The document outlines 7 steps to machine learning: 1) Data collection which involves gathering images from sources like Kaggle. 2) Data pre-processing to prepare the raw data for modeling. 3) Model selection where the appropriate machine learning model is chosen such as linear regression or neural networks. 4) Training the model using portions of the data. 5) Evaluation to measure the model's performance during training. 6) Tuning to optimize the model by adjusting hyperparameters. 7) Using the model to make predictions on new data.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
Linear regression
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
Run XGBoost with Amazon SageMaker (AIM335) - AWS re:Invent 2018Amazon Web Services
XGBoost makes applying machine learning (ML) to real-world scenarios easy and powerful. Amazon SageMaker has XGBoost built in, and this enables the transition of ML models from training to production at scale. In this chalk talk, we discuss the details of using XGBoost on Amazon SageMaker, and we cover how to train and deploy ML models in a way that is simple, powerful, and scalable.
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotAmazon Web Services
Amazon SageMaker AutoPilot è una funzionalità di Amazon SageMaker che crea automaticamente il miglior modello di apprendimento automatico per il tuo set di dati. Con SageMaker Autopilot, si fornisce un set di dati tabellare e si seleziona la variabile target da prevedere, che può essere numerica o categorica. SageMaker Autopilot esplorerà automaticamente diverse soluzioni per trovare il modello migliore. È quindi possibile distribuire direttamente il modello in produzione con un solo clic o esplorare le soluzioni consigliate con Amazon SageMaker Studio per migliorare ulteriormente la qualità del modello. In questo webinar approfondiremo questa capacità, con dimostrazioni pratiche su come utilizzare il servizio.
This document provides an overview of machine learning capabilities on AWS. It begins with introductions to machine learning concepts and the benefits of performing machine learning in the cloud. It then describes various AWS machine learning services like Amazon SageMaker for building, training, and deploying models. The rest of the document explores Amazon SageMaker in more detail, demonstrating how to train models using built-in algorithms or custom containers and deploy them for inference.
BigdataConference Europe - BigQuery MLMárton Kodok
One of the hottest topics in database land these days is BigQuery ML. A new way to use machine learning on top of tabular data straight on your tables without leaving the query editor.
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets.
In this demo session, we are going to demonstrate common marketing Machine Learning use cases how to build, train, eval and predict, your own scalable machine learning models using SQL language.
The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as:
– Multiclass logistic regression for classification
– K-means clustering
– Matrix factorization
– ARIMA time series predictions
– Import TensorFlow models for prediction in BigQuery
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
-Linear regression
-Multiclass logistic regression for classification
-K-means clustering
-Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
Scaling AutoML-Driven Anomaly Detection With LuminaireDatabricks
Luminaire is an open-source Python library developed by Zillow to provide scalable and automated time series anomaly detection. It uses AutoML techniques to optimize model selection and configuration with minimal input. Luminaire profiles and preprocesses time series data, trains both batch and streaming models, scores anomalies, and supports distributed training and scoring on large datasets using Spark. Zillow uses Luminaire to monitor data quality across many metrics and data sources that power its products and services.
The document outlines 7 steps to machine learning: 1) Data collection which involves gathering images from sources like Kaggle. 2) Data pre-processing to prepare the raw data for modeling. 3) Model selection where the appropriate machine learning model is chosen such as linear regression or neural networks. 4) Training the model using portions of the data. 5) Evaluation to measure the model's performance during training. 6) Tuning to optimize the model by adjusting hyperparameters. 7) Using the model to make predictions on new data.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
Linear regression
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
Run XGBoost with Amazon SageMaker (AIM335) - AWS re:Invent 2018Amazon Web Services
XGBoost makes applying machine learning (ML) to real-world scenarios easy and powerful. Amazon SageMaker has XGBoost built in, and this enables the transition of ML models from training to production at scale. In this chalk talk, we discuss the details of using XGBoost on Amazon SageMaker, and we cover how to train and deploy ML models in a way that is simple, powerful, and scalable.
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotAmazon Web Services
Amazon SageMaker AutoPilot è una funzionalità di Amazon SageMaker che crea automaticamente il miglior modello di apprendimento automatico per il tuo set di dati. Con SageMaker Autopilot, si fornisce un set di dati tabellare e si seleziona la variabile target da prevedere, che può essere numerica o categorica. SageMaker Autopilot esplorerà automaticamente diverse soluzioni per trovare il modello migliore. È quindi possibile distribuire direttamente il modello in produzione con un solo clic o esplorare le soluzioni consigliate con Amazon SageMaker Studio per migliorare ulteriormente la qualità del modello. In questo webinar approfondiremo questa capacità, con dimostrazioni pratiche su come utilizzare il servizio.
This document provides an overview of machine learning capabilities on AWS. It begins with introductions to machine learning concepts and the benefits of performing machine learning in the cloud. It then describes various AWS machine learning services like Amazon SageMaker for building, training, and deploying models. The rest of the document explores Amazon SageMaker in more detail, demonstrating how to train models using built-in algorithms or custom containers and deploy them for inference.
BigdataConference Europe - BigQuery MLMárton Kodok
One of the hottest topics in database land these days is BigQuery ML. A new way to use machine learning on top of tabular data straight on your tables without leaving the query editor.
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets.
In this demo session, we are going to demonstrate common marketing Machine Learning use cases how to build, train, eval and predict, your own scalable machine learning models using SQL language.
The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as:
– Multiclass logistic regression for classification
– K-means clustering
– Matrix factorization
– ARIMA time series predictions
– Import TensorFlow models for prediction in BigQuery
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
-Linear regression
-Multiclass logistic regression for classification
-K-means clustering
-Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
Scaling AutoML-Driven Anomaly Detection With LuminaireDatabricks
Luminaire is an open-source Python library developed by Zillow to provide scalable and automated time series anomaly detection. It uses AutoML techniques to optimize model selection and configuration with minimal input. Luminaire profiles and preprocesses time series data, trains both batch and streaming models, scores anomalies, and supports distributed training and scoring on large datasets using Spark. Zillow uses Luminaire to monitor data quality across many metrics and data sources that power its products and services.
This webinar, hosted by SigOpt co-founder and CEO Scott Clark, explains how advanced features can help you achieve your modeling goals. These features include metric definition and multimetric optimization, conditional parameters, and multitask optimization for long training cycles.
Amazon SageMaker is a fully managed Machine Learning service which facilitates seamless adoption of #MachineLearning across various industries! Jayesh is walking us through details of SageMaker with demo in this talk!
Ludwig: A code-free deep learning toolbox | Piero Molino, Uber AIData Science Milan
The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.
Bio:
Piero Molino is a Senior Research Scientist at Uber AI with focus on machine learning for language and dialogue. Piero completed a PhD on Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs.
A case study in using ibm watson studio machine learning services ibm devel...Einar Karlsen
This IBM Developer article shows various ways of predicting customer churn using IBM Watson Studio ranging from a semi-automated approach using the Model Builder, a diagrammatic approach using SPSS Modeler Flows to a fully programmed style using Jupyter notebooks.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Augmenting Machine Learning with Databricks Labs AutoML ToolkitDatabricks
<p>Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.</p><p>In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.</p>
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
Automated machine learning in Azure allows users to train models without extensive data science knowledge. It automatically tries different preprocessing techniques and algorithms in parallel to find the best performing model. Users can create an automated machine learning job that configures settings like the training script and compute target before starting a run. The automated process prepares data, trains and evaluates multiple models, and can deploy the best performing model as a predictive service.
Cloud study jams workshop - classify images of clouds in the cloud with aut...Nicolas Bortolotti
AutoML Vision helps developers with limited ML expertise train high quality image recognition models. Once you upload images to the AutoML UI, you can train a model that will be immediately available on GCP for generating predictions via an easy to use REST API
In this lab you will upload images to Cloud Storage and use them to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.)
Augmenting Machine Learning with Databricks Labs AutoML ToolkitDatabricks
Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.
In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.
Google cloud Professional Data Engineer practice exam test 2020SkillCertProExams
Google Cloud Certified Professional Data Engineer Exam questions pdf
https://skillcertpro.com/yourls/gcpdataeng
Want to practice more questions?We have 390+ Practice set questions for Google Cloud Certified -Professional Data Engineer certification (Taken from previous exams)
BigQuery is a powerful tool for marketers because it allows for fast SQL queries across large datasets, seamless integration with Google products like Analytics and Machine Learning, and cheap pricing. The document discusses two use cases: 1) integrating CRM and Analytics data to attribute marketing channels to sales outcomes, and 2) using BigQuery ML to cluster user groups, build predictive models, and activate remarketing lists. In conclusion, the document emphasizes that BigQuery makes data warehousing, exploration, and basic machine learning surprisingly easy and affordable.
This document outlines the preparation and process of classification modeling. It discusses the objectives of the training, which are for participants to be able to prepare for and conduct classification modeling. It describes dividing data into training, validation, and test sets. Other topics covered include determining modeling techniques, setting model parameters, and evaluating model performance using metrics like accuracy for classification tasks. The document provides information about the training, including the competencies, learning outcomes, duration, tools used, and training team.
The document describes the author's approach to building a machine learning pipeline for a Kaggle competition to predict product categories from tabular data. The pipeline includes: 1) Loading and processing the training, testing, and submission data, 2) Performing cross-validated model training and evaluation using algorithms like XGBoost, LightGBM and CatBoost, 3) Averaging the results to generate final predictions and create a submission file. The author aims to share details of algorithms, hardware performance, and results in subsequent blog posts.
Tuning for Systematic Trading: Talk 3: Training, Tuning, and Metric StrategySigOpt
This talk explains how you can train and tune efficiently using metric strategy to assign, store, and optimize a variety of metrics, even changing them over time. Tobias Andreassen, who supports a number of our systematic trading customers, explained how he helps customers tune more efficiently with these SigOpt features in real-world scenarios.
GluonCV는 컴퓨터 비전에 특화된 Apache MXNet의 딥러닝 툴킷입니다. 본 실습에서는 GluonCV가 제공하는 최신 컴퓨터 비전 알고리즘의 기(旣) 훈련(Pre-trained) 모델을 사용하여 이미지 인식, 객체 검출, 영역 구분 등의 다양한 문제를 해결합니다. GluonCV의 설치에서부터 모델 학습과 배포에 이르는 전과정을 따라해 볼 수 있습니다.
The document discusses AdWords scripts for managing multiple AdWords accounts from a central MCC account using JavaScript. It provides an overview of MCC scripts, how to get started with them, and examples of common tasks like accessing child accounts, selecting a specific account, processing accounts in parallel, and returning results.
This document discusses tools and services for optimizing Google Ads accounts, including Kratu for discovering optimization opportunities. It describes profiling accounts, using services like Targeting Idea and Traffic Estimator for keyword research and estimates, and optimizing through an iterative process. The document demonstrates Kratu for analyzing account data and displaying issues and opportunities, and provides resources for using Kratu with a backend like AwReporting.
Data technology can help companies predict outcomes through simulations, find unexpected relationships in large data sets, and monitor situations in real-time. OpenSistemas is a company that specializes in data management, analysis, storage and visualization using technologies like Apache Spark, machine learning, and cloud integration. They provide services in areas like data processing, analytics, visualization and cloud to help clients strengthen the strategic value of their information.
Data Platform & Analytics OpenSistemas MSFT PlaybookOpenSistemas
This document is a playbook for Microsoft partners to develop a practice focused on data platforms and analytics. It provides guidance on defining a practice focus, understanding the opportunities in data modernization, business analytics, and IoT. It also includes case studies and a maturity model for data and analytics practices. The playbook was created by Microsoft and partner companies to help other partners optimize and grow their Azure practices focused on data and analytics.
This webinar, hosted by SigOpt co-founder and CEO Scott Clark, explains how advanced features can help you achieve your modeling goals. These features include metric definition and multimetric optimization, conditional parameters, and multitask optimization for long training cycles.
Amazon SageMaker is a fully managed Machine Learning service which facilitates seamless adoption of #MachineLearning across various industries! Jayesh is walking us through details of SageMaker with demo in this talk!
Ludwig: A code-free deep learning toolbox | Piero Molino, Uber AIData Science Milan
The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.
Bio:
Piero Molino is a Senior Research Scientist at Uber AI with focus on machine learning for language and dialogue. Piero completed a PhD on Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs.
A case study in using ibm watson studio machine learning services ibm devel...Einar Karlsen
This IBM Developer article shows various ways of predicting customer churn using IBM Watson Studio ranging from a semi-automated approach using the Model Builder, a diagrammatic approach using SPSS Modeler Flows to a fully programmed style using Jupyter notebooks.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Augmenting Machine Learning with Databricks Labs AutoML ToolkitDatabricks
<p>Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.</p><p>In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.</p>
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
Automated machine learning in Azure allows users to train models without extensive data science knowledge. It automatically tries different preprocessing techniques and algorithms in parallel to find the best performing model. Users can create an automated machine learning job that configures settings like the training script and compute target before starting a run. The automated process prepares data, trains and evaluates multiple models, and can deploy the best performing model as a predictive service.
Cloud study jams workshop - classify images of clouds in the cloud with aut...Nicolas Bortolotti
AutoML Vision helps developers with limited ML expertise train high quality image recognition models. Once you upload images to the AutoML UI, you can train a model that will be immediately available on GCP for generating predictions via an easy to use REST API
In this lab you will upload images to Cloud Storage and use them to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.)
Augmenting Machine Learning with Databricks Labs AutoML ToolkitDatabricks
Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.
In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.
Google cloud Professional Data Engineer practice exam test 2020SkillCertProExams
Google Cloud Certified Professional Data Engineer Exam questions pdf
https://skillcertpro.com/yourls/gcpdataeng
Want to practice more questions?We have 390+ Practice set questions for Google Cloud Certified -Professional Data Engineer certification (Taken from previous exams)
BigQuery is a powerful tool for marketers because it allows for fast SQL queries across large datasets, seamless integration with Google products like Analytics and Machine Learning, and cheap pricing. The document discusses two use cases: 1) integrating CRM and Analytics data to attribute marketing channels to sales outcomes, and 2) using BigQuery ML to cluster user groups, build predictive models, and activate remarketing lists. In conclusion, the document emphasizes that BigQuery makes data warehousing, exploration, and basic machine learning surprisingly easy and affordable.
This document outlines the preparation and process of classification modeling. It discusses the objectives of the training, which are for participants to be able to prepare for and conduct classification modeling. It describes dividing data into training, validation, and test sets. Other topics covered include determining modeling techniques, setting model parameters, and evaluating model performance using metrics like accuracy for classification tasks. The document provides information about the training, including the competencies, learning outcomes, duration, tools used, and training team.
The document describes the author's approach to building a machine learning pipeline for a Kaggle competition to predict product categories from tabular data. The pipeline includes: 1) Loading and processing the training, testing, and submission data, 2) Performing cross-validated model training and evaluation using algorithms like XGBoost, LightGBM and CatBoost, 3) Averaging the results to generate final predictions and create a submission file. The author aims to share details of algorithms, hardware performance, and results in subsequent blog posts.
Tuning for Systematic Trading: Talk 3: Training, Tuning, and Metric StrategySigOpt
This talk explains how you can train and tune efficiently using metric strategy to assign, store, and optimize a variety of metrics, even changing them over time. Tobias Andreassen, who supports a number of our systematic trading customers, explained how he helps customers tune more efficiently with these SigOpt features in real-world scenarios.
GluonCV는 컴퓨터 비전에 특화된 Apache MXNet의 딥러닝 툴킷입니다. 본 실습에서는 GluonCV가 제공하는 최신 컴퓨터 비전 알고리즘의 기(旣) 훈련(Pre-trained) 모델을 사용하여 이미지 인식, 객체 검출, 영역 구분 등의 다양한 문제를 해결합니다. GluonCV의 설치에서부터 모델 학습과 배포에 이르는 전과정을 따라해 볼 수 있습니다.
The document discusses AdWords scripts for managing multiple AdWords accounts from a central MCC account using JavaScript. It provides an overview of MCC scripts, how to get started with them, and examples of common tasks like accessing child accounts, selecting a specific account, processing accounts in parallel, and returning results.
This document discusses tools and services for optimizing Google Ads accounts, including Kratu for discovering optimization opportunities. It describes profiling accounts, using services like Targeting Idea and Traffic Estimator for keyword research and estimates, and optimizing through an iterative process. The document demonstrates Kratu for analyzing account data and displaying issues and opportunities, and provides resources for using Kratu with a backend like AwReporting.
Data technology can help companies predict outcomes through simulations, find unexpected relationships in large data sets, and monitor situations in real-time. OpenSistemas is a company that specializes in data management, analysis, storage and visualization using technologies like Apache Spark, machine learning, and cloud integration. They provide services in areas like data processing, analytics, visualization and cloud to help clients strengthen the strategic value of their information.
Data Platform & Analytics OpenSistemas MSFT PlaybookOpenSistemas
This document is a playbook for Microsoft partners to develop a practice focused on data platforms and analytics. It provides guidance on defining a practice focus, understanding the opportunities in data modernization, business analytics, and IoT. It also includes case studies and a maturity model for data and analytics practices. The playbook was created by Microsoft and partner companies to help other partners optimize and grow their Azure practices focused on data and analytics.
El futuro Data Driven en e-Learning y RR.HH.OpenSistemas
El documento describe cómo los datos masivos y continuos generados por personas y objetos están dando lugar a un mundo impulsado por los datos, y cómo esto está afectando a los recursos humanos y la formación. Se analizan casos reales de aplicación de análisis de datos a procesos de selección, análisis operativo, benchmarking y predicción de bajas. También se discuten las implicaciones éticas y legales del uso de datos personales, y la necesidad de gestionar las expectativas sobre los resultados de los modelos predictivos.
Apache spark y cómo lo usamos en nuestros proyectosOpenSistemas
This document discusses Apache Spark and how it is used in projects at OpenSistemas. It provides a brief history of Hadoop and the limitations that led to the development of Apache Spark. It then explains what Apache Spark is, how it was designed from the ground up to take advantage of memory, and how it is influenced by Scala. It also introduces the Kappa architecture as an alternative to the Lambda architecture that only requires maintaining a single stream processing codebase. Finally, it provides examples of how OpenSistemas uses the Kappa architecture with Apache Spark streaming for various clients in domains like telecommunications, insurance, energy, and more.
The document discusses several tips for software development and business including:
1) Ensure immediate visibility for projects to avoid "the march of death".
2) Understand that clients want a solution to their "pain" rather than experiencing the solution process.
3) Recognize that software is never finished and the goal is to deliver value-adding versions to clients.
Cómo crear ports en FreeBSD #PicnicCode2015OpenSistemas
Este documento explica los FreeBSD ports, que es una colección de directorios que contienen la información necesaria para descargar, compilar e instalar software de terceros en FreeBSD de forma automatizada. Los ports siguen un proceso estandarizado que incluye descargar el código fuente, aplicar parches, compilar el software y crear paquetes de instalación. Los archivos Makefile guían este proceso y cada port contiene la información necesaria como checksums, parches y listas de archivos.
OpenSistemas es una compañía española con más de diez años de experiencia en proyectos y productos innovadores relacionados con análisis de datos, educación y gestión de contenidos sobre plataformas Open Source y sistemas operativos Linux. Ofrece servicios de consultoría, desarrollo de software, formación y soporte especializado en tecnologías open source para áreas como análisis de datos, educación, nube y gestión de contenidos. Sus clientes incluyen universidades, centros de innovación y empresas de telecomunicaciones.
Drupal 7. Puesta en producción en sistemas multientornoOpenSistemas
Este documento presenta un procedimiento para implementar sistemas Drupal en varios entornos (local, desarrollo, preproducción y producción). Explica las herramientas de Drupal como Features, módulos de actualización, exportación de nodos y Feeds que permiten transferir datos entre entornos. También detalla los pasos para subir contenido local a desarrollo y para pasar cambios a producción de manera controlada.
osBrain: una herramienta para la inversión automática en bolsa y mercados de ...OpenSistemas
Este documento describe el desarrollo de una herramienta llamada Wopr para la inversión automática en bolsa y mercados de divisas. Wopr tiene una arquitectura modular y distribuida que incluye nodos especializados. También incluye una interfaz gráfica llamada XWopr. El documento detalla los objetivos, herramientas, componentes y estrategias de inversión implementadas como Elliott y detección de patrones, así como estadísticas sobre pruebas y desarrollo de código.
OpenSistemas es una compañía española con más de diez años de experiencia en proyectos y productos relacionados con análisis de datos, educación y gestión de contenidos sobre plataformas Open Source. Ofrece servicios de soporte especializado en tecnologías Open Source como Big Data, Pentaho, almacenamiento NoSQL, nube, plataformas LMS y CMS, entre otras áreas. El soporte incluye mantenimiento correctivo, evolutivo y preventivo siguiendo una metodología basada en ITIL.
Proceso de liberación en el marco legal del código abierto - OpenSistemasOpenSistemas
El documento describe el proceso de liberar software como código abierto, incluyendo elegir una licencia libre como GPL o MIT, seguir buenas prácticas como documentar y empaquetar el software, y crear una infraestructura de sitio web para dar soporte a usuarios y colaboradores. El objetivo principal es crear una comunidad en torno al proyecto de software libre.
Virtualization - Solaris LDOMs - OpenSistemasOpenSistemas
The document discusses Oracle VM Server for Sparc (formerly known as Logical Domains or LDOMs), a type I hypervisor for Sparc T platform servers. It provides an overview of LDOMs, describing them as logical domains that allow full virtualization through the hypervisor. It also discusses requirements like specific CPUs that support chip multithreading and the ability to assign threads to virtual machines. Finally, it touches on installing LDOMs and potentially needing a firmware upgrade.
CACert - A Community-driven Certification Authority - OpenSistemasOpenSistemas
This document discusses Opensistemas, a global IT solutions company specialized in open source and Linux platforms. It details Opensistemas' vision to be an international leader in open source technologies, their mission to deliver effective solutions while promoting employee development, and their values like delivering customer solutions and commitment to open source. It also provides Opensistemas' contact information and describes their presence across multiple countries.
This document discusses several influential figures in the history of free and open source software (FLOSS). It begins by explaining how FLOSS and computer science histories are parallel, as early computers were openly shared and customized. It notes that FLOSS evolution has been driven by key individuals. The document then provides several names and short biographies of influential FLOSS leaders, including Richard Stallman, Linus Torvalds, Eric Raymond, Alan Cox, Bruce Perens, Ian Murdock, Miguel de Icaza, Larry Wall, and Theo de Raadt. It instructs the reader to research these individuals and prepare a presentation with more information on each.
Business Intelligence and Pentaho Services - OpenSistemasOpenSistemas
Open Sistemas provides business intelligence (BI) services and open source solutions. Their approach involves analyzing business needs, defining goals, normalizing data, and creating reports, analyses, and BI platforms. Their offerings include Pentaho, a complete open source BI suite, as well as ETL, OLAP, and reporting tools. Open Sistemas has experience implementing BI solutions for various organizations and providing services such as data integration, custom software development, and open source project integration.
EasyGTD is a personal task management service that uses the Getting Things Done (GTD) methodology to help users get organized, stay focused, reduce stress, and improve professional performance. The GTD methodology involves capturing all tasks, clarifying each task, organizing tasks into projects, doing tasks, and regularly reviewing the system. EasyGTD provides an online platform and resources to implement the GTD methodology through five main steps - capturing, clarifying, organizing, doing, and reviewing.
EasyGTD es un servicio en línea basado en la metodología GTD para la gestión de tareas personales y proyectos. Ofrece cinco pasos para organizar el trabajo: capturar ideas, clarificar acciones, organizar tareas por proyectos, completar acciones, y revisar periódicamente el sistema. Además, provee cursos, videos y una comunidad para apoyar la productividad de usuarios con altas cargas de trabajo.
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
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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.
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.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
7. Train your models at scale
Cloud Machine
Learning Engine
● Write your model using the Dataset and Estimator APIs
● Stage it in Cloud Storage
● Store training and evaluation datasets in Cloud Storage
● Configure your cluster (CPU, memory, GPU/TPU) — can
also run in local mode
● Write your checkpoints to Cloud Storage
● Tune the hyperparameters of your model1
● Monitor from the Google Cloud Console, Stackdriver
Logging, TensorBoard and the command-line
1
http://bit.ly/mle-hypertune
8. Host your trained model on the cloud
● Online prediction with serverless, fully-managed hosting
● Batch prediction at scale
● Manage deployed models and versions
● Restrict and audit access to your deployed models
● Monitor your predictions in Stackdriver Logging
Cloud Machine
Learning Engine
23. AI models optimized
for Edge
Training
& inference
AI
TensorFlow Lite
& Android NN API
Cloud IoT Edge Cloud AI & servicesSoftware
Edge Cloud
CPUs, GPUs
Hardware
CPUs, GPUs
Cloud TPU
AI models
Edge TPU
27. BigQuery ML
● Petabyte-scale, fast linear and binary logistic regression
● SQL-like language:
○ CREATE MODEL
○ ML.EVALUATE, ML.ROC_CURVE
○ ML.PREDICT
● Automatic learning rate adjustment
● L1
and/or L2
regularization
● Batch prediction within BigQuery
● Exportable models1
:
○ ML.WEIGHTS
○ ML.FEATURE_INFO
BigQuery
NEW
1
http://bit.ly/bqml-online
28. Training a model
CREATE MODEL `bqml_tutorial.natality_model`
OPTIONS (model_type='linear_reg',
input_label_cols=['weight_pounds']) AS
SELECT weight_pounds,
is_male,
gestation_weeks,
mother_age,
CAST(mother_race AS STRING) AS mother_race
FROM `bigquery-public-data.samples.natality`
WHERE weight_pounds IS NOT NULL
BigQuery
29. SELECT * FROM
ML.EVALUATE(
MODEL `bqml_tutorial.natality_model`, (
SELECT weight_pounds,
is_male,
gestation_weeks,
mother_age,
CAST(mother_race AS STRING) AS mother_race
FROM `bigquery-public-data.samples.natality`
WHERE weight_pounds IS NOT NULL)
)
BigQuery
Returns: mean_absolute_error, mean_squared_error, mean_squared_log_error,
median_absolute_error, r2_score, explained_variance
Evaluating a model
30. Getting predictions from a model
SELECT predicted_weight_pounds FROM
ML.PREDICT(
MODEL `bqml_tutorial.natality_model`, (
SELECT is_male,
gestation_weeks,
mother_age,
CAST(mother_race AS STRING) AS mother_race
FROM `bigquery-public-data.samples.natality`
WHERE state = "WY")
)
BigQuery
44. ● Image properties (dominant colors, crop hints)
● Landmark recognition
● Handwriting recognition
● Object detection
● General availability
● Additional file types: PDF and TIFF
Cloud Vision API
Other features
NEW
NEW
NEW
NEW
45. Cloud
Speech-to-Text
Cloud Video
Intelligence API
Cloud Natural
Language API
Cloud
Translation API
● Language identification
● Word-level confidence scores
● Multi-participant recognition
Cloud
Text-to-Speech
● DeepMind WaveNet voices
● Output channel optimization
Other pretrained models
NEW
NEW
NEW
NEW
NEW
47. ● Based on transfer learning and neural architecture search
● Prediction available from REST APIs
● For (beta): domain-specific translated pairs
● For (beta): predict domain-specific
categories (single or multi-label classification)
● For (beta): detect custom objects or predict
domain-specific labels
Cloud Auto ML
Add your domain-specific knowledge
50. ● Phone Gateway (beta): assign a phone number to a virtual
agent, with speech recognition, speech synthesis and
natural language understanding
● Knowledge Connectors (beta): understand unstructured
documents like FAQs or knowledge base articles and
complement your pre-built intents
● Automatic Spelling Correction (beta)
● Sentiment Analysis (beta)
● Text-to-Speech (beta)
● Enables
DialogFlow
Enterprise Edition
Interact via natural language
NEW