VSSML16 LR2. Summary Day 2
Valencian Summer School in Machine Learning 2016
Day 2 VSSML16
Summary Day 2
Mercè Martin (BigML)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2016
VSSML16 LR1. Summary Day 1
Valencian Summer School in Machine Learning 2016
Day 1
Summary Day 1
Mercè Martin (BigML)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2016
VSSML16 L5. Basic Data Transformations
Valencian Summer School in Machine Learning 2016
Day 2 VSSML16
Lecture 5
Basic Data Transformations
Poul Petersen (BigML)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2016
Valencian Summer School 2015
Day 1
Lecture 5
Data Transformation and Feature Engineering
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School in Machine Learning 2017 - Day 2
Lecture 6: Time Series and Deepnets. By Charles Parker (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
VSSML16 LR1. Summary Day 1
Valencian Summer School in Machine Learning 2016
Day 1
Summary Day 1
Mercè Martin (BigML)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2016
VSSML16 L5. Basic Data Transformations
Valencian Summer School in Machine Learning 2016
Day 2 VSSML16
Lecture 5
Basic Data Transformations
Poul Petersen (BigML)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2016
Valencian Summer School 2015
Day 1
Lecture 5
Data Transformation and Feature Engineering
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School in Machine Learning 2017 - Day 2
Lecture 6: Time Series and Deepnets. By Charles Parker (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Valencian Summer School in Machine Learning 2017 - Day 2
Lecture Review: Summary Day 2 Sessions. By Mercè Martín Prats (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
Winning Kaggle 101: Introduction to StackingTed Xiao
An Introduction to Stacking by Erin LeDell, from H2O.ai
Presented as part of the "Winning Kaggle 101" event, hosted by Machine Learning at Berkeley and Data Science Society at Berkeley. Special thanks to the Berkeley Institute of Data Science for the venue!
H2O.ai: http://www.h2o.ai/
ML@B: ml.berkeley.edu
DSSB: http://dssberkeley.org
BIDS: http://bids.berkeley.edu/
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Overview of Machine Learning and Feature EngineeringTuri, Inc.
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or “flocks” that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.
Robust and declarative machine learning pipelines for predictive buying at Ba...Gianmario Spacagna
Proof of concept of how to use Scala, Spark and the recent library Sparkz for building production quality machine learning pipelines for predicting buyers of financial products.
The pipelines are implemented through custom declarative APIs that gives us greater control, transparency and testability of the whole process.
The example followed the validation and evaluation principles as defined in The Data Science Manifesto available in beta at www.datasciencemanifesto.org
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
Brazilian Summer School in Machine Learning 2016
Day 2 - Lecture 4: Advanced Workflows: Feature Selection, Boosting, Gradient Descent, and Stacking
Lecturer: Dr. José Antonio Ortega - jao (BigML)
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Valencian Summer School in Machine Learning 2017 - Day 2
Lecture Review: Summary Day 2 Sessions. By Mercè Martín Prats (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
Winning Kaggle 101: Introduction to StackingTed Xiao
An Introduction to Stacking by Erin LeDell, from H2O.ai
Presented as part of the "Winning Kaggle 101" event, hosted by Machine Learning at Berkeley and Data Science Society at Berkeley. Special thanks to the Berkeley Institute of Data Science for the venue!
H2O.ai: http://www.h2o.ai/
ML@B: ml.berkeley.edu
DSSB: http://dssberkeley.org
BIDS: http://bids.berkeley.edu/
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Overview of Machine Learning and Feature EngineeringTuri, Inc.
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or “flocks” that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.
Robust and declarative machine learning pipelines for predictive buying at Ba...Gianmario Spacagna
Proof of concept of how to use Scala, Spark and the recent library Sparkz for building production quality machine learning pipelines for predicting buyers of financial products.
The pipelines are implemented through custom declarative APIs that gives us greater control, transparency and testability of the whole process.
