Apache Spark & ML Workflows

May 11, 20180 likes143 views

This document discusses how Apache Spark can be used in machine learning workflows. It covers typical Spark components and cluster hardware configurations. It then discusses how Spark fits into the ML modeling lifecycle, from loading and preparing data to training, evaluating, and deploying models. Specific examples covered include using Spark for distributed model training, grid search, and batch scoring. The document concludes by summarizing how Spark can be used for large-scale training of single and multiple models and applying models to many inputs.

Apache Spark &
ML Workflows
Juliet Hougland
@j_houg
October 2017

Apache Spark Components
Spark Core
Dataframe API
Spark
SQL
Spark
MLLib
Spark
Streaming

Typical Hardware
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Storage Compute

Clusters in a Data Center
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Distributed Computation
Distributed Storage

Clusters and Spark
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Storage Compute
Driver
Executors

How can Spark fit
in the ML modeling
lifecycle?

Churn Prediction Dataset
KS, 128, 415, 382-4657, no, yes, 25, 265.1, 110, 45.07, 197.4, 99, 16.78, 244.7, 91, 11.01, 10, 3, 2.7, 1, False.
OH, 107, 415, 371-7191, no, yes, 26, 161.6, 123, 27.47, 195.5, 103, 16.62, 254.4, 103, 11.45, 13.7, 3, 3.7, 1, False
NJ, 137, 415, 358-1921, no, no, 0, 243.4, 114, 41.38, 121.2, 110, 10.3, 162.6, 104, 7.32, 12.2, 5, 3.29, 0, False.
OH, 84, 408, 375-9999, yes, no, 0, 299.4, 71, 50.9, 61.9, 88, 5.26, 196.9, 89, 8.86, 6.6, 7, 1.78, 2, True.
OK, 75, 415, 330-6626, yes, no, 0, 166.7, 113, 28.34, 148.3, 122, 12.61, 186.9, 121, 8.41, 10.1, 3, 2.73, 3, False
Discrete
Features

You have a few options:
Well, how did you save it?
● Pickle
● Joblib
● PMML
● Custom

Pickle and Joblib have issues
“Pickles are for delis”
● Insecure
● Not Portable
● Big
● Slow
pyvideo.org/pycon-us-2014/pickles-are-for-delis-not-

Scoring with a REST server:
Open Scoring

Distributed Batch Model Scoring:
JPMML and Spark

How can Spark be used
in ML Workflows?
1. Training a single model on a large data set
2. Training many models
3. Applying a model to many inputs

Thanks!
Juliet Hougland
@j_houg
October 2017

The document discusses optimizing Hadoop clusters on AWS for bursty analysis demands. It presents three solutions: a permanent EC2 cluster, using Elastic MapReduce with data stored in S3, and using an EBS-backed HDFS cluster with task-only nodes. Performance results are shown for different workloads, showing the EBS HDFS solution has the lowest cost for most scenarios. Tips are also provided for setting up Hadoop on EC2.

Data Lake ETL in the Cloud with ADFMark Kromer

This document discusses using Azure Data Factory (ADF) for data lake ETL processes in the cloud. It describes how ADF can ingest data from on-premises, cloud, and SaaS sources into a data lake for preparation, transformation, enrichment, and serving to downstream analytics or machine learning processes. The document also provides several links to YouTube videos and articles about using ADF for these tasks.

Data cleansing and data prep with synapse data flowsMark Kromer

Elasticsearch Meetup - August 2018 - SocialCopsAayush Sarva

This document discusses near real-time data synchronization between a primary datastore like MongoDB and Elasticsearch by tailing the primary's operation logs. It explains why this is useful for faster search, analytics and aggregations. Key terms are introduced like replica sets, oplogs and tailable cursors. Concepts covered include locating the logs, reading them continuously and putting the data into scalable stores. Considerations for implementation involve preventing overload, failover and minimizing lag. A demo of oplog tailing is provided.

Hyperspace: An Indexing Subsystem for Apache SparkDatabricks

Databricks with R: Deep DiveDatabricks

In this presentation we'll explain how to use the R programming language with Spark using a Databricks notebook and the SparkR package. We'll discuss how to push data wrangling to the Spark nodes for massive scale and how to bring it back to a single node so we can use open source packages on the data. We'll demonstrate converting SQL tables into R distributed data frames and how to convert R data frames to SQL tables. We'll also have a look at how to train predictive models using data distributed over the Spark nodes. Bring your popcorn. This is a fun and interesting presentation. Speaker: Bryan Cafferky

Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco

Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks. Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o Presentation demo: https://github.com/devlace/azure-databricks-anomaly

Azure Databricks & Spark @ Techorama 2018Nathan Bijnens

Spark is an open-source framework for large-scale data processing. Azure Databricks provides Spark as a managed service on Microsoft Azure, allowing users to deploy production Spark jobs and workflows without having to manage infrastructure. It offers an optimized Databricks runtime, collaborative workspace, and integrations with other Azure services to enhance productivity and scale workloads without limits.

Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Databricks

AI at ScaleAdi Polak

This document discusses AI at scale using Apache Spark on Azure. It provides an overview of Apache Spark, how it can be used for machine learning with tools like MLlib and Databricks, and how cognitive services can be combined with Spark. It also discusses using Azure services like Databricks, HDInsight and AKS for running Spark workloads at scale, and the roles of data engineers and data scientists.

How Microsoft Synapse Analytics Can Transform Your Data Analytics.pdfAddend Analytics

UNIT -IV.docxRevathiparamanathan

The document discusses Azure Synapse Analytics and its core services. It provides an overview of Azure Synapse Analytics, its high-level architecture, components and features. It discusses Azure Synapse Studio, Azure Synapse Data Integration, Synapse SQL Pools, Apache Spark for Azure Synapse and Azure Synapse security. It also discusses Azure HDInsight, its features, architecture, metastore best practices, migration practices and security and DevOps.

.Net development with Azure Machine Learning (AzureML) Nov 2014Mark Tabladillo

An Insider’s Guide to Maximizing Spark SQL PerformanceTakuya UESHIN

This document provides an overview of optimizing Spark SQL performance. It begins with introducing the speaker and their background with Spark. It then discusses reading query plans, interpreting them to understand optimizations, and tuning plans by pushing down filters, avoiding implicit casts, and other techniques. It emphasizes tracking query execution through the Spark UI to analyze jobs, stages and tasks for bottlenecks. The document aims to help understand how to maximize Spark SQL performance.

MongoDB and Azure DatabricksMongoDB

This talk will provide a brief update on Microsoft’s recent history in Open Source with specific emphasis on Azure Databricks, a fast, easy and collaborative Apache Spark-based analytics service. Attendees will learn how to integrate MongoDB Atlas with Azure Databricks using the MongoDB Connector for Spark. This integration allows users to process data in MongoDB with the massive parallelism of Spark, its machine learning libraries, and streaming API.

Synapse for mere mortalsMichael Stephenson

Spark1Dr. G. Bharadwaja Kumar

Apache Spark is a fast and general engine for large-scale data processing that eBay uses to improve user experiences, provide relevant offers, and optimize performance. Spark provides simple programming abstractions and powerful in-memory caching capabilities to enable high-performance iterative processing of large datasets. At eBay, Spark jobs are commonly run on Hadoop clusters using Yarn and process data stored in HDFS, with many jobs written in Scala. Spark is helping eBay create more value from its data and its use is expanding from experimental to everyday.

IBM Strategy for SparkMark Kerzner

This document provides an introduction and overview of Spark: - Spark is an open-source in-memory data processing engine that can handle large datasets across clusters of computers using an API in Scala, Python, or R. - IBM is heavily committed to Spark, contributing the most code and fixing the most issues reported by other organizations to continually improve the full analytics stack. - An example is presented on using Spark to predict hospital readmissions from diabetes patient data, obtaining AUC scores comparable to other published models.

Big Data Processing with Spark and Scala Edureka!

Scalable Machine Learning with PySparkLadle Patel

The document discusses scalable machine learning using PySpark. It introduces Apache Spark, an open-source framework for large-scale data processing, and how it allows for both batch and streaming data processing using its in-memory computation engine. The document also provides resources for learning Spark, including tutorials, documentation, and links to large public datasets that can be used for building scalable machine learning models.

Spark for big data analyticsEdureka!

This document discusses Apache Spark, an open-source cluster computing framework for big data processing. It provides an overview of Spark, how it fits into the Hadoop ecosystem, why it is useful for big data analytics, and hands-on analysis of data using Spark. Key features that make Spark suitable for big data analytics include simplifying data analysis, built-in machine learning and graph processing libraries, support for multiple programming languages, and faster performance than Hadoop MapReduce.

Eugene Polonichko "Azure Data Lake: what is it? why is it? where is it?"DataConf

Ai big dataconference_eugene_polonichko_azure data lake Olga Zinkevych

Topic of presentation: Azure Data Lake: what is it? why is it? where is it? The main points of the presentation: What is Azure Data Lake? Why does this technology call Microsoft Big Data? Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. It removes the complexities of ingesting and storing all of your data while making it faster to get up and running with batch, streaming, and interactive analytics. http://dataconf.com.ua/index.php#agenda #dataconf #AIBDConference

Started with-apache-sparkHappiest Minds Technologies

Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!

This Edureka Spark SQL Tutorial will help you to understand how Apache Spark offers SQL power in real-time. This tutorial also demonstrates an use case on Stock Market Analysis using Spark SQL. Below are the topics covered in this tutorial: 1) Limitations of Apache Hive 2) Spark SQL Advantages Over Hive 3) Spark SQL Success Story 4) Spark SQL Features 5) Architecture of Spark SQL 6) Spark SQL Libraries 7) Querying Using Spark SQL 8) Demo: Stock Market Analysis With Spark SQL

5 reasons why spark is in demand!Edureka!

This document discusses 5 reasons why Apache Spark is in high demand: 1) Low latency processing by keeping data in memory, 2) Support for streaming data through resilient distributed datasets (RDDs), 3) Integration of machine learning and graph processing libraries, 4) DataFrame API for easier data analysis, and 5) Ability to integrate with Hadoop for large scale data processing. It provides details on Spark's architecture and benchmarks showing its faster performance compared to Hadoop for tasks like sorting large datasets.

Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks

In this webcast, Patrick Wendell from Databricks will be speaking about Apache Spark's new 1.6 release. Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].

What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!

This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial: 1) Big Data Analytics 2) What is Apache Spark? 3) Why Apache Spark? 4) Using Spark with Hadoop 5) Apache Spark Features 6) Apache Spark Architecture 7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX 8) Demo: Analyze Flight Data Using Apache Spark

Progression by Regression: How to increase your A/B Test VelocityStitch Fix Algorithms

T-tests are the industry de facto method for analyzing A/B tests. Regression is a more general approach that allows you to also control for covariates, potentially increasing your power. This can lead to reduced run times, the ability to detect smaller changes, and higher testing velocity. Come learn why Stitch Fix uses regression-based analysis for experiments. We'll also share practical insights on how we’re enabling this in an automated platform at scale at Stitch Fix.

Deep recommendations in PyTorchStitch Fix Algorithms

The document discusses recommendations and matrix factorization models in PyTorch. It begins with an introduction and instructions for following along with a tutorial on simple matrix factorization models. It then discusses how matrix factorization works by decomposing a sparse user-item rating matrix into dense user and item embedding matrices. Biases and additional features are also discussed. It notes that recommendation models can be used to understand latent dimensions or "styles" in the data. Further techniques like variational modeling, temporal embeddings, and connections to word2vec are presented. Finally, it discusses directions for future work like factorization machines, mixture of tastes models, and non-Euclidean embedding spaces.

More Related Content

Similar to Apache Spark & ML Workflows (20)

Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Databricks

AI at ScaleAdi Polak

How Microsoft Synapse Analytics Can Transform Your Data Analytics.pdfAddend Analytics

UNIT -IV.docxRevathiparamanathan

.Net development with Azure Machine Learning (AzureML) Nov 2014Mark Tabladillo

An Insider’s Guide to Maximizing Spark SQL PerformanceTakuya UESHIN

MongoDB and Azure DatabricksMongoDB

Synapse for mere mortalsMichael Stephenson

Spark1Dr. G. Bharadwaja Kumar

IBM Strategy for SparkMark Kerzner

Big Data Processing with Spark and Scala Edureka!

Scalable Machine Learning with PySparkLadle Patel

Spark for big data analyticsEdureka!

Eugene Polonichko "Azure Data Lake: what is it? why is it? where is it?"DataConf

Ai big dataconference_eugene_polonichko_azure data lake Olga Zinkevych

Started with-apache-sparkHappiest Minds Technologies

Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!

5 reasons why spark is in demand!Edureka!

Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks

What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!

Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Databricks

AI at ScaleAdi Polak

How Microsoft Synapse Analytics Can Transform Your Data Analytics.pdfAddend Analytics

UNIT -IV.docxRevathiparamanathan

.Net development with Azure Machine Learning (AzureML) Nov 2014Mark Tabladillo

An Insider’s Guide to Maximizing Spark SQL PerformanceTakuya UESHIN

MongoDB and Azure DatabricksMongoDB

Synapse for mere mortalsMichael Stephenson

Spark1Dr. G. Bharadwaja Kumar

IBM Strategy for SparkMark Kerzner

Big Data Processing with Spark and Scala Edureka!

Scalable Machine Learning with PySparkLadle Patel

Spark for big data analyticsEdureka!

Eugene Polonichko "Azure Data Lake: what is it? why is it? where is it?"DataConf

Ai big dataconference_eugene_polonichko_azure data lake Olga Zinkevych

Started with-apache-sparkHappiest Minds Technologies

Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!

5 reasons why spark is in demand!Edureka!

Apache® Spark™ 1.6 presented by Databricks co-founder Patrick WendellDatabricks

What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!

More from Stitch Fix Algorithms (11)

Progression by Regression: How to increase your A/B Test VelocityStitch Fix Algorithms

Deep recommendations in PyTorchStitch Fix Algorithms

Tracking data lineage at Stitch FixStitch Fix Algorithms

Personalization allows Stitch Fix to style its clients and provide recommendations to help them find what they love. To do this, the company gathers information about a client’s preferences up front when they sign up from the service and learns more about them as they become longer-term customers. This information is important for making recommendations but also must be protected and managed with care. The data science team at Stitch Fix is the primary owner of the recommendation systems. Backing them up is the data platform team, who maintain the data infrastructure, data warehouse, and supporting tools and services. This data warehouse has several different data sources that read and write into it. This includes a logging pipeline for events, every Spark-based ETL, and daily snapshots of structured data from Stitch Fix applications. Neelesh Srinivas Salian explains Stitch Fix’s process to better understand the movement and evolution of data within its data warehouse, from the initial ingestion from outside sources through all of its ETLs. Neelesh also details how Stitch Fix built a service that helps the company understand the lineage information that is associated with each table in the data warehouse. This service helps the company understand the source, parentage, and journey of all data in the warehouse. Although Stitch Fix makes sure to anonymize and filter out sensitive information from this data, the company needs a more flexible long-term solution as the business expands.

Improving ad hoc and production workflows at Stitch FixStitch Fix Algorithms

Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. Neelesh Srinivas Salian offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way. Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that expedite the process of getting started with Spark and transitioning from an ad hoc to a production workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix. Neelesh shares Stitch Fix’s journey, exploring its ad hoc and production infrastructure and detailing its in-house tools and how they work in synergy with open source frameworks in a cloud environment. Neelesh also discusses the additional improvements to the infrastructure that help persist information for future use and optimization and explains how the implementation of Amazon’s EMR FS has helped make it easier to read from the S3 source.

A compute infrastructure for data scientistsStitch Fix Algorithms

Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. This talk offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way. Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that make it easier to get started with Spark and transition themselves to a daily workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix. In this talk, we look at Stitch Fix’s journey, exploring its Spark setup, in-house tools and how they work in synergy with open source frameworks in a cloud environment. There are additional improvements to the infrastructure that help persist information for future use and optimization and we look at how the implementation of Amazon’s EMR FS has helped make it easier for us to read from the S3 source.

Moment-based estimation for hierarchical models in Apache SparkStitch Fix Algorithms

At Stitch Fix, hierarchical models are one of the core machine learning frameworks used in our recommender systems technology. Hierarchical models allow for estimation on clustered data, when classical assumptions of identically distributed random variables break down. Traditional likelihood-based methods for fitting hierarchical models often struggle with the scale of data found in industry, which has prompted recent research into moment-based procedures for parameter estimation. Spark doesn't have a native library for fitting these models, and to our knowledge, no moment-based estimation software has been developed previously utilizing a distributed computational system. This talk will review our development of Spark software utilizing these new estimation methods, detail the theory behind the approach, and compare our software to similar open source packages in Spark and other popular languages.

Production model deploymentStitch Fix Algorithms

This document discusses the challenges of deploying machine learning models. It describes the model lifecycle from data to deployment and identifies three common modes of deployment. Key challenges discussed include ensuring models continue to accurately predict outcomes over time as data changes, meeting engineering requirements for throughput and latency, and establishing team structures that support continuous model development and deployment through open standards and capabilities. Solutions proposed focus on continuous training, materializing model outputs, and organizing multi-disciplinary teams around model capabilities rather than functions.

Optimizing SparkStitch Fix Algorithms

I'll provide guidelines for thinking about empirical performance evaluation of parallel programs in general and of Spark jobs in particular. It's easier to be systematic about this if you think in terms of "what's the effective network bandwidth we're getting?" instead of "How fast does this particular job run?" In addition, the figure of merit for parallel performance isn't necessarily obvious. If you want to minimize your AWS bill you should almost certainly run on a single node (but your job may take six months to finish). You may think you want answers as quickly as possible, but if you could make a job finish in 55 minutes instead 60 minutes while doubling your AWS bill, would you do it? No? Then what exactly is the metric that you should optimize?

When We Spark and When We Don’t: Developing Data and ML PipelinesStitch Fix Algorithms

The data platform at Stitch Fix runs thousands of jobs a day to feed data products that provide algorithmic capabilities to power nearly all aspects of the business, from merchandising to operations to styling recommendations. Many of these jobs are distributed across Spark clusters, while many others are scheduled as isolated single-node tasks in containers running Python, R, or Scala. Pipelines are often comprised of a mix of task types and containers. This talk will cover thoughts and guidelines on how we develop, schedule, and maintain these pipelines at Stitch Fix. We’ll discuss guidelines on how we think about which portions of the pipelines we develop to run on what platforms (e.g. what is important to run distributed across Spark clusters vs run in stand-alone containers) and how we get them to play well together. We’ll also provide an overview of tools and abstractions that have been developed at Stitch Fix to facilitate the process from development, to deployment, to monitoring them in production.

IncrementalityStitch Fix Algorithms

When marketing teams spend money on a paid acquisitions program it is crucial to understand the effect of that ad spend. In this talk, we will outline incrementality as a way to measure the causal impact that ad spend has on acquiring new customers and its advantages over more traditional metrics. We will walk through several ad measurement products available today and give examples of how to apply them to your business.

Enabling full stack data scientistsStitch Fix Algorithms

Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. Data Scientists are expected to build their systems end to end and maintain them in the long run. We rely on automation, documentation, and collaboration to enable data scientist to build and maintain production services. In this talk I will discuss what we have built and how we communicate about these tools with our data scientists.

Progression by Regression: How to increase your A/B Test VelocityStitch Fix Algorithms

Deep recommendations in PyTorchStitch Fix Algorithms

Tracking data lineage at Stitch FixStitch Fix Algorithms

Improving ad hoc and production workflows at Stitch FixStitch Fix Algorithms

A compute infrastructure for data scientistsStitch Fix Algorithms

Moment-based estimation for hierarchical models in Apache SparkStitch Fix Algorithms

Production model deploymentStitch Fix Algorithms

Optimizing SparkStitch Fix Algorithms

When We Spark and When We Don’t: Developing Data and ML PipelinesStitch Fix Algorithms

IncrementalityStitch Fix Algorithms

Enabling full stack data scientistsStitch Fix Algorithms

Recently uploaded (20)

Technology Trends in 2025: AI and Big Data AnalyticsInData Labs

At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including: -Artificial Intelligence Market Overview -Strategies for AI Adoption in 2025 -Anticipated drivers of AI adoption and transformative technologies -Benefits of AI and Big data for your business -Tips on how to prepare your business for innovation -AI and data privacy: Strategies for securing data privacy in AI models, etc. Download your free copy nowand implement the key findings to improve your business.

Splunk Security Update | Public Sector Summit Germany 2025Splunk

Build 3D Animated Safety Induction - Tech EHSTECH EHS Solution

Train Smarter, Not Harder – Let 3D Animation Lead the Way! Discover how 3D animation makes inductions more engaging, effective, and cost-efficient. Check out the slides to see how you can transform your safety training process! Slide 1: Why 3D animation changes the game Slide 2: Site-specific induction isn’t optional—it’s essential Slide 3: Visitors are most at risk. Keep them safe Slide 4: Videos beat text—especially when safety is on the line Slide 5: TechEHS makes safety engaging and consistent Slide 6: Better retention, lower costs, safer sites Slide 7: Ready to elevate your induction process? Can an animated video make a difference to your site's safety? Let's talk.

Rusty Waters: Elevating Lakehouses Beyond Sparkcarlyakerly1

Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark? At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍 Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀

TrsLabs - Fintech Product & Business ConsultingTrs Labs

Hybrid Growth Mandate Model with TrsLabs Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant. An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices. Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company Talk to us & Unlock the competitive advantage

TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc

Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency. This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data. Attendees will learn: - Consumer awareness around data brokers and what consumers are doing to limit data collection - How businesses assess third-party vendors and their consent management operations - Where business preparedness needs improvement - What these trends mean for the future of privacy governance and public trust This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.

AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix

Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025 https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/ Is AI just another technology, or does it fundamentally change the way we live and think? Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater. At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts. At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.

MINDCTI revenue release Quarter 1 2025 PRMIND CTI

tecnologias de las primeras civilizaciones.pdffjgm517

Top 10 IT Help Desk Outsourcing ServicesInfrassist Technologies Pvt. Ltd.

Procurement Insights Cost To Value Guide.pptxJon Hansen

The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john

Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB

Want to learn practical tips for designing systems that can scale efficiently without compromising speed? Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development. As you explore key principles of designing low-latency systems with Rust, you will learn how to: - Create and compile a real-world app with Rust - Connect the application to ScyllaDB (NoSQL data store) - Negotiate tradeoffs related to data modeling and querying - Manage and monitor the database for consistently low latencies

Cybersecurity Identity and Access Solutions using Azure ADVICTOR MAESTRE RAMIREZ

Web and Graphics Designing Training in RajpuraErginous Technology

Web & Graphics Designing Training at Erginous Technologies in Rajpura offers practical, hands-on learning for students, graduates, and professionals aiming for a creative career. The 6-week and 6-month industrial training programs blend creativity with technical skills to prepare you for real-world opportunities in design. The course covers Graphic Designing tools like Photoshop, Illustrator, and CorelDRAW, along with logo, banner, and branding design. In Web Designing, you’ll learn HTML5, CSS3, JavaScript basics, responsive design, Bootstrap, Figma, and Adobe XD. Erginous emphasizes 100% practical training, live projects, portfolio building, expert guidance, certification, and placement support. Graduates can explore roles like Web Designer, Graphic Designer, UI/UX Designer, or Freelancer. For more info, visit erginous.co.in , message us on Instagram at erginoustechnologies, or call directly at +91-89684-38190 . Start your journey toward a creative and successful design career today!

Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...BookNet Canada

Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next. Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/ Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.

Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55

Manifest Pre-Seed Update | A Humanoid OEM Deeptech In Francechb3

HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungenpanagenda

Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-und-verwaltung-von-multiuser-umgebungen/ HCL Nomad Web wird als die nächste Generation des HCL Notes-Clients gefeiert und bietet zahlreiche Vorteile, wie die Beseitigung des Bedarfs an Paketierung, Verteilung und Installation. Nomad Web-Client-Updates werden “automatisch” im Hintergrund installiert, was den administrativen Aufwand im Vergleich zu traditionellen HCL Notes-Clients erheblich reduziert. Allerdings stellt die Fehlerbehebung in Nomad Web im Vergleich zum Notes-Client einzigartige Herausforderungen dar. Begleiten Sie Christoph und Marc, während sie demonstrieren, wie der Fehlerbehebungsprozess in HCL Nomad Web vereinfacht werden kann, um eine reibungslose und effiziente Benutzererfahrung zu gewährleisten. In diesem Webinar werden wir effektive Strategien zur Diagnose und Lösung häufiger Probleme in HCL Nomad Web untersuchen, einschließlich - Zugriff auf die Konsole - Auffinden und Interpretieren von Protokolldateien - Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS) - Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien - Nutzung der Client Clocking-Funktion

Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan

This is a Quick Research Guide (QRG). QRGs include the following: - A brief, high-level overview of the QRG topic. - A milestone timeline for the QRG topic. - Links to various free online resource materials to provide a deeper dive into the QRG topic. - Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic. QRGs planned for the series: - Artificial Intelligence QRG - Quantum Computing QRG - Big Data Analytics QRG - Spacecraft Guidance, Navigation & Control QRG (coming 2026) - UK Home Computing & The Birth of ARM QRG (coming 2027) Any questions or comments? - Please contact Arthur Morgan at [email protected]. 100% human made.