Runs contain MLflow models and are linked to a notebook revision. Experiments contain zero or more runs. Registered models contain versions which point to a run's MLflow model. Relationships include experiments having runs, runs belonging to experiments and containing models, and registered model versions linking to runs and models.
Doing data science at scale? PySpark and MLlib bring the power of Spark's distributed processing to python users so that you can train machine learning models on massive datasets. MLlib provides tools for data extraction, transformation and loading, common ML algorithms, and model evaluation. And with the addition of MLFlow, it's easier than ever to log, reproduce and deploy your ML models. This walkthrough is aimed at those new to MLflow, and will take you through the ML lifecycle with PySpark ML toolset.
What MLflow is; what problem it solves for machine learning lifecycle; and how it solves; How it will be used with Databricks; and CI/CD pipeline with Databricks.
In Data Engineer's Lunch #54, we will discuss the data build tool, a tool for managing data transformations with config files rather than code. We will be connecting it to Apache Spark and using it to perform transformations.
Accompanying YouTube: https://youtu.be/dwZlYG6RCSY
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Hands-on tutorial on Ballerina language concepts, features and type system. This was conducted at SummerSOC conference on June 22, 2019 - https://www.summersoc.eu/program/
MLflow: Infrastructure for a Complete Machine Learning Life CycleDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
Doing data science at scale? PySpark and MLlib bring the power of Spark's distributed processing to python users so that you can train machine learning models on massive datasets. MLlib provides tools for data extraction, transformation and loading, common ML algorithms, and model evaluation. And with the addition of MLFlow, it's easier than ever to log, reproduce and deploy your ML models. This walkthrough is aimed at those new to MLflow, and will take you through the ML lifecycle with PySpark ML toolset.
What MLflow is; what problem it solves for machine learning lifecycle; and how it solves; How it will be used with Databricks; and CI/CD pipeline with Databricks.
In Data Engineer's Lunch #54, we will discuss the data build tool, a tool for managing data transformations with config files rather than code. We will be connecting it to Apache Spark and using it to perform transformations.
Accompanying YouTube: https://youtu.be/dwZlYG6RCSY
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Hands-on tutorial on Ballerina language concepts, features and type system. This was conducted at SummerSOC conference on June 22, 2019 - https://www.summersoc.eu/program/
MLflow: Infrastructure for a Complete Machine Learning Life CycleDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this talk, I present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
Apache Spark Toronto Meetup, July 27, 2016.
Wattpad talks about their experiences with Apache Spark. From starting in 2014 with Shark, to building distributed recommendation algorithms using ANN, to improving search results using a sessionized query log. We also talk about some of the issues we faced building our analytics pipeline, including getting spark to work with Luigi, an open source project by Spotify.
Managing the Complete Machine Learning Lifecycle with MLflowDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
Design and Implementation of the Security Graph LanguageAsankhaya Sharma
Today software is built in fundamentally different
ways from how it was a decade ago. It is increasingly common
for applications to be assembled out of open-source components,
resulting in the use of large amounts of third-party code. This
third-party code is a means for vulnerabilities to make their
way downstream into applications. Recent vulnerabilities such
as Heartbleed, FREAK SSL/TLS, GHOST, and the Equifax data
breach (due to a flaw in Apache Struts) were ultimately caused
by third-party components. We argue that an automated way to
audit the open-source ecosystem, catalog existing vulnerabilities,
and discover new flaws is essential to using open-source safely.
To this end, we describe the Security Graph Language (SGL), a
domain-specific language for analysing graph-structured datasets
of open-source code and cataloguing vulnerabilities. SGL allows
users to express complex queries on relations between libraries
and vulnerabilities in the style of a program analysis language.
SGL queries double as an executable representation for vulnerabilities, allowing vulnerabilities to be automatically checked
against a database and deduplicated using a canonical representation. We outline a novel optimisation for SGL queries based on
regular path query containment, improving query performance up to 3 orders of magnitude. We also demonstrate the
effectiveness of SGL in practice to find zero-day vulnerabilities
by identifying sever
Enterprise PHP Architecture through Design Patterns and Modularization (Midwe...Aaron Saray
Object Oriented Programming in enterprise level PHP is incredibly important. In this presentation, concepts like MVC architecture, data mappers, services, and domain and data models will be discussed. Simple demonstrations will be used to show patterns and best practices. In addition, using tools like Doctrine or integration with Salesforce or the AS/400 will also be discussed. There will be an emphasis on the practical application of these techniques as well - this isn't just a theoretical talk! This presentation is great for those just beginning to create enterprise applications as well as those who have had years of experience.
