Databricks Apache Spark Developer Certification Antonio Cachuan
Antonio Martin Cachuan Alipazaga was granted a Databricks Certified Developer - Apache Spark 2.x for Python certification on April 27, 2019. The certificate ID for this certification is 0000000031. This certification demonstrates proficiency in developing applications using Apache Spark 2.x with Python.
This document provides details about a Cloudera Big Data Architecture workshop held from December 11-13, 2018. The workshop was led by Antonio Cachuan and provided training on Cloudera's big data architecture and solutions over a three day period from the start date of December 11th through the end date of December 13th, 2018.
Antonio Martín Cachuán Alipázaga completed the KM204G course on IBM InfoSphere DataStage Essentials (version 11.5) on November 30, 2017. The document certifies that Antonio successfully finished the essentials training for IBM InfoSphere DataStage.
Databricks Apache Spark Developer Certification Antonio Cachuan
Antonio Martin Cachuan Alipazaga was granted a Databricks Certified Developer - Apache Spark 2.x for Python certification on April 27, 2019. The certificate ID for this certification is 0000000031. This certification demonstrates proficiency in developing applications using Apache Spark 2.x with Python.
This document provides details about a Cloudera Big Data Architecture workshop held from December 11-13, 2018. The workshop was led by Antonio Cachuan and provided training on Cloudera's big data architecture and solutions over a three day period from the start date of December 11th through the end date of December 13th, 2018.
Antonio Martín Cachuán Alipázaga completed the KM204G course on IBM InfoSphere DataStage Essentials (version 11.5) on November 30, 2017. The document certifies that Antonio successfully finished the essentials training for IBM InfoSphere DataStage.
This document is about a course titled "Importing Data in Python (Part 2)" by Antonio Martín Cachuán Alipázaga. The course number is 3,955,612 and focuses on techniques for importing and working with data in the Python programming language.
This document is about a Python course titled "Importing Data in Python (Part 1)" by Antonio Martín Cachuán Alipázaga. The course teaches students how to import different types of data into Python programs for analysis and manipulation. Students will learn the fundamentals of importing CSV files, JSON data, XML documents and more into Python.
Antonio Martín Cachuán Alipázaga has completed the Deep Learning in Python course. The course number is 2,997,280. The course title is Deep Learning in Python.
Antonio Martín Cachuán Alipázaga is taking an introductory Python for Data Science course. The course number is 2,338,974. The document provides Antonio's name and details about the Python course he is enrolled in.
Antonio Martín Cachuán Alipázaga completed the Python Data Science Toolbox (Part 1) course. The course teaches fundamental Python programming and data science tools and techniques. It provides a foundation for performing data analysis and visualization with Python.
El documento es un diploma que otorga a Antonio Martín Cachuán Alipázagala diplomatura de Estudios en Estadística Aplicada de la Facultad de Ciencias e Ingeniería. Antonio completó satisfactoriamente los estudios entre agosto de 2016 y abril de 2017 con un total de 174 horas en cursos como Procedimientos Básicos Estadísticos, Técnicas de Predicción, Técnicas de Muestreo, Análisis Multivariado y Análisis de Datos Categóricos. El diploma fue firmado por
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
This document is about a course titled "Importing Data in Python (Part 2)" by Antonio Martín Cachuán Alipázaga. The course number is 3,955,612 and focuses on techniques for importing and working with data in the Python programming language.
This document is about a Python course titled "Importing Data in Python (Part 1)" by Antonio Martín Cachuán Alipázaga. The course teaches students how to import different types of data into Python programs for analysis and manipulation. Students will learn the fundamentals of importing CSV files, JSON data, XML documents and more into Python.
Antonio Martín Cachuán Alipázaga has completed the Deep Learning in Python course. The course number is 2,997,280. The course title is Deep Learning in Python.
Antonio Martín Cachuán Alipázaga is taking an introductory Python for Data Science course. The course number is 2,338,974. The document provides Antonio's name and details about the Python course he is enrolled in.
Antonio Martín Cachuán Alipázaga completed the Python Data Science Toolbox (Part 1) course. The course teaches fundamental Python programming and data science tools and techniques. It provides a foundation for performing data analysis and visualization with Python.
El documento es un diploma que otorga a Antonio Martín Cachuán Alipázagala diplomatura de Estudios en Estadística Aplicada de la Facultad de Ciencias e Ingeniería. Antonio completó satisfactoriamente los estudios entre agosto de 2016 y abril de 2017 con un total de 174 horas en cursos como Procedimientos Básicos Estadísticos, Técnicas de Predicción, Técnicas de Muestreo, Análisis Multivariado y Análisis de Datos Categóricos. El diploma fue firmado por
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge