Self driving computers active learning workflows with human interpretable ve...Adam Gibson
Human in the loop learning workflows leveraging deep learning to group and cluster data. Also, techniques for accounting for machine learning failures.
Self driving computers active learning workflows with human interpretable ve...Adam Gibson
Human in the loop learning workflows leveraging deep learning to group and cluster data. Also, techniques for accounting for machine learning failures.
Anomaly Detection and Automatic Labeling with Deep LearningAdam Gibson
Adam Gibson demonstrates how to use variational autoencoders to automatically label time series location data. You'll explore the challenge of imbalanced classes and anomaly detection, learn how to leverage deep learning for automatically labeling (and the pitfalls of this), and discover how you can deploy these techniques in your organization.
CI/CD for Machine Learning with Daniel KobranDatabricks
What we call the public cloud was developed primarily to manage and deploy web servers. The target audience for these products is Dev Ops. While this is a massive and exciting market, the world of Data Science and Deep Learning is very different — and possibly even bigger. Unfortunately, the tools available today are not designed for this new audience and the cloud needs to evolve. This talk would cover what the next 10 years of cloud computing will look like.
How to Feed a Data Hungry Organization – by Traveloka Data TeamTraveloka
In Traveloka's Inaugural Data Meetup held in April 2017, Ainun Najib (Head of Data), Dr. Philip Thomas (Lead Data Scientist), and Rendy B. Junior (Lead Data Engineer) shared about the journey that Traveloka's Data Team have taken so far so that the audience can learn from the struggles and triumphs in managing Traveloka's burgeoning data.
You will learn more about:
1) Data culture in Traveloka
2) Data engineering in Traveloka
3) Data science in Traveloka
To follow our LinkedIn page, visit bit.ly/TravelokaLinkedInPage
Safe Harbor Statement
Our discussion may include predictions, estimates or other information that might be considered conclusive. While these conclusive statements represent our current judgment on the best practices, they are subject to risks and uncertainties that could cause actual results to differ materially. You are cautioned not to place undue reliance on our statements, which reflect our opinions only as of the date of this presentation. Please keep in mind that we are not obligating ourselves to revise or publicly release the results of any revision to these presentation materials in light of new information or future events.
A Microservices Framework for Real-Time Model Scoring Using Structured Stream...Databricks
Open-source technologies allow developers to build microservices framework to build myriad real-time applications. One such application is building the real-time model scoring. In this session,
we will showcase how to architect a microservice framework, in particular how to use it to build a low-latency, real-time model scoring system. At the core of the architecture lies Apache Spark’s Structured
Streaming capability to deliver low-latency predictions coupled with Docker and Flask as additional open source tools for model service. In this session, you will walk away with:
* Knowledge of enterprise-grade model as a service
* Streaming architecture design principles enabling real-time machine learning
* Key concepts and building blocks for real-time model scoring
* Real-time and production use cases across industries, such as IIOT, predictive maintenance, fraud detection, sepsis etc.
H2O World - Survey of Available Machine Learning Frameworks - Brendan HergerSri Ambati
H2O World 2015 - Brendan Herger of Capital One
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/1...MLconf
Recommendations for Building Machine Learning Software: Building a real system that uses machine learning can be a difficult both in terms of the algorithmic and engineering challenges involved. In this talk, I will focus on the engineering side and discuss some of the practical lessons we’ve learned from years of developing the machine learning systems that power Netflix. I