These slides correspond to a recording of a live webcast of a demo of Metric Management functionality in SigOpt, keeping model size down while increasing validation accuracy for a road sign image classification problem.
Selling commercial Solar +/or Energy Storage solutions? You need GridMAP! Iain Beveridge
GridMAP is a powerful tool to design, optimise and evaluate new energy generation & storage assets. GridMAP saves time, maximises profitability, and increases project success rates. Find out more in the attached [short] presentation.
How SAP For Construction Works within the Fourfold Of Company?AvinashMittal5
Digitalize the sap for construction deliver chain to foster call for-pushed, collaborative supply networks. Growth agility and effectiveness to guide the commercial enterprise priority of serving the “segment of one” and enforce a virtual deliver chain. https://illumiti.com/industries/construction/
Marios Michailidis & Mathias Muller, H2O.ai - Time Series with H2O Driverless...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/0pvvDHfxdZ8
Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy.
Driverless AI is now equipped with time-series functionality. Time series helps forecast sales, predict industrial machine failure and more. With the time series capability in Driverless AI, H2O.ai directly addresses some of the most pressing concerns of organizations across industries for use cases such as transactional data in capital markets, in retail to track in-store and online sales, and in manufacturing with sensor data to improve supply chain or predictive maintenance.
Bio: Marios Michailidis is a Competitive Data Scientist at H2O.ai. He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a PhD in machine learning at from UCL . He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: acquisition, retention, recommenders, fraud detection, portfolio optimization and more. He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. He currently ranks 3rd.
Bio: A Kaggle Grandmaster and a Data Scientist at H2O.ai, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to H2O.ai, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with H2O.ai.
Discover Minitab Workspace - The Ultimate Visual Toolkit to Elevate Your Work...Minitab, LLC
Searching for simple solutions to achieve the greatest impact with your work? Minitab Workspace enables you to move work forward with powerful visual tools, process maps, brainstorming diagrams and forms, all in one intuitive interface right at your fingertips. Our tools help form processes and identify opportunities ultimately making problems easier to solve.
Boost Your Data Expertise with the Latest Release of Minitab Statistical Soft...Minitab, LLC
The ability to derive and communicate meaningful information from your data is a critical skill, particularly now when we all must work smarter and more efficiently. You have more data available to you and your organisation than ever before, but are you tapping into it effectively using the best visualisations and analytics?
Download your free trial of Minitab Statistical Software today: https://hubs.ly/H0qsnbs0
Customer segmentation for business success with knimeKnoldus Inc.
Customer segmentation has undoubtedly been one of the most implemented applications in data analytics since the birth of customer intelligence and CRM. Data scientists and modern business analysts work closely together to achieve and automize a comprehensive description of the company’s group of customers.
However, they usually came across these two challenges:
~ Need to implement a customer segmentation frame that can accommodate a self-adjusting procedure.
~ Need an interactive way to inject their knowledge into the customer segmentation frame without ever opening the underlying data processing workflow.
Learn how to generate different customer groups using clustering and how to provide insights into the performance of sales activities.
Tips & Tricks for CART (Classification and Regression Trees) in Minitab Stati...Minitab, LLC
This presentation is part of an on-demand webinar you can access here:https://hubs.ly/H0BVzst0
A while ago we encouraged you to up your Minitab Statistical Software game with our Tips & Tricks for Minitab webinar (https://info.minitab.com/resources/webinars/tips-tricks-minitab-statistical-software), and we received some great feedback!
Now, Minitab Solutions Architect Marilyn Wheatley is back to draw on over a decade of experience and share more tips and tricks with you, this time specifically focusing on Minitab’s newest predictive analytics tools – Classification and Regression Trees (CART).
Join us to learn
Tricks for manipulating your view of CART models
Tips for exploring CART results
Penalties for specific data conditions
Best practices for working with your results
Selling commercial Solar +/or Energy Storage solutions? You need GridMAP! Iain Beveridge
GridMAP is a powerful tool to design, optimise and evaluate new energy generation & storage assets. GridMAP saves time, maximises profitability, and increases project success rates. Find out more in the attached [short] presentation.
How SAP For Construction Works within the Fourfold Of Company?AvinashMittal5
Digitalize the sap for construction deliver chain to foster call for-pushed, collaborative supply networks. Growth agility and effectiveness to guide the commercial enterprise priority of serving the “segment of one” and enforce a virtual deliver chain. https://illumiti.com/industries/construction/
Marios Michailidis & Mathias Muller, H2O.ai - Time Series with H2O Driverless...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/0pvvDHfxdZ8
Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy.