The example followed the validation and evaluation principles as defined in The Data Science Manifesto available in beta at www.datasciencemanifesto.org
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
Brazilian Summer School in Machine Learning 2016
Day 2 - Lecture 4: Advanced Workflows: Feature Selection, Boosting, Gradient Descent, and Stacking
Lecturer: Dr. José Antonio Ortega - jao (BigML)
BSSML16 L8. REST API, Bindings, and Basic WorkflowsBigML, Inc
Brazilian Summer School in Machine Learning 2016
Day 2 - Lecture 3: REST API, Bindings, and Basic Workflows
Lecturer: Dr. José Antonio Ortega - jao (BigML)
Learn all you need to know about BigML's implementation of Latent Dirichlet Allocation (LDA), one of the most popular probabilistic methods for topic modeling. Topic Models, BigML's latest resource, helps you find relevant terms thematically related in your unstructured text data. With the BigML Topic Models in your Dashboard and in the BigML API, you will be able to discover the hidden topics in your text fields and use them as final output for information retrieval tasks, collaborative filtering, or for assessing document similarity, among others. You can also use the topics discovered as input features to train other models.
Valencian Summer School in Machine Learning 2017 - Day 1
Lectures Review: Summary Day 1 Sessions. By Mercè Martín (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
MOPs & ML Pipelines on GCP - Session 6, RGDCgdgsurrey
MLOps Lifecycle
ML problem framing
ML solution architecture
Data preparation and processing
ML model development
ML pipeline automation and orchestration
ML solution monitoring, optimization, and maintenance
Predictive Analytics Project in Automotive IndustryMatouš Havlena
Original article: http://www.havlena.net/en/business-analytics-intelligence/predictive-analytics-project-in-automotive-industry/
I had a chance to work on a predictive analytics project for a US car manufacturer. The goal of the project was to evaluate the feasibility to use Big Data analysis solutions for manufacturing to solve different operational needs. The objective was to determine a business case and identify a technical solution (vendor). Our task was to analyze production history data and predict car inspection failures from the production line. We obtained historical data on defects on the car, how the car moved along the assembly line and car specific information like engine type, model, color, transmission type, and so on. The data covered the whole manufacturing history for one year. We used IBM BigInsights and SPSS Modeler to make the predictions.
AI/ML Infra Meetup | ML explainability in MichelangeloAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Eric Wang (Software Engineer, @Uber)
Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes.
In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
[DSC Europe 22] Smart approach in development and deployment process for vari...DataScienceConferenc1
During development of machine learning model about 80% of time is used for data preparation and due to data quality issues, especially when there is a need to combine data from structured and unstructured data sources. Development of smart generic data mart can reduce go to production time for new ML models. We will share creative solutions for challenges we encountered during data transfer between DWH and Data Lake, furthermore data preprocessing, development, deployment/orchestration of ML models, using python/pyspark scripts.
Production-Ready BIG ML Workflows - from zero to heroDaniel Marcous
Data science isn't an easy task to pull of.
You start with exploring data and experimenting with models.
Finally, you find some amazing insight!
What now?
How do you transform a little experiment to a production ready workflow? Better yet, how do you scale it from a small sample in R/Python to TBs of production data?
Building a BIG ML Workflow - from zero to hero, is about the work process you need to take in order to have a production ready workflow up and running.
Covering :
* Small - Medium experimentation (R)
* Big data implementation (Spark Mllib /+ pipeline)
* Setting Metrics and checks in place
* Ad hoc querying and exploring your results (Zeppelin)
* Pain points & Lessons learned the hard way (is there any other way?)
In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. This talks explores recent advances in this area in both research and practice. I will explain how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models.
Enhancing and Automating Decision Making with Machine Learning - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML for AML ComplianceBigML, Inc
Machine Learning for Anti Money Laundering Compliance, by Kevin Nagel, Consultant and Data Scientist at INFORM.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - My First Anomaly Detector BigML, Inc
My First Anomaly Detector: Practical Workshop, by Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Introduction to End-to-End Machine Learning: Classification and Regression - Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - A Data-Driven CompanyBigML, Inc
A Data-Driven Company: 21 Lessons for Large Organizations to Create Value from AI, by Richard Benjamins, Chief AI and Data Strategist at Telefónica.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML in the Legal SectorBigML, Inc
How Machine Learning Transforms and Automates Legal Services, by Arnoud Engelfriet, Co-Founder at Lynn Legal.
*Machine Learning School in The Netherlands 2022.
Machine Learning for Public Safety: Reducing Violence and Discrimination in Stadiums.
Speakers: Ramon van Ingen, Co-Founder at Siip, Entrepreneur, Researcher, and Pablo González, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Anomaly Detection at ScaleBigML, Inc
Lessons Learned Applying Anomaly Detection at Scale, by Álvaro Clemente, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Citizen Development in AIBigML, Inc
Citizen Development in AI, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
This new feature is a continuation of and improvement on our previous Image Processing release. Now, Object Detection lets you go a step further with your image data and allows you to locate objects and annotate regions in your images. Once your image regions are defined, you can train and evaluate Object Detection models, make predictions with them, and automate end-to-end Machine Learning workflows on a single platform. To make that possible, BigML enables Object Detection by introducing the regions optype.
As with any other BigML feature, Object Detection is available from the BigML Dashboard, API, and WhizzML for automation. Object Detection is extremely helpful to tackle a wide range of computer vision use cases such as medical image analysis, quality control in manufacturing, license plate recognition in transportation, people detection in security surveillance, among many others.
This new release brings Image Processing to the BigML platform, a feature that enhances our offering to solve image data-driven business problems with remarkable ease of use. Because BigML treats images as any other data type, this unique implementation allows you to easily use image data alongside text, categorical, numeric, date-time, and items data types as input to create any Machine Learning model available in our platform, both supervised and unsupervised.
Now, it is easier than ever to solve a wide variety of computer vision and image classification use cases in a single platform: label your image data, train and evaluate your models, make predictions, and automate your end-to-end Machine Learning workflows. As with any other BigML feature, Image Processing is available from the BigML Dashboard, API, and WhizzML, and it can be applied to solve use cases such as medical image analysis, visual product search, security surveillance, and vehicle damage detection, among others.
Machine Learning in Retail: Know Your Customers' Customer. See Your FutureBigML, Inc
This session presents a quite common situation for those working in food and beverage retail (FnB) and highlights interesting insights to fight waste reduction.
Speaker: Stephen Kinns, CEO and Co-Founder at catsAi.
*ML in Retail 2021: Webinar.
Machine Learning in Retail: ML in the Retail SectorBigML, Inc
This is an introductory session about the role that Machine Learning is playing in the retail sector and how it is being deployed across the different areas of this industry.
Speaker: Atakan Cetinsoy, VP of Predictive Applications at BigML.
*ML in Retail 2021: Webinar.
ML in GRC: Machine Learning in Legal Automation, How to Trust a LawyerbotBigML, Inc
This presentation analyzes the role that Machine Learning plays in legal automation with a real-world Machine Learning application.
Speaker: Arnoud Engelfriet, Co-Founder at Lynn Legal.
*ML in GRC 2021: Virtual Conference.
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...BigML, Inc
This is a real-life Machine Learning use case about integrated risk.
Speakers: Thomas Rengersen, Product Owner of the Governance Risk and Compliance Tool for Rabobank, and Thomas Alderse Baas, Co-Founder and Director of The Bowmen Group.
*ML in GRC 2021: Virtual Conference.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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.
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
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
4. BigML, Inc 4
Basic transformations
●
Select the right model for the problem you want to solve:
Classification, regression, cluster analysis, anomaly detection,
association discovery
●
Perform cleansing, denormalizing, aggregating, pivoting, and
other data wrangling tasks to generate a collection of instances
relevant to the problem at hand. Finally use a very common format as
output format: CSV
●
Choose the right format to store each type of feature into a field
●
Feature engineering: Using domain knowledge and Machine
Learning expertise, generate explicit features that help to better
represent the instances (Flatline)
ML-ready steps
5. BigML, Inc 5
Basic transformations
Cleansing: Homogenize missing values and different types in the
same feature, fix input errors, correct semantic issues, etc.