In this talk, I present an introduction of MLFlow. I also show some examples of using it by means of MLFlow Tracking, MLFlow Projects and MLFlow Models. I also used Databricks as an example of remote tracking.
MLflow 1.0 is coming soon as the first stable release of MLflow. It also packs many cleanups and improvements, such as simpler metadata management, search APIs and HDFS support. In this talk, we’ll present these new features in detail, and then discuss additional MLflow components that Databricks and other companies are working on for the rest of 2019. These new tools include a model registry to share and track models, as well as a multi-step workflow abstraction, both of which were announced at Spark + AI Summit 2019.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Have you already tried to work on the same model with your team members? Then, you probably faces the problem of accessing a coveted resource! And you rapidly came to the conclusion that one single file for a model is not scalable on active teams.
In this talk we will explain the two main solutions that you can adopt to collaborate on a model with Sirius: splitting your model into several resources or store your model into a shared repository.
Slides from the talk of the same name presented at SiriusCon 2015 (http://www.siriuscon.org/).
A design pattern is a general repeatable solution to a commonly occurring problem in software design. They make your code scalable, robust and easy for developers to learn. The three categories - Creational, Structural and Behavioral. A major look through of the Scala specific design patterns such as the Lens pattern, duck typing, memoization, etc. These patterns let you explore the features of Scala and use a design pattern using those features which can help you solve your different use cases.
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this talk, I present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
Apache Spark Toronto Meetup, July 27, 2016.
Wattpad talks about their experiences with Apache Spark. From starting in 2014 with Shark, to building distributed recommendation algorithms using ANN, to improving search results using a sessionized query log. We also talk about some of the issues we faced building our analytics pipeline, including getting spark to work with Luigi, an open source project by Spotify.
Managing the Complete Machine Learning Lifecycle with MLflowDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
Design and Implementation of the Security Graph LanguageAsankhaya Sharma
Today software is built in fundamentally different
ways from how it was a decade ago. It is increasingly common
for applications to be assembled out of open-source components,
resulting in the use of large amounts of third-party code. This
third-party code is a means for vulnerabilities to make their
way downstream into applications. Recent vulnerabilities such
as Heartbleed, FREAK SSL/TLS, GHOST, and the Equifax data
breach (due to a flaw in Apache Struts) were ultimately caused
by third-party components. We argue that an automated way to
audit the open-source ecosystem, catalog existing vulnerabilities,
and discover new flaws is essential to using open-source safely.
To this end, we describe the Security Graph Language (SGL), a
domain-specific language for analysing graph-structured datasets
of open-source code and cataloguing vulnerabilities. SGL allows
users to express complex queries on relations between libraries
and vulnerabilities in the style of a program analysis language.
SGL queries double as an executable representation for vulnerabilities, allowing vulnerabilities to be automatically checked
against a database and deduplicated using a canonical representation. We outline a novel optimisation for SGL queries based on
regular path query containment, improving query performance up to 3 orders of magnitude. We also demonstrate the
effectiveness of SGL in practice to find zero-day vulnerabilities
by identifying sever
Enterprise PHP Architecture through Design Patterns and Modularization (Midwe...Aaron Saray
Object Oriented Programming in enterprise level PHP is incredibly important. In this presentation, concepts like MVC architecture, data mappers, services, and domain and data models will be discussed. Simple demonstrations will be used to show patterns and best practices. In addition, using tools like Doctrine or integration with Salesforce or the AS/400 will also be discussed. There will be an emphasis on the practical application of these techniques as well - this isn't just a theoretical talk! This presentation is great for those just beginning to create enterprise applications as well as those who have had years of experience.
In this talk, I present an introduction of MLFlow. I also show some examples of using it by means of MLFlow Tracking, MLFlow Projects and MLFlow Models. I also used Databricks as an example of remote tracking.
MLflow 1.0 is coming soon as the first stable release of MLflow. It also packs many cleanups and improvements, such as simpler metadata management, search APIs and HDFS support. In this talk, we’ll present these new features in detail, and then discuss additional MLflow components that Databricks and other companies are working on for the rest of 2019. These new tools include a model registry to share and track models, as well as a multi-step workflow abstraction, both of which were announced at Spark + AI Summit 2019.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Have you already tried to work on the same model with your team members? Then, you probably faces the problem of accessing a coveted resource! And you rapidly came to the conclusion that one single file for a model is not scalable on active teams.
In this talk we will explain the two main solutions that you can adopt to collaborate on a model with Sirius: splitting your model into several resources or store your model into a shared repository.
Slides from the talk of the same name presented at SiriusCon 2015 (http://www.siriuscon.org/).
A design pattern is a general repeatable solution to a commonly occurring problem in software design. They make your code scalable, robust and easy for developers to learn. The three categories - Creational, Structural and Behavioral. A major look through of the Scala specific design patterns such as the Lens pattern, duck typing, memoization, etc. These patterns let you explore the features of Scala and use a design pattern using those features which can help you solve your different use cases.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
2. ● Databricks MLflow objects (runs, experiments, registered models and their
versions, notebooks) form a complex web of relationships.
● Objects live in different places: workspace objects, DBFS (cloud) and MySQL.
○ A run’s metadata lives in MySQL, its artifacts in cloud and its notebook in the workspace and/or git.
● Experiments have zero or more runs.
● Registered models have 0 or more versions that point to a run’s MLflow model.
● Code that generated a run’s MLflow model:
○ MLflow runs have pointers to a notebook revision that generated the model.
○ Runs will/should have pointers to the git version of a notebook that generated the model.
Overview
3. ● Model is an overloaded term with three meanings:
○ Native model artifact - this is the lowest level and is simply the native flavor’s serialized format. For
sklearn it’s a pickle file, for Keras it’s a directory with TensorFlow’s native SaveModel format files.
○ MLflow model - a wrapper around the native model artifact with metadata in the MLmodel file and
environment information in conda.yaml and requirements.txt files.
○ Registered model - a bucket of model versions. A model version contains one MLflow model that is
cached in the model repository. A version has the following links (expressed as tags):
■ run_id - points to the run that generated the version’s model.
■ source - points to the path of MLflow model in the run that corresponds to the version’s model.
■ workspace_uri - currently missing. Needed if using shared model registry. ML-19472.
Model terminology
5. ● Runs
○ Contains one or more MLflow models
● Experiments
○ Notebook experiments
○ Workspace experiments
● Registered models
○ A registered model contains versions
○ A version points to one run’s MLflow model
○ Native model artifacts - the actual bits that execute predictions that are part of the MLflow model
● Notebooks
Databricks MLflow object relationships
7. ● Diagram uses the UML modeling language.
○ *: indicates a many relationship
○ 1: indicates a required one relationship.
○ 0..1: indicates an optional one relationship.
● This is a logical diagram. Not all nuances are captured for simplification.
● The diagram represents a notebook experiment.
● A workspace experiment is not represented in the diagram.
Diagram legend
8. ● A registered model is a bucket for model versions.
● A version has one MLflow model which is linked to the run that generated it.
● The production and staging stage have one "latest" version.
● Registered model versions are cached in the model registry.
● This is a clone of the run's MLflow model that the version points to.
● If source run is in a different workspace we have a lineage reachability problem.
See ML-19472 - Add workspace URI field in ModelVersion for a registered
model to make run reachable.
Registered models
9. ● An experiment has zero or more runs.
● Two types of experiments:
○ Notebook experiment
■ Relationship of experiment to notebook is one-to-one.
■ Workspace path of the experiment is the same as its notebook.
○ Workspace experiment
■ Relationship of experiment to notebook is one-to-many.
■ Explicitly specify the experiment path with set_experiment method.
■ Different notebooks can create runs in the same experiment.
Experiments
10. ● A run belongs to only one experiment.
● A run is linked to one notebook revision. MLflow notebook tags:
○ mlflow.databricks.notebookRevisionID
○ mlflow.databricks.notebookID
○ mlflow.databricks.notebookPath
● Optionally a run’s notebook can be linked to a git reference.
○ See discussion on Notebook below for details.
● A run can have one or more MLflow models (flavors) such as Sklearn and ONNX.
● Every run has a default Pyfunc flavor which is wrapper around the native model.
Runs
12. ● An MLflow Run has three basic components
○ Metadata (params, metrics, tags) residing in a MySQL database.
○ MLflow model artifact which lives in DBFS (cloud). Note you can also have arbitrary customer
artifacts.
○ Link to code:
■ For Databricks, the run points to either:
● Workspace notebook revision
● Repos notebook a pointer to git.
■ For open source the link points to git.
MLflow Run Details Legend
13. ● A notebook has many revisions.
● Optionally, a notebook revision can be checked into git with Databricks Repos.
● Need to capture git reference analogous to the MLflow open source tags:
○ mlflow.source.git.commit
○ mlflow.source.git.repoURL
○ mlflow.gitRepoURL
● See ML-19473 - Add git reference tags to Databricks run if its notebook is synced with
Repos
● Two sources of truth for a notebook snapshot that can be confusing:
○ Databricks notebook revision
○ Git version
Notebooks
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?
What did they do with us?
what are they trying to do? recommendation? content curation?
how does that work?
How come Delta and Spark and those things can help with that thing (recommendation, or whatever they do)?