Driverless AI is now equipped with time-series functionality. Time series helps forecast sales, predict industrial machine failure and more. With the time series capability in Driverless AI, H2O.ai directly addresses some of the most pressing concerns of organizations across industries for use cases such as transactional data in capital markets, in retail to track in-store and online sales, and in manufacturing with sensor data to improve supply chain or predictive maintenance.
Bio: Marios Michailidis is a Competitive Data Scientist at H2O.ai. He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a PhD in machine learning at from UCL . He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: acquisition, retention, recommenders, fraud detection, portfolio optimization and more. He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. He currently ranks 3rd.
Bio: A Kaggle Grandmaster and a Data Scientist at H2O.ai, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to H2O.ai, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with H2O.ai.
Discover Minitab Workspace - The Ultimate Visual Toolkit to Elevate Your Work...Minitab, LLC
Searching for simple solutions to achieve the greatest impact with your work? Minitab Workspace enables you to move work forward with powerful visual tools, process maps, brainstorming diagrams and forms, all in one intuitive interface right at your fingertips. Our tools help form processes and identify opportunities ultimately making problems easier to solve.
Boost Your Data Expertise with the Latest Release of Minitab Statistical Soft...Minitab, LLC
The ability to derive and communicate meaningful information from your data is a critical skill, particularly now when we all must work smarter and more efficiently. You have more data available to you and your organisation than ever before, but are you tapping into it effectively using the best visualisations and analytics?
Download your free trial of Minitab Statistical Software today: https://hubs.ly/H0qsnbs0
Customer segmentation for business success with knimeKnoldus Inc.
Customer segmentation has undoubtedly been one of the most implemented applications in data analytics since the birth of customer intelligence and CRM. Data scientists and modern business analysts work closely together to achieve and automize a comprehensive description of the company’s group of customers.
However, they usually came across these two challenges:
~ Need to implement a customer segmentation frame that can accommodate a self-adjusting procedure.
~ Need an interactive way to inject their knowledge into the customer segmentation frame without ever opening the underlying data processing workflow.
Learn how to generate different customer groups using clustering and how to provide insights into the performance of sales activities.
Tips & Tricks for CART (Classification and Regression Trees) in Minitab Stati...Minitab, LLC
This presentation is part of an on-demand webinar you can access here:https://hubs.ly/H0BVzst0
A while ago we encouraged you to up your Minitab Statistical Software game with our Tips & Tricks for Minitab webinar (https://info.minitab.com/resources/webinars/tips-tricks-minitab-statistical-software), and we received some great feedback!
Now, Minitab Solutions Architect Marilyn Wheatley is back to draw on over a decade of experience and share more tips and tricks with you, this time specifically focusing on Minitab’s newest predictive analytics tools – Classification and Regression Trees (CART).
Join us to learn
Tricks for manipulating your view of CART models
Tips for exploring CART results
Penalties for specific data conditions
Best practices for working with your results
SigOpt founder and CEO, Scott Clark, PhD, explains the tradeoffs you'll want to consider when designing your modeling platform and integrating hyperparameter optimization to enhance data scientist productivity.
This talk discusses the intuition behind Bayesian optimization with and without multiple metrics. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
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.
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
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.
Advanced Optimization for the Enterprise WebinarSigOpt
Building on the TWIML eBook, TWIMLcon event and TWIML podcast series that explore Machine Learning Platforms in great detail, this webinar examines the machine learning platforms that power enterprise leaders in AI. SigOpt CEO Scott Clark will provide an overview of critical technical capabilities that our customers have prioritized in their ML platforms.
Review these slides to learn about:
- Critical capabilities for data, experiment and model management
- Tradeoffs between building and buying these capabilities
- Lessons from the implementation of these platforms by AI leaders
Why focus on these platforms and the capabilities that power them? Nearly every company is investing in machine learning that differentiates products or generates revenue. These so-called "differentiated models" represent the biggest opportunity for AI to transform the business. Most of these teams find success hiring expert data scientists and machine learning engineers who can build these models. But most of these teams also struggle to create a more sustainable, scalable and reproducible process for model development, and have begun building ML platforms to tackle this challenge.
In this deck I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.
This presentation discusses how prescriptive analytics can help energy traders, originators, portfolio managers and risk managers make faster, accurate decisions in the most complex scenarios
Intro of Key Features of Auto eCAAT Ent Softwarerafeq
This presentation provides a brief overview of Auto eCAAT Ent with use cases. Auto eCAAT Ent is a Data Analytics/BI software specially designed for automating analytics in the assignments of Assurance, Compliance and Fraud Investigations.
SigOpt at O'Reilly - Best Practices for Scaling Modeling PlatformsSigOpt
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry. Join in to learn how Two Sigma, a leading quantitative investment and technology firm, solved its model optimization problem.
Anomaly Detection launch & update
* Recap: What is anomaly detection?
* Recap: Why ML & AI for anomaly detection?
* Why VictoriaMetrics Anomaly Detection?
* What’s new: Flexible Configs
* What’s new: AutoTune
* What’s new: Docs & site updates
● Quickstart - minimalistic guide on how to set up and run `vmanomaly` (Docker, Kubernetes)
● Model types - explanations and diagrams to understand specifics of a lifecycle and find the best model for your use case
● AutoTuned model introduction - find out how to set-and-forget the model of your choice to learn from your data
● VictoriaMetrics Anomaly Detection got its own feature page
* Roadmap for 2024
● Streaming models support
● GUI: Deeper integration with anomaly detection service
● Node_exporter preset. Presets for common tasks, like “seasonal_weekly”, “testing”, “autotuned_daily”
● (Q3-Q4) Root Cause Analysis: Drill down your incidents faster and more efficient. Finishing transition from PoC to production.
Tuning the Untunable - Insights on Deep Learning OptimizationSigOpt
Patrick Hayes originally gave this talk at ODSC West in 2018. During this talk, Patrick discusses a couple key barriers to deep learning optimization and how SigOpt solves them. First, Patrick discusses the problem of lengthy training cycles and how novel techniques like multitask optimization are designed to use partial information to solve this challenge. Second, Patrick discusses automated cluster management and how solving this problem makes it much easier to manage training cycles for these models.
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Optimizing BERT and Natural Language Models with SigOpt Experiment ManagementSigOpt
SigOpt Machine Learning Engineer Meghana Ravikumar explains how she reduced the size of a BERT natural language model trained on the SQUAD 2.0 question-answer database, to reduce its size while maintaining performance using a "distillation" process optimized with SigOpt's Experiment Management functionality.
SigOpt's Fay Kallel, Head of Product, and Jim Blomo, Head of Engineering, describe the latest updates to SigOpt, a suite of features that help you manage your modeling process.
More Related Content
Similar to Metric Management: a SigOpt Applied Use Case
SigOpt founder and CEO, Scott Clark, PhD, explains the tradeoffs you'll want to consider when designing your modeling platform and integrating hyperparameter optimization to enhance data scientist productivity.
This talk discusses the intuition behind Bayesian optimization with and without multiple metrics. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
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.
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
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.
Advanced Optimization for the Enterprise WebinarSigOpt
Building on the TWIML eBook, TWIMLcon event and TWIML podcast series that explore Machine Learning Platforms in great detail, this webinar examines the machine learning platforms that power enterprise leaders in AI. SigOpt CEO Scott Clark will provide an overview of critical technical capabilities that our customers have prioritized in their ML platforms.
Review these slides to learn about:
- Critical capabilities for data, experiment and model management
- Tradeoffs between building and buying these capabilities
- Lessons from the implementation of these platforms by AI leaders
Why focus on these platforms and the capabilities that power them? Nearly every company is investing in machine learning that differentiates products or generates revenue. These so-called "differentiated models" represent the biggest opportunity for AI to transform the business. Most of these teams find success hiring expert data scientists and machine learning engineers who can build these models. But most of these teams also struggle to create a more sustainable, scalable and reproducible process for model development, and have begun building ML platforms to tackle this challenge.
In this deck I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.
This presentation discusses how prescriptive analytics can help energy traders, originators, portfolio managers and risk managers make faster, accurate decisions in the most complex scenarios
Intro of Key Features of Auto eCAAT Ent Softwarerafeq
This presentation provides a brief overview of Auto eCAAT Ent with use cases. Auto eCAAT Ent is a Data Analytics/BI software specially designed for automating analytics in the assignments of Assurance, Compliance and Fraud Investigations.
SigOpt at O'Reilly - Best Practices for Scaling Modeling PlatformsSigOpt
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry. Join in to learn how Two Sigma, a leading quantitative investment and technology firm, solved its model optimization problem.
Anomaly Detection launch & update
* Recap: What is anomaly detection?
* Recap: Why ML & AI for anomaly detection?
* Why VictoriaMetrics Anomaly Detection?
* What’s new: Flexible Configs
* What’s new: AutoTune
* What’s new: Docs & site updates
● Quickstart - minimalistic guide on how to set up and run `vmanomaly` (Docker, Kubernetes)
● Model types - explanations and diagrams to understand specifics of a lifecycle and find the best model for your use case
● AutoTuned model introduction - find out how to set-and-forget the model of your choice to learn from your data
● VictoriaMetrics Anomaly Detection got its own feature page
* Roadmap for 2024
● Streaming models support
● GUI: Deeper integration with anomaly detection service
● Node_exporter preset. Presets for common tasks, like “seasonal_weekly”, “testing”, “autotuned_daily”
● (Q3-Q4) Root Cause Analysis: Drill down your incidents faster and more efficient. Finishing transition from PoC to production.
Tuning the Untunable - Insights on Deep Learning OptimizationSigOpt
Patrick Hayes originally gave this talk at ODSC West in 2018. During this talk, Patrick discusses a couple key barriers to deep learning optimization and how SigOpt solves them. First, Patrick discusses the problem of lengthy training cycles and how novel techniques like multitask optimization are designed to use partial information to solve this challenge. Second, Patrick discusses automated cluster management and how solving this problem makes it much easier to manage training cycles for these models.
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
Optimizing BERT and Natural Language Models with SigOpt Experiment ManagementSigOpt
SigOpt Machine Learning Engineer Meghana Ravikumar explains how she reduced the size of a BERT natural language model trained on the SQUAD 2.0 question-answer database, to reduce its size while maintaining performance using a "distillation" process optimized with SigOpt's Experiment Management functionality.
SigOpt's Fay Kallel, Head of Product, and Jim Blomo, Head of Engineering, describe the latest updates to SigOpt, a suite of features that help you manage your modeling process.
Efficient NLP by Distilling BERT and Multimetric OptimizationSigOpt
SigOpt ML Engineer Meghana Ravikumar explains how to use multimetric optimization to achieve a more efficient, compact BERT model to perform on a question-answering task.
SigOpt Research Engineer Michael McCourt and DarwinAI CTO Alexander Wong explain how they used SigOpt and hyperparameter optimization to successfully improve accuracy of detecting COVID-19 cases from chest X-Rays, using the COVID-Net model and the COVIDx open dataset.
Tuning Data Augmentation to Boost Model PerformanceSigOpt
In this webinar, SigOpt ML Engineer Meghana Ravikumar presents on and builds an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning—fine tuning and feature extraction—and the impact of Multitask optimization, a more efficient form of Bayesian optimization, on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will use image augmentation to double the size of the dataset to boost the classifier’s performance. Instead of manually tuning the hyperparameters associated with image augmentation, we will use Multitask Optimization to learn these hyperparameters using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also explore the features of these augmented images and the downstream implications for our image classifier.
SigOpt helps your algorithmic traders and data scientists build better models faster. Learn how to integrate SigOpt into your modeling platform for quick ROI for your data science team.
Interactive Tradeoffs Between Competing Offline Metrics with Bayesian Optimiz...SigOpt
Many real world applications - machine learning models, simulators, etc. - have multiple competing metrics that define performance; these require practitioners to carefully consider potential tradeoffs. However, assessing and ranking this tradeoff is nontrivial, especially when the number of metrics is more than two. Often times, practitioners scalarize the metrics into a single objective, e.g., using a weighted sum.
In this talk, we pose this problem as a constrained multi-objective optimization problem. By setting and updating the constraints, we can efficiently explore only the region of the Pareto efficient frontier of the model/system of most interest. We motivate this problem with the application of an experimental design setting, where we are trying to fabricate high performance glass substrate for solar cell panels.
As data science workloads grow, so does their need for infrastructure. But, is it fair to ask data scientists to also become infrastructure experts? If not the data scientists, then, who is responsible for spinning up and managing data science infrastructure? This talk will address the context in which ML infrastructure is emerging, walk through two examples of ML infrastructure tools for launching hyperparameter optimization jobs, and end with some thoughts for building better tools in the future.
Originally given as a talk at the PyData Ann Arbor meetup (https://www.meetup.com/PyData-Ann-Arbor/events/260380989/)
SigOpt at Uber Science Symposium - Exploring the spectrum of black-box optimi...SigOpt
At the inaugural Uber science symposium, SigOpt research engineer Bolong (Harvey) Cheng shares insights on black-box optimization from his experience working with both leading academics and innovative enterprises.
Training and tuning models with lengthy training cycles like those in deep learning can be extremely expensive and may sometimes involve techniques that degrade performance. We'll explore recent research on optimization strategies to efficiently tune these types of deep learning models. We will provide benchmarks and comparisons to other popular methods for optimizing the models, and we'll recommend valuable areas for further applied research.
SigOpt at GTC - Reducing operational barriers to optimizationSigOpt
Advanced hardware like NVIDIA technology lowers technical barriers to model size and scope, but issues remain in areas like model performance and training infrastructure management. We'll discuss operational challenges to training models at scale with a particular focus on how training management and hyperparameter tuning can inform each other to accomplish specific goals. We'll also explore techniques like parallelism and scheduling, discuss their impact on model optimization, and compare various techniques. We'll also evaluate results of this approach. In particular, we'll focus on how new tools that automate training orchestration accelerate model development and increase the volume and quality of models in production.
SigOpt CEO Scott Clark provides insights for modeling at scale in systematic trading. SigOpt works with algorithmic trading firms that collectively represent $300 billion in assets under management (AUM). In this presentation, Scott draws on this experience to provide a few critical insights to how these companies effectively model at scale. Alongside these insights, Scott shares a more specific case study from working with Two Sigma, a leading systematic investment manager.
SigOpt at MLconf - Reducing Operational Barriers to Model TrainingSigOpt
In this talk at MLconf NYC, Alexandra Johnson, platform engineering lead at SigOpt, discusses common operational challenges with scaling model training and how solutions are designed to
Machine learning infrastructure solve data scientists' problems using infrastructure tools. This talk shows the case study of building SigOpt Orchestrate, an ML infrastructure tool. The talk highlights how data scientists' concerns as user mapped to solutions with some of today's most popular infrastructure tools.
To learn more about SigOpt Orchestrate: https://sigopt.com/orchestrate
Originally given as a talk for UC Berkeley's Women in Electrical Engineering and Computer Science group on January 24, 2019.
AWS makes it easy to build, train, tune, and deploy Machine Learning (ML) models. If you're excited to get started with ML on AWS but want a refresher on the ML concepts behind build, train, tune, and deploy, this Dev Chat is for you.
Originally delivered as a Dev Chat at AWS Summit SF by Software Engineer Alexandra Johnson
Tips and techniques for hyperparameter optimizationSigOpt
All machine learning and artificial intelligence pipelines - from reinforcement agents to deep neural nets - have tunable hyperparameters. Optimizing these hyperparameters can take a model from scrappy prototype to production-ready system. This presentation shows techniques for performing hyperparameter optimization from an engineer who builds advanced and widely used optimization tools.
MLconf 2017 Seattle Lunch Talk - Using Optimal Learning to tune Deep Learning...SigOpt
In this talk we introduce Bayesian Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. Deep learning pipelines are notoriously expensive to train and often have many tunable parameters including hyperparameters, the architecture, feature transformations that can have a large impact on the efficacy of the model.
We will motivate the problem by giving several example applications using multiple open source deep learning frameworks and open datasets. We’ll compare the results of Bayesian Optimization to standard techniques like grid search, random search, and expert tuning.
Using Optimal Learning to Tune Deep Learning PipelinesSigOpt
SigOpt talk from NVIDIA GTC 2017 and AWS SF AI Day
We'll introduce Bayesian optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time consuming or expensive. Deep learning pipelines are notoriously expensive to train and often have many tunable parameters, including hyperparameters, the architecture, and feature transformations, that can have a large impact on the efficacy of the model. We'll provide several example applications using multiple open source deep learning frameworks and open datasets. We'll compare the results of Bayesian optimization to standard techniques like grid search, random search, and expert tuning. Additionally, we'll present a robust benchmark suite for comparing these methods in general.
Common Problems in Hyperparameter OptimizationSigOpt
Originally given at MLConf NYC 2017.
All large machine learning pipelines have tunable parameters, commonly referred to as hyperparameters. Hyperparameter optimization is the process by which we find the values for these parameters that cause our system to perform the best. SigOpt provides a Bayesian optimization platform that is commonly used for hyperparameter optimization, and I’m going to share some of the common problems we’ve seen when integrating into machine learning pipelines.
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
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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.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
3. Opportunity: Standardize process, boost performance
Notebook & Model Framework
Hardware Environment
Data
Preparation
Experimentation, Training, Evaluation
Model
Productionalization
Validation
Serving
Deploying
Monitoring
Managing
Inference
Online Testing
Transformation
Labeling
Pre-Processing
Pipeline Dev.
Feature Eng.
Feature Stores
On-Premise Hybrid Multi-Cloud
Experimentation & Model Optimization
Insights, Tracking,
Collaboration
Model Search,
Hyperparameter Tuning
Resource Scheduler,
Management
...and more
4. Your firewall
Training
Data
AI, ML, DL,
Simulation Model
Model Evaluation
or Backtest
Testing
Data
New
Configurations
Tracked
Objective
Metrics
Better
Results
EXPERIMENT INSIGHTS
Track, organize, analyze and
reproduce any model
ENTERPRISE PLATFORM
Built to fit any stack and scale
with your needs
OPTIMIZATION ENGINE
Explore and exploit with a
variety of techniques
RESTAPI
Configuration
Parameters or
Hyperparameters
Your data
and models
stay private
Iterative, automated optimization
Integrates
with any
modeling
stack
5. Context: From optimization system to modeling platform
Early Stopping
Multitask
Multisolution
Conditionals
Constraints
Failure Regions
10,000 Observations
100x Parallel
100 Parameters
Model
Optimization
Algorithms
Model
Optimization
System
Patented ensemble
of Bayesian & global
optimization
algorithms for any
combination of
parameter types
Patented system for
millisecond latency
and asynchronous
parallelization
Advanced
Model
Optimization
Experiment
Insights
Full Exp. History
Best-Seen Trace
Parameter Importance
Parallel Coordinates
Multitask Insights
Metric
Management
Multimetric Optimization
Multimetric Visualization
Metric Strategy
Metric Tracking
Metric Thresholds
Metric Constraints
Training
Insights
6. Challenge: Tough to define and select the right metric
Business contexts create
more than one metric
Optimizing on a single
metric is limiting or
inadequate
Need to understand
tradeoffs between
metrics
Multimetric optimization
generates a Pareto
efficient frontier
Metric constraints
consider additional
business constraints
Metric strategy records
all observable metric
values
Need to apply guardrail
to metrics
Neet to track and
monitor additional
information
8. SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
9. SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
10. SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
Metric Strategy Control which metrics are targeted—and how—via API
11. SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
Metric Strategy Control which metrics are targeted—and how—via API
Multimetric Optimization
Optimize against two metrics at the same time,
and analyze the tradeoff frontier
12. SigOpt. Confidential.
Metric Management Feature How it Helps You
Metric Storage Allows for later analysis
Metric Thresholds Help SigOpt focus on optimizations that meet business needs
Metric Constraints Set boundaries on what metric outcomes are useful for your end result
Metric Strategy Control which metrics are targeted—and how—via API
Multimetric Optimization
Optimize against two metrics at the same time,
and analyze the tradeoff frontier
Observation/metric failures
Mark failed observation states,
to guide SigOpt away from their regions
13. Use Case: Metric Management for Computer Vision
Road Sign Classification task
● Dataset:
German Traffic Sign Recognition Benchmark
● Modeling framework: Keras
● Model type: CNN
14. Now, on to the demo!
Harvey Cheng
SigOpt Research Engineer
15. Feature Use case
Metric Storage
Tracking auxiliary metrics such as training time
and testing accuracy for later analysis.
Multimetric Optimization
Optimizing validation accuracy of the network and the MAC
operations. Understanding tradeoffs.
Metric Constraints Setting thresholds on the size of the network.
Observation/metric failures
Marking diverged networks as failures
to resolve for further investigation.
Demo Recap
16. “Integrating SigOpt with our modeling platform
empowers our team to more efficiently experiment,
optimize and, ultimately, model at scale.”
Peter Welinder
Research Scientist
17. “We’ve integrated SigOpt’s optimization service and
are now able to get better results faster and cheaper
than any solution we’ve seen before.”
Matt Adereth
Managing Director
19. SigOpt. Confidential.
Check out our
YouTube channel:
See the example yourself:
Find the public
SigOpt experiment here.
Try our solution:
Sign up at
sigopt.com/try-it
today.
Click Here
Upcoming webinars:
● Warm Start Tuning with Prior Beliefs
Thursday, June 4 at 10am PT / 1pm ET
● Detecting COVID-19 Cases
with Deep Learning
Tuesday, June 9 at 10am PT / 1pm ET