Machine Learning on AWS

This post are some notes of the course done related to ML on AWS (8 hours). Table of contents Module 1: Introduction to Machine LearningModule 2: Artificial Intelligence Services on AWSModule 3: Machine Learning ProcessModule 4: Data Collection, Integration, Preparation, Visualization, and AnalysisModule 5: Deep Learning Amazon Machine ImagesModule 6: Amazon SageMaker ConceptsModule 7: Amazon … Read more

Amazon SageMaker + Spark

Some screenshots and notes for my poor memory ML Pipeline with PCA on Spark, and K-Means on Amazon SageMaker Apache Spark is an open-source unified analytics engine for large-scale data processing.  PCA = principal components analysis. Collaborative Filtering Deep Structure Semantic Module (DSSM) A matrix factorization solution in its core is multiplication of 2 matrices. Neural Networks are … Read more

Awful AI

Awful AI is a curated list to track current scary usages of AI – hoping to raise awareness to its misuses in society. https://github.com/daviddao/awful-ai I keep it for my poor memory.

Project approach for a Machine Learning project

The project approach is defined by the lifecycle of the solution, and here I will focused just on the machine learning side of the project (other sections as infrastructure, education, operations…) will not be reviewed here. What is the lifecycle of a Machine Learning solution? The lifecycle is sequential and in reality is can be … Read more

Considerations for the project scope of a Machine Learning project

Let’s start with the basic questions What is the business problem to be solved? What is the situation AS-IS? What are the current pain points you are facing? How are you attending these pain points? Are the causes of the problem identified? What is the problem impact? What is the desired situation TO-BE? What are … Read more

Machine Learning project: Agile or Waterfall approach?

This question is so easy: agile approach. Why? Because it recognizes that the construction of the solution requires different loops. Reason 1: ML models change overtime Machine Learning projects are supported on ML models, and models change overtime. Why do a model change overtime? A model change overtime because the data used to train the … Read more

AWS Sagemaker

These are some notes about the basis of Sagemaker Sagemaker services SageMaker Neo optimizes the trained model and compiles it into an executable. Taking the target hardware where the model will be run as input; the compiler uses a ML model to apply performance optimizations on your model. Ground truth makes easy to label data. … Read more

CRISP-DM methodology

The cross-industry standard process for data mining or CRISP-DM is an open standard process framework model for data mining project planning, created in 1996. The process of CRISP-DM is into 6 phases or components: Business understanding – What does the business need? Data understanding – What data do we have / need? Is it clean? Data preparation – How do we organize … Read more