MLOps vs LLMOps (by workflows and use cases) - 2024-05-21Alessandra Bilardi
MLOps @ localhost 2024
A pragmatic approach to manage ML systems by workflows and use cases.
https://www.grusp.org/conferenze_/mlops-127-0-0-1-21-maggio-2024/
How to move your ML system from local to production - 2024-03-15Alessandra Bilardi
Incontro DevOps Italia 2024
When a Cloud Engineer has to do a review the code of its colleague Data Scientist for production environment, it is always important to understand where it is best to put the focus. Often, the best approach is to promote the resources awareness to be used and to find a framework to split the work together.
https://2024.incontrodevops.it/talks_speakers/
This document summarizes a PyData Venice meetup on February 29, 2024. The meetup will be held in-person and streamed live at 7:00 PM featuring talks from Fabio Dal Forno on Kaggle competitions and Alessandra Bilardi on an overview of the Kaggle platform. Kaggle is a platform for machine learning competitions and projects with over 17 million users worldwide and datasets, models, and discussions. The document outlines Kaggle's history and growth, resources available on the platform, and how it can be useful for learning feature engineering and algorithm tuning through real projects.
AWS User Group Padova event.
Which AWS services there are for forecasting scenarious ?
https://www.meetup.com/it-IT/aws-user-group-padova/events/298480058/
From your laptop to all resource that you need - 2023-12-09Alessandra Bilardi
PyData Impact Scholars - PyData Global 2023
Imagine you are processing your data and your ML system from your laptop and there are not enough resources, but by adding a few lines of code you can access all the resources you need. So, from your Jupyter notebook you can orchestrate tests of your code and then you can run the same code in the cloud with … a flag and little else.
https://github.com/bilardi/pydata-global/tree/master/2023
MLOps vs LLMOps (by workflows and use cases) - 2024-05-21Alessandra Bilardi
MLOps @ localhost 2024
A pragmatic approach to manage ML systems by workflows and use cases.
https://www.grusp.org/conferenze_/mlops-127-0-0-1-21-maggio-2024/
How to move your ML system from local to production - 2024-03-15Alessandra Bilardi
Incontro DevOps Italia 2024
When a Cloud Engineer has to do a review the code of its colleague Data Scientist for production environment, it is always important to understand where it is best to put the focus. Often, the best approach is to promote the resources awareness to be used and to find a framework to split the work together.
https://2024.incontrodevops.it/talks_speakers/
This document summarizes a PyData Venice meetup on February 29, 2024. The meetup will be held in-person and streamed live at 7:00 PM featuring talks from Fabio Dal Forno on Kaggle competitions and Alessandra Bilardi on an overview of the Kaggle platform. Kaggle is a platform for machine learning competitions and projects with over 17 million users worldwide and datasets, models, and discussions. The document outlines Kaggle's history and growth, resources available on the platform, and how it can be useful for learning feature engineering and algorithm tuning through real projects.
AWS User Group Padova event.
Which AWS services there are for forecasting scenarious ?
https://www.meetup.com/it-IT/aws-user-group-padova/events/298480058/
From your laptop to all resource that you need - 2023-12-09Alessandra Bilardi
PyData Impact Scholars - PyData Global 2023
Imagine you are processing your data and your ML system from your laptop and there are not enough resources, but by adding a few lines of code you can access all the resources you need. So, from your Jupyter notebook you can orchestrate tests of your code and then you can run the same code in the cloud with … a flag and little else.
https://github.com/bilardi/pydata-global/tree/master/2023
PyDataVE #13
Which open source libraries can compete with Pandas, PySpark with some activities as apply, groupby and sum ?
https://www.meetup.com/pydata-venice/events/296507635/
This document discusses forecasting in AWS and summarizes key points:
1. It discusses the importance of forecasting for retail demand planning, supply chain planning, resource planning, and operational planning.
2. It outlines the different types of data that can be used for forecasting, including target time series data, related time series data, categorical data, and geolocation data.
3. It reviews AWS services for forecasting, including Amazon SageMaker Canvas, Amazon Forecast, Amazon SageMaker JumpStart, and Amazon SageMaker built-in algorithms.
