The slides from my talk at the FOSDEM HPC, Big Data and Data Science devroom, which has general tips from various sources about putting your first machine learning model in production.
The video is available from the FOSDEM website: https://fosdem.org/2017/schedule/event/machine_learning_zoo/
Global web tutors experts provides you help in writing all types of subjects assignment especially for Computer graphics Assignment help. If you need any help in any subject then you can avail our services via call, chat or email to our experts. Our experts are available 24*7.
Global web tutors experts provides you help in writing all types of subjects assignment especially for Computer graphics Assignment help. If you need any help in any subject then you can avail our services via call, chat or email to our experts. Our experts are available 24*7.
The Music Information Retrieval Evaluation eXchange (MIREX) is a valuable community service, having established standard datasets, metrics, baselines, methodologies, and infrastructure for comparing MIR methods. While MIREX has managed to successfully maintain operations for over a decade, its long-term sustainability is at risk without considerable ongoing financial support. The imposed constraint that input data cannot be made freely available to participants necessitates that all algorithms run on centralized computational resources, which are administered by a limited number of people. This incurs an approximately linear cost with the number of submissions, exacting significant tolls on both human and financial resources, such that the current paradigm becomes less tenable as participation increases. To alleviate the recurring costs of future evaluation campaigns, we propose a distributed, community-centric paradigm for system evaluation, built upon the principles of openness, transparency, reproducibility, and incremental evaluation. We argue that this proposal has the potential to reduce operating costs to sustainable levels. Moreover, the proposed paradigm would improve scalability, and eventually result in the release of large, open datasets for improving both MIR techniques and evaluation methods.
Online Machine Learning: introduction and examplesFelipe
In this talk I introduce the topic of Online Machine Learning, which deals with techniques for doing machine learning in an online setting, i.e. where you train your model a few examples at a time, rather than using the full dataset (off-line learning).
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance.
How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them?
Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models.
Agenda:
-----------
3:45pm - 4:00pm: Arrival & Networking
4:00pm - 4:15pm: News & Intro
4:15pm - 5:15pm: How to QA your ML models
5:15pm - 5:30pm: Virtual Snack & Networking
About the main speaker:
---------------------------------
Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.
London TensorFlow Meetup - 18th July 2017Daniel Ecer
Slides from London TensorFlow Meetup on 18th July 2017
Corresponding repositories:
https://github.com/elifesciences/sciencebeam
https://github.com/elifesciences/sciencebeam-gym
Improving How We Deliver Machine Learning Models (XCONF 2019)David Tan
In this talk, we share some better ways of working that help us with some common challenges faced in a ML project.
Repos:
1. https://github.com/ThoughtWorksInc/ml-app-template
2. https://github.com/ThoughtWorksInc/ml-cd-starter-kit
Demo videos:
1. Dockerised setup https://www.youtube.com/watch?v=S6kWaXQ530k
2. Installing cross-cutting services (e.g. GoCD, MLFlow, EFK): https://www.youtube.com/watch?v=p8jKTlcpnks
3. Rolling back harmful models: https://www.youtube.com/watch?v=rNfrgaRTz7c
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
The Music Information Retrieval Evaluation eXchange (MIREX) is a valuable community service, having established standard datasets, metrics, baselines, methodologies, and infrastructure for comparing MIR methods. While MIREX has managed to successfully maintain operations for over a decade, its long-term sustainability is at risk without considerable ongoing financial support. The imposed constraint that input data cannot be made freely available to participants necessitates that all algorithms run on centralized computational resources, which are administered by a limited number of people. This incurs an approximately linear cost with the number of submissions, exacting significant tolls on both human and financial resources, such that the current paradigm becomes less tenable as participation increases. To alleviate the recurring costs of future evaluation campaigns, we propose a distributed, community-centric paradigm for system evaluation, built upon the principles of openness, transparency, reproducibility, and incremental evaluation. We argue that this proposal has the potential to reduce operating costs to sustainable levels. Moreover, the proposed paradigm would improve scalability, and eventually result in the release of large, open datasets for improving both MIR techniques and evaluation methods.
