Integration and Deployment of GenAI Models

Create a free AWS account by guiding email verification, password setup, personal details, and entering credit card information, then access the AWS console and learn about the free tier.

Explore the Amazon SageMaker console, navigate services like Studio and canvas, and understand admin configuration and role setup to build, train, and deploy ML models.

Define and manage Amazon SageMaker domains, roles, and user profiles to secure access, control compute and storage resources, and enable multi-user collaboration in a shared environment.

Learn how SageMaker Studio provides a cloud-based, single environment for building, training, debugging, and deploying ML models with unified workflows and powerful tools like Jupyter Lab and MLflow.

Explore how sage maker canvas enables no-code machine learning for analysts to build and predict. See how data wrangler imports, cleans data, and integrates with AWS for deployment.

Analyze data with Data Wrangler to understand data types, handle missing values and outliers, and visualize feature correlations via heatmaps to prepare for training GenAI models on canvas.

Develop a bank customer prediction model using clean data exported from the data wrangler to a canvas data set, then preview and build the model with a two-class target.

Learn to build and evaluate a machine learning model with canvas, choose quick build for speed, analyze accuracy and F1, view confusion matrices, and deploy no code predictions.

Identify running AWS SageMaker Studio workflows and stop or delete them to prevent unnecessary charges, using billing and cost management to monitor current costs.

Learn how SageMaker Jumpstart speeds ML with ready-to-use models and templates for rapid prototyping and deployment. Manage quotas, deploy endpoints, and safely delete resources to avoid charges.

Learn to run SageMaker Python SDK workflows from JupyterLab, preprocess data, encode categoricals, perform train test splits, and upload prepared csv files to S3 for XGBoost training.

Learn to train an XGBoost model in SageMaker with the Python SDK: fetch the Docker image URI, create an estimator, set hyperparameters, prepare training inputs, and run a training job.

Deploy a trained GenAI model to a SageMaker endpoint with the Python SDK, configure one ml.m5.large instance, and generate test predictions; then delete the endpoint to avoid AWS charges.

Explore Amazon Bedrock, a fully managed AWS service that enables building and scaling generative AI apps with foundation models from providers via API calls, including fine-tuning on your data.

Explore how to access and configure Bedrock models across regions, including cross-region inference, adjust settings like temperature and top p, and test text and image generation in playgrounds.

Learn to connect a local Python environment to AWS by creating an IAM bedrock user, generating access keys, and saving them in the dot aws credentials file for boto3 access.

Connect your Python environment to AWS using boto3 to access Bedrock foundation models, configure credentials and region, list models, and identify model IDs for text generation experiments.

Explore how to connect to AWS bedrock foundation models with boto3, filter the metamodels, and build a flexible text generation function using prompts, temperature, and top P.

Explore image generation with Amazon Bedrock by configuring Stability AI diffusion models in Python, using prompts, weights, cfg scale, guidance presets, and image parameters.

Connect to AWS Bedrock from a local Python environment to generate images with Stability AI diffusion, configuring prompts, cfg scale, seeds, steps, and decoding base64 results into a saved image.

Learn how retrieval augmented generation fuses a large language model with a document store, using embeddings and a vector store, semantic search, and prompt augmentation to deliver up-to-date, accurate answers.

Explore Anthropic cloud, a Claude foundation model family including haiku, sonnet, and opus, with varying speed, cost, and capabilities, and learn about model versions and benchmarks.

Set up the Anthropic cloud by creating an account, purchasing credits, and generating API keys; learn to store keys securely and connect via API for Python SDK workflows.

Connect to the anthropic cloud with the anthropic Python package, authenticate with an API key, and generate Claude-based text by choosing a model and adjusting maximum tokens and temperature.

Learn to design AI conversations with user and assistant roles, preserving history to guide responses. Experiment with role definitions and temperature to balance coherence and creativity.

Learn how system prompts shape large language model behavior by defining rules, personality, and responses to control tone and output style.

Master multimodal ai in integration and deployment of genai models by encoding images to base64, sending them with text prompts to Claude, and obtaining evaluative descriptions.