Introduction to AI and Machine Learning on Google Cloud

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Introduction to AI and Machine Learning on Google Cloud

Coursera · Beginner ·📐 ML Fundamentals ·3mo ago

Skills: ML Pipelines70%ML Maths Basics60%

Key Takeaways

Introduction to AI and machine learning on Google Cloud for building ML models and pipelines

Original Description

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.

Watch on External: Coursera ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

More on: ML Pipelines

View skill →

Building a Dog Breed Identifier App from scratch - DogNet

Building a Dog Breed Identifier App from scratch - DogNet

Aladdin Persson

Complete Dockers For Data Science Tutorial In One Shot

Complete Dockers For Data Science Tutorial In One Shot

Part 6 | Deploy ML Model on Kubernetes | Auto-Scaling with HPA and Monitoring with Prometheus

Part 6 | Deploy ML Model on Kubernetes | Auto-Scaling with HPA and Monitoring with Prometheus

Abonia Sojasingarayar

MLOps Tutorial: Build a Full ML Pipeline with MLflow, DVC & Deploy on AWS

MLOps Tutorial: Build a Full ML Pipeline with MLflow, DVC & Deploy on AWS

Analytics Vidhya

Vertex Pipelines: Qwik Start

Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation

Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation

Related Reads

Smaller, Slower, Wrong: What Aggressive Quantization Costs On-Device Inference

Aggressive quantization can lead to slower and less accurate on-device inference, highlighting the importance of balancing model size and performance

Smaller, Slower, Wrong: What Aggressive Quantization Costs On-Device Inference

Aggressive quantization can lead to slower and less accurate on-device inference, highlighting the importance of balancing model compression and performance

Medium · Machine Learning

Causal Inference in Finance: Moving Beyond “What Happened?” to “What Actually Worked?”

Learn to apply causal inference in finance to move beyond descriptive analytics and understand what actually drives outcomes

Medium · Machine Learning

does quantising a model reduce its performance ?[R]

Quantizing a model from fp32 to fp8 can reduce its performance due to information loss, but the extent of the loss depends on the model and task

Reddit r/MachineLearning

Dropout in Deep Learning