Denormalizing: Data is usually normalized in relational databases,
MLReady datasets need the information denormalized in a single
file/dataset.
Aggregation: When data is stored as individual transactions, as in
log files, an aggregation to get the entity might be needed
Pivoting: Different values of a feature are pivoted to new columns in
the result dataset
Regular time windows: Create new features using values over
different periods of time
Preprocessing data
6. BigML, Inc 6
Basic transformations
For numeric features:
– Discretization: percentiles, within percentiles, groups
– Replacement
– Normalization
– Exponentiation
– Shocks (speed of change compared to stdev)
For text features:
– Mispellings
– Length
– Number of subordinate sentences
– Language
– Levenshtein distance
Stacking
Compute a field using nonlinear combinations of other fields
Feature engineering
9. BigML, Inc 9
Feature Engineering
Data + ML Algorithm, is that enough?
The ML Algorithm only knows about the features in the dataset.
Features can be useless to the algorithm if:
●
They are not correlated to the objective to be predicted
●
Their values change their meaning when combined with other
features
For ML Algorithms to work there must be some kind of statistical
relation between some of the features and the objective.
Sometimes, you must transform the available features to find such
relations
Feature engineering: the process of transforming raw data into
machine learning readydata
Charles Parker
10. BigML, Inc 10
Feature Engineering
When do you need Feature Engineering?
●
When the relationship between the feature and the
objective is mathematically unsatisfying
●
When the relationship of a function of two or more
features with the objective is far more relevant than the
one of the original features
●
When there is missing data
●
When the data is timeseries, especially when the
previous time period’s objective is known
●
When the data can’t be used for machine learning in the
obvious way (e.g., timestamps, text data)
11. BigML, Inc 11
Feature Engineering
Mathematical transformations
●
Statistical aggregations (group by, all and allbut)
●
Better categories
– too many detailed categories should be avoided
– ordered categories can be translated to numeric values. The model will be able to
extract more information by partinioning the ordered number range
●
Binning or discretization: consider whether your number is more informative in
ranges (quartiles, deciles, percentiles) even for the objective field
●
Linearization: nonimportant for decision trees but can be for logistic regression
(watch out for exponential distributions)
Missing data
●
Missing value induction (replace missings with common values: mean, median,
mode, even with a Machine Learning model)
●
Missing values presence can be informative, so this can be added as a new feature
12. BigML, Inc 12
Feature Engineering
Timeseries transformations
●
Better objective (percent change instead of absolute
values)
●
Deltas from previous reference time points
●
Deltas from moving average (time windows)
●
Recent Volatility...
Problem: Exponential explosion of possible transformations
Caveats:
●
The regularity in time of the points has to match your training data
●
You have to keep track of past points to compute your windows
●
Really easy to get information leakage by including your objective in a
window computation (and can be very hard to detect)!
13. BigML, Inc 13
Feature Engineering
Datetime features
●
Cannot be used “as is” in a model. It's a collection of features. BigML is able to
decompose them automatically when they are provided in the most usual
formats. With Flatline, you can decompose them all.
●
Datetime predicates that the computer does not know (some of them, domain
dependent): Working hours? Daylight? Is rush hour?...
Text features
●
Bag of words: a new feature is associated to each word in the document
●
Tokenization: how do we select tokens? Do we want ngrams? What about
numbers?
●
Stemming: grouping forms of the same word in a unique term
●
Length
●
Text predicates: Dollar amounts? Dates? Salutations? Please and Thank you?
16. BigML, Inc 16
REST API, bindings and basic workflows
jao (José Antonio Ortega)
Academics Real world
How do Machine Learning Workflows look like?
We need highlevel tools to face the real world workflows by growing in:
● Automation
● Abstraction
17. BigML, Inc 17
REST API, bindings and basic workflows
The foundations
●
REST API first applications: Standards in software development.
First level of abstraction
Client side tools
●
Web UI: Sitting on top of the REST API. Humanfriendly access and
visualizations for all the Machine Learning resources. Workflows must
be defined and executed step by step. Second level of abstraction.
●
Bindings: Sitting on top of the REST API. Finegrained accessors for
the REST API calls. Workflows must be defined and executed step by
step. Second level of abstraction.
●
BigMLer: Relying on the bindings. Highlevel syntax. Entire workflows
can be created in only one command line. Third level of abstraction.
18. BigML, Inc 18
REST API, bindings and basic workflows
.
BigMLer automation
●
Basic 1click workflows in one command line
●
Rich parameterized workflows: feature selection, crossvalidation, etc.
●
Models are downloaded to your laptop, tablet, cell phone, etc. once
and can be used offline to create predictions
Still..
Great for local predictions
19. BigML, Inc 19
REST API, bindings and basic workflows
.
Problems of clientside solutions
●
Complexity Lots of details outside the problem
domain
●
Reuse No interlanguage compatibility
●
Scalability Clientside workflows hard to optimize
●
Extensibility BigMLer hides complexity at the cost of
flexibility
●
Not enough abstraction
20. BigML, Inc 20
REST API, bindings and basic workflows
.Solution: bringing automation and abstraction to the serverside
●
DSL for ML workflow automation
●
Framework for scalable, remote execution of ML workflows
Sophisticated serverside optimization
Outofthebox scalability
Clientserver brittleness removed
Infrastructure for creating and sharing ML scripts and libraries
WhizzML
21. BigML, Inc 21
REST API, bindings and basic workflows
.
WhizzML's new REST API resources:
Scripts: Executable code that describes an actual
workflow, taking a list of typed inputs and producing
a list of outputs.
Executions: Given a script and a complete set of
inputs, the workflow can be executed and its outputs
generated.
Libraries: A collection of WhizzML definitions that
can be imported by other libraries or scripts.
22. BigML, Inc 22
REST API, bindings and basic workflows
Scripts
Creating scripts
●
Usable by any binding (from any language)
●
Builtin parallelization
●
BigML resources management as primitives of the language
●
Complete programming language for workflow definition
Using scripts
Web UI
Bindings
BigMLer
WhizzML
23. BigML, Inc 23
Advanced WhizzML workflows
Charles Parker
WhizzML offers:
● Primitives for all ML resources: (datasets, models, clusters, etc.)
● A complete programming language to compose at will these ML resources.
● Parallelization and Scalability builtin.
This empowers the user to benefit from:
● Automated feature engineering: Bestfirst feature selection.
● Automated configuration choice: Randomized parameter optimization,
SMACdown.
● Complex algorithms as 1click: Stacked generalization, Boosting.
All of them can be shared, reproduced and reused as one more
BigML resource in a languageagnostic way.
24. BigML, Inc 24
Advanced WhizzML workflows
f5 fn
... ...
......
... ...
f5 f7 f5 fn
... ...
......
... ...
f5 f1
Selected
fields
()
(f5)
The best score
is obtained for
the model with (f5)
The best score
is obtained for
the model with (f5 f7)
Following iterations don't improve the score for the model
with (f5 f7), so the process stops
Step 1
Step 2
f1
Bestfirst feature selection
25. BigML, Inc 25
Advanced WhizzML workflows
A new dataset is generated
with the predictions for the
hold out data
A new metamodel is created
from this dataset
50%
Hold out
Stacked generalization
26. BigML, Inc 26
Advanced WhizzML workflows
Configuration
random
generator
... ...
Best
score
Process stops when you reach the expected performance
or the usergiven iterations limit
+
Randomized parameter optimization
27. BigML, Inc 27
Advanced WhizzML workflows
Configuration
random
generator
... ...
+ New configurations are filtered
according to the predictions
of the model of performances
Only promising
configurations are analyzed
SMACdown