Machine Learning - Tag - DataSci Ocean

Introduction to AWS ML Services and Amazon SageMaker

Sat, 11 Feb 2023 00:00:00 +0000

AWS ML Services, led by Amazon SageMaker, help teams build, train, and deploy their own models. This post covers SageMaker Studio, Distributed Training, and Clarify.

AWS AI Services Explained: 13 Ready-to-Use ML Capabilities

Fri, 10 Feb 2023 00:00:00 +0000

AWS's machine learning lineup can be overwhelming. This guide covers the AI Services layer: 13 ready-made use cases you can call via API, no model training needed.

Model Inference: The Final Step in the Machine Learning Workflow

Sun, 05 Feb 2023 00:00:00 +0000

The last step in the ML workflow is model inference: deploying a trained model and running predictions. See how it differs from training, plus Pruning and Quantization.

Machine Learning in 5 Steps: How to Evaluate a Model

Thu, 02 Feb 2023 00:00:00 +0000

Once training is done, how do you know a model is any good? A look at model evaluation, what overfitting is, and the metrics used for classification and regression tasks.

Machine Learning Problem-Solving in 5 Steps: Model Training

Sun, 29 Jan 2023 00:00:00 +0000

Third in our beginner's ML series: what training a model really means, how parameters and the loss function relate, plus hyperparameters, libraries, and model types.

How to Build a Dataset for Machine Learning (in 4 Steps)

Sat, 28 Jan 2023 00:00:00 +0000

Data preparation eats up roughly 80% of a machine learning project's time. Learn its four stages: collection, inspection, summary statistics, visualization.

Machine Learning in 5 Steps: How to Define the Problem

Fri, 27 Jan 2023 00:00:00 +0000

Part 1 of a 5-step ML workflow: how to define a problem clearly, and tell supervised learning (regression, classification) apart from unsupervised clustering.