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Introduction to GCP

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.

Cloud basics and beyond

Google Cloud Platform (GCP), unique advantages and diverse services in storage, database, and compute. Architecture, core components, and service interconnectivity. Floud data storage, virtual machines, container orchestration, and serverless computing.

The GCP infrastructure

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Introduction to NoSQL

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

Introduction to NoSQL databases

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.

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Introduction to AWS

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

Introduction AWS

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.

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CI/CD for Machine Learning

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.

Introduction

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

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Introduction to Testing in Python

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.

Creating Tests with pytest

• Why is testing so important?
• What is testing?
• Testing with pytest - a simple example

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Google Cloud Big Data and Machine Learning Fundamentals

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.

Big Data and Machine Learning 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:

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Generative AI Explorer game

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!

Vertex AI: Qwik Start (lab)

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.

1. Enable Google Cloud services

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AWS Network Connectivity Options

Knowledge of networking on Amazon Web Services (AWS) beyond the foundational level. Learn about various connectivity options, including how to choose the best way to create a secure network. Explore and analyze complex network design patterns and learn best practices.

Module 1: Understanding Connectivity Concepts

• Multi-tier architecture
• Multi-virtual private cloud (VPC) architecture
• High availability
• Hybrid networks

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GitHub Concepts

Learn How to Use GitHub and explore Key GitHub Concepts.

Building on the topics covered in Introduction to Version Control with Git, this conceptual course enables you to navigate the user interface of GitHub effectively. Create public and private repositories, create and modify files, branches, and issues, assign tasks, tag users, review pull requests, and merge branches. Clone and fork repositories and generate private access tokens (PAT).

By Jasmin Ludolf, Data Science Content Developer, DataCamp

1. Introduction to GitHub

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Introduction to version control with Git

Discover the importance of version control when working on data science projects and explore how to use Git to track files, compare differences, modify and save files, undo changes, and allow collaborative development through the use of branches. Introduction to the structure of a repository, create new repositories and clone existing ones, and show how Git stores data. Skills to handle conflicting files.

By George Boorman, Analytics and Data Science Curriculum Manager, DataCamp

Ressources: Git Cheatsheet

1. Introduction to Git