Comprehensive AWS Cloud Technology and Services course. A spectrum of AWS services, from optimizing compute resources - AWS storage solutions, database management, networking services, analytics, machine learning, and beyond.
By Rahulraj Singh - Technical Product Manager, Bloomberg
Core AWS compute services, Amazon EC2 for scalable cloud computing. Optimize instances, use load balancing and auto-scaling, serverless options like AWS Lambda and Fargate for event-driven applications.
Exploration of Reinforcement Learning (RL), a pivotal branch of machine learning. Core principles of RL, training intelligent agents, teaching them to make strategic decisions and maximize rewards. Agents will learn to navigate a whole host of different environments from OpenAI's gym toolkit, including navigating frozen lakes and mountains.
Presented by Fouad Trad, Machine Learning Engineer
Foundational concepts, roles, and applications. RL framework, agent-environment interaction. Use the Gymnasium library to create environments, visualize states, and perform actions - practical foundation in RL concepts and applications.
Foundational understanding of Amazon Redshift, a distributed columnar database within the AWS cloud. Integration into the AWS ecosystem and its SQL constructs, including common table expressions and analytics queries. Create tables, manage data distribution, handle raw data files, and extract information from JSON. Optimize queries, tune performance, and enhance data security.
By Jason Myers, Co-Author of Essential SQLAlchemy and Software Engineer
Introduction to GCP's architecture and core services, including Storage, Database, and Compute. GCP's data handling capabilities, covering Cloud Spanner, BigQuery, and the integration of AI and ML technologies. Modernization of business using GCP, addressing legacy systems, cloud migration, and digital transformation.
By Nabeel Imam - Data scientist
Unique advantages and diverse services in Google Cloud Platform (GCP) storage, database, and compute. Architecture, core components, and service interconnectivity. Floud data storage, virtual machines, container orchestration, and serverless computing.
NoSQL databases have seen a surge in popularity due to their ability to handle large volumes of unstructured data. Learning to leverage NoSQL tools such as column-oriented, document, key-value, and graph databases allows data engineers, analysts, and scientists to interact with a wider breadth of data. These NoSQL databases bring additional functionality to data storage and retrieval that isn’t available in traditional relational databases.
By Jake Roach, Senior AI Engineer
Basics of NoSQL databases. Identify the pros and cons of using column-oriented, document, key-value, and graph databases to make data available to downstream processes and data consumers.
Discover the world of Amazon Web Services (AWS) and understand why it's at the forefront of cloud computing.
By Amar Suchak - Software Engineer, Microsoft
An in-depth introduction to AWS. Learn about its impact on cloud computing, explore its core service domains - Compute, Storage, Database, and Networking, and understand its global architecture.
Streamline your machine learning development processes, enhancing efficiency, reliability, and reproducibility in your projects. Develop a comprehensive understanding of CI/CD workflows and YAML syntax, utilizing GitHub Actions (GA) for automation, training models in a pipeline, versioning datasets with DVC, and performing hyperparameter tuning.
Essential principles of Continuous Integration/Continuous Delivery (CI/CD) and YAML. Software development life cycle and key terms like build, test, and deploy. Continuous Integration, Continuous Delivery, and Continuous Deployment. Significance of CI/CD in machine learning and experimentation.
SDLC Overview
Learn the very basics of creating tests in Python. Meet four types of software testing methods. Know the types of testing methods, and choose the most suitable ones for a specific context. Design tests and implement them in Python using the pytest and the unittest libraries.
Introduction the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. Explore the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Key components of Google Cloud's infrastructure. Big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.
Learning Objectives:
Create and test a prompt, create a conversation, and explore the prompt gallery, all while having fun! Explore Generative AI Studio in Vertex AI. You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed!
In this lab, you will use BigQuery for data processing and exploratory data analysis and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value. The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV.