusing buckets, uploading files or folders, and adding production-ready checks using IAM credentials.

Learn to classify disaster tweets with supervised learning, tokenize data with transformers, train and assess models using trainer and training arguments, and deploy via S3 in an end-to-end ML pipeline.

Tokenize disaster tweet data using a tiny Bert model with auto tokenizer, and prepare input IDs, token type IDs, and attention masks for model training and evaluation.

Build an ml ops pipeline, train and save a tiny bert disaster tweet model with transformers and hugging face, achieving 81% accuracy, then load it for inference in production.

Explore image classification with human pose data from a hugging face dataset, train on the train split, and deploy multiple models to S3 as part of an end-to-end ml pipeline.

Create label-to-id and id-to-label mappings, extract features and labels from the train split, and convert IDs to human-readable labels for production deployment.

Learn image preprocessing for vision transformers using PyTorch, including automatic resizing, normalization, and tensor conversion with an auto image processor and a ViT pretrained checkpoint.

Import an auto image classification model, configure the trainer with training arguments, and train a ViT model for five epochs, evaluating and saving the best model.

Compute the classification report and confusion matrix to reveal 78% accuracy across 15 classes and misclassifications like calling, texting, and listening to music, then plan uploading the model to S3.

Push the trained model to AWS S3 using boto3, reusing existing code, saving to the local path vit human pose classification, and preparing for end-to-end production deployment.

Learn the basics of Streamlit by coding along, importing libraries, and building a simple app with a title, header, subheader, and images, then run it with streamlit run.

Master streamlit basics by displaying text and headings with st.text and st.text, enabling user input via st.text_input and st.text_area, and rendering markdown with st.markdown.

Upload and display images in a streamlit app from urls or local files with adjustable width, and use checkbox to toggle visibility; explore slider, spinner, radio, and select box.

design and deploy a Streamlit app that downloads a sentiment-analysis model from S3 and classifies user reviews with a predict button, showcasing an end-to-end ML pipeline.

Create a streamlit app that downloads a ml model with a spinner, accepts reviews via a text area, and performs sentiment analysis with a transformers pipeline.

Upload and deploy an ml model streamlit app by creating a GitHub repository, configuring gitignore and requirements.txt, and pushing code to Streamlit Share for hosting on the Streamlit server.

Upload your ML model deployment code to GitHub, connect Streamlit Share, configure secrets and credentials, deploy, test predictions, and share a public app while noting production options like EC2.

Learn to deploy an ml model on AWS EC2 by reconfiguring code from EC2 and S3 sections and setting up security groups. Automate start/stop workflows for scalable APIs.

Automate deploying a deep learning model on ec2 with a deep learning ami that includes PyTorch, configure volume, and open ports 2280 and 8501.

Launch a fast, production-ready EC2 server with Ubuntu deep learning PyTorch image, then configure storage, security group, and key pair for quick deployment.

Connect to your deep learning ami instance via browser or ssh, using a pem file and the ubuntu user, to access preinstalled python, conda, and cuda.

Connect VS Code to your EC2 server via remote SSH using your PEM file, edit remote files in an ML ops folder, and monitor resources with htop.

Connect your server to GitHub by generating an SSH key, adding it to the SSH agent and your GitHub account, then clone the deployment repo via SSH from VS Code.