Online Machine Learning: introduction and examplesFelipe
In this talk I introduce the topic of Online Machine Learning, which deals with techniques for doing machine learning in an online setting, i.e. where you train your model a few examples at a time, rather than using the full dataset (off-line learning).
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance.
How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them?
Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models.
Agenda:
-----------
3:45pm - 4:00pm: Arrival & Networking
4:00pm - 4:15pm: News & Intro
4:15pm - 5:15pm: How to QA your ML models
5:15pm - 5:30pm: Virtual Snack & Networking
About the main speaker:
---------------------------------
Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.
London TensorFlow Meetup - 18th July 2017Daniel Ecer
Slides from London TensorFlow Meetup on 18th July 2017
Corresponding repositories:
https://github.com/elifesciences/sciencebeam
https://github.com/elifesciences/sciencebeam-gym
Improving How We Deliver Machine Learning Models (XCONF 2019)David Tan
In this talk, we share some better ways of working that help us with some common challenges faced in a ML project.
Repos:
1. https://github.com/ThoughtWorksInc/ml-app-template
2. https://github.com/ThoughtWorksInc/ml-cd-starter-kit
Demo videos:
1. Dockerised setup https://www.youtube.com/watch?v=S6kWaXQ530k
2. Installing cross-cutting services (e.g. GoCD, MLFlow, EFK): https://www.youtube.com/watch?v=p8jKTlcpnks
3. Rolling back harmful models: https://www.youtube.com/watch?v=rNfrgaRTz7c
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
5. Formulating an ML problem
● Common aspects
○ Model (θ)
Source: Xing (2015)
6. Formulating an ML problem
● Common aspects
○ Model (θ)
○ Data (D)
Source: Xing (2015)
7. Formulating an ML problem
● Common aspects
○ Model (θ)
○ Data (D)
● Objective function: L(θ, D)
Source: Xing (2015)
8. Formulating an ML problem
● Common aspects
○ Model (θ)
○ Data (D)
● Objective function: L(θ, D)
● Prior knowledge: r(θ)
Source: Xing (2015)
9. Formulating an ML problem
● Common aspects
○ Model (θ)
○ Data (D)
● Objective function: L(θ, D)
● Prior knowledge: r(θ)
● ML program: f(θ, D) = L(θ, D) + r(θ)
Source: Xing (2015)
10. Formulating an ML problem
● Common aspects
○ Model (θ)
○ Data (D)
● Objective function: L(θ, D)
● Prior knowledge: r(θ)
● ML program: f(θ, D) = L(θ, D) + r(θ)
● ML Algorithm: How to optimize f(θ, D)
Source: Xing (2015)
11. Example: Improve retention at Twitter
● Goal: Reduce the churn of users on Twitter
● Assumption: Users churn because they don’t engage with the platform
● Idea: Increase the retweets, by promoting tweets more likely to be
retweeted
12. Example: Improve retention at Twitter
● Goal: Reduce the churn of users on Twitter
● Assumption: Users churn because they don’t engage with the platform
● Idea: Increase the retweets, by promoting tweets more likely to be
retweeted
● Data (D):
● Model (θ):
● Objective function - L(D, θ):
● Prior knowledge (Regularization):
● Algorithm:
13. Example: Improve retention at Twitter
● Goal: Reduce the churn of users on Twitter
● Assumption: Users churn because they don’t engage with the platform
● Idea: Increase the retweets, by promoting tweets more likely to be
retweeted
● Data (D): Features and labels, xi
, yi
● Model (θ):
● Objective function - L(D, θ):
● Prior knowledge (Regularization):
● Algorithm:
14. Example: Improve retention at Twitter
● Goal: Reduce the churn of users on Twitter
● Assumption: Users churn because they don’t engage with the platform
● Idea: Increase the retweets, by promoting tweets more likely to be
retweeted
● Data (D): Features and labels, xi
, yi
● Model (θ): Logistic regression, parameters w
○ p(y|x, w) = Bernouli(y | sigm(wΤ
x))
● Objective function - L(D, θ):
● Prior knowledge (Regularization):
● Algorithm:
15. Example: Improve retention at Twitter
● Goal: Reduce the churn of users on Twitter
● Assumption: Users churn because they don’t engage with the platform
● Idea: Increase the retweets, by promoting tweets more likely to be
retweeted
● Data (D): Features and labels, xi
, yi
● Model (θ): Logistic regression, parameters w
○ p(y|x, w) = Bernouli(y | sigm(wΤ
x))
● Objective function - L(D, θ): NLL(w) = Σ log(1 + exp(-y wΤ
xi
))
● Prior knowledge (Regularization): r(w) = λ*wΤ
w
● Algorithm:
Warning:
Notation abuse
16. Example: Improve retention at Twitter
● Goal: Reduce the churn of users on Twitter
● Assumption: Users churn because they don’t engage with the platform
● Idea: Increase the retweets, by promoting tweets more likely to be
retweeted
● Data (D): Features and labels, xi
, yi
● Model (θ): Logistic regression, parameters w
○ p(y|x, w) = Bernouli(y | sigm(wΤ
x))
● Objective function - L(D, θ): NLL(w) = Σ log(1 + exp(-y wΤ
xi
))
● Prior knowledge (Regularization): r(w) = λ*wΤ
w
● Algorithm: Gradient Descent
20. Data readiness
● Problem: “Data” as a concept is hard to reason about.
● Goal: Make the stakeholders aware of the state of the data at all stages
Source: Lawrence (2017)
23. Data readiness
● Band C
○ Accessibility
● Band B
○ Representation and faithfulness
Source: Lawrence (2017)
24. Data readiness
● Band C
○ Accessibility
● Band B
○ Representation and faithfulness
● Band A
○ Data in context
Source: Lawrence (2017)
25. Data readiness
● Band C
○ “How long will it take to bring our user data to C1 level?”
● Band B
○ “Until we know the collection process we can’t move the data to B1.”
● Band A
○ “We realized that we would need location data in order to have an A1 dataset.”
Source: Lawrence (2017)
26. Data readiness
● Band C
○ “How long will it take to bring our user data to C1 level?”
● Band B
○ “Until we know the collection process we can’t move the data to B1.”
● Band A
○ “We realized that we would need location data in order to have an A1 dataset.”
32. Selecting algorithms
● Always go for the simplest model you can afford
○ Your first model is more about getting the infrastructure right
Source: Zinkevich (2017)
33. Selecting algorithms
● Always go for the simplest model you can afford
○ Your first model is more about getting the infrastructure right
○ Simple models are usually interpretable. Interpretable models are easier to debug.
Source: Zinkevich (2017)
34. Selecting algorithms
● Always go for the simplest model you can afford
○ Your first model is more about getting the infrastructure right
○ Simple models are usually interpretable. Interpretable models are easier to debug.
○ Complex model erode boundaries
Source: Sculley et al. (2015)
35. Selecting algorithms
● Always go for the simplest model you can afford
○ Your first model is more about getting the infrastructure right
○ Simple models are usually interpretable. Interpretable models are easier to debug.
○ Complex model erode boundaries
■ CACE principle: Changing Anything Changes Everything
Source: Sculley et al. (2015)
48. Bringing it all together
● Define your problem as optimizing your objective function using data
● Determine (and monitor) the readiness of your data
● Don't spend too much time at first choosing an ML framework/algorithm
● Worry much more about what happens when your model meets the world.
50. Sources
● Google auto-replies: Shared photos, and text
● Silver et al. (2016): Mastering the game of Go
● Xing (2015): A new look at the system, algorithm and theory foundations of Distributed ML
● Lawrence (2017): Data readiness levels
● Asimov Institute (2016): The Neural Network Zoo
● Zinkevich (2017): Rules of Machine Learning - Best Practices for ML Engineering
● Sculley et al. (2015): Hidden Technical Debt in Machine Learning Systems