This document provides an introduction to key concepts in artificial intelligence including object detection using Python. It discusses input datasets, model training, and output predictions. As an example, it describes how object detection works by taking an image as input, training a model, and outputting a prediction of the classified object and its position in the image. It also references providing a demonstration of basic Python and Coolab for object detection.
This document discusses data transformation in AWS. It covers the importance of data transformation in terms of quantity, quality, and noise/compatibility. It then describes common transformation methods like extraction, parsing, cleaning, and enrichment. Several AWS services for data transformation are presented, including AWS Glue, AWS DataBrew, AWS Data Pipeline, Amazon SageMaker Data Wrangler, and notebooks. These services are compared based on difficulty, execution times, and costs.
Automation: from local test to production deploy - 2020-11-05Alessandra Bilardi
CloudConf 2020 in Streaming
Talk about a sample of Automation Solution from local to production
https://2020.cloudconf.it/
https://github.com/bilardi/aws-saving/
https://github.com/bilardi/aws-simple-pipeline/
This document provides an overview of solving a Rubik's cube, including its history, features, notation, and algorithms. It describes how Ernő Rubik invented the Rubik's cube in 1974 and discusses its notation system using letters and prime symbols to denote clockwise and counterclockwise turns of different sides. The document outlines the levels approach to solving a Rubik's cube and includes some edge and corner algorithms like the sexy move to solve each level.
AWS Summit 2018 Milano
Our part of AWS presentation: Managed Relational Databases
https://www.slideshare.net/AmazonWebServices/database-relazionali-gestiti
PyDataVE #13
Which open source libraries can compete with Pandas, PySpark with some activities as apply, groupby and sum ?
https://www.meetup.com/pydata-venice/events/296507635/
This document discusses forecasting in AWS and summarizes key points:
1. It discusses the importance of forecasting for retail demand planning, supply chain planning, resource planning, and operational planning.
2. It outlines the different types of data that can be used for forecasting, including target time series data, related time series data, categorical data, and geolocation data.
3. It reviews AWS services for forecasting, including Amazon SageMaker Canvas, Amazon Forecast, Amazon SageMaker JumpStart, and Amazon SageMaker built-in algorithms.
This document provides an introduction to key concepts in artificial intelligence including object detection using Python. It discusses input datasets, model training, and output predictions. As an example, it describes how object detection works by taking an image as input, training a model, and outputting a prediction of the classified object and its position in the image. It also references providing a demonstration of basic Python and Coolab for object detection.
This document discusses data transformation in AWS. It covers the importance of data transformation in terms of quantity, quality, and noise/compatibility. It then describes common transformation methods like extraction, parsing, cleaning, and enrichment. Several AWS services for data transformation are presented, including AWS Glue, AWS DataBrew, AWS Data Pipeline, Amazon SageMaker Data Wrangler, and notebooks. These services are compared based on difficulty, execution times, and costs.
Automation: from local test to production deploy - 2020-11-05Alessandra Bilardi
CloudConf 2020 in Streaming
Talk about a sample of Automation Solution from local to production
https://2020.cloudconf.it/
https://github.com/bilardi/aws-saving/
https://github.com/bilardi/aws-simple-pipeline/
This document provides an overview of solving a Rubik's cube, including its history, features, notation, and algorithms. It describes how Ernő Rubik invented the Rubik's cube in 1974 and discusses its notation system using letters and prime symbols to denote clockwise and counterclockwise turns of different sides. The document outlines the levels approach to solving a Rubik's cube and includes some edge and corner algorithms like the sexy move to solve each level.
AWS Summit 2018 Milano
Our part of AWS presentation: Managed Relational Databases
https://www.slideshare.net/AmazonWebServices/database-relazionali-gestiti
2. Oggi
1. Come è fatto un led?
2. Che cosa è un circuito?
3. Che cosa è una breadboard?
4. Creiamo un circuito con un led
5. Come è fatto un led RGB?
6. Creiamo un circuito con un led RGB
3. Come è fatto un led?
● Anodo, positivo +
● Catodo, negativo -
● Filamento
● Direzione della corrente + -
4. Cosa è un circuito?
● Anodo +
● Catodo -
● Direzione + -