Just a place to write
This post is a summary of this video titled: Privacy Preserving Machine Learning with Fully Homomorphic Encryption, done by Jordan Fréry. Abstract In today’s digital age, protecting privacy has become increasingly difficult. However, new developments such as Fully Homomorphic Encryption (FHE) provide a means of safeguarding sensitive client information. We are excited to present Concrete-ML, … Read more
The publication of the pricing deck for the OpenAI models focused on Enterprises gave me some time about how to approach this from the point of view of companies. I have started with this Wardley Map The starting point I will start with the actual situation of a given company. Where to start? We all … Read more
This analysis is great to visit it and pay attention to the data that have been organized by First Mark. The main document where you can start reading is here:
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
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 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.
This post is the continuation of Project approach for a Machine Learning project, and it focus on the schedule of a given project on the context of machine learning. The creation of project schedules is something that can be done if you are able to estimate the efforts and you know the availability of the … Read more
Background and goal This post covers different aspects of a Machine Learning (ML) project, looking at it from the project / program manager point of view. This post is being updated as new feedback is received and new experiences are lived. This post is being divided in different posts as its so large to digest. … Read more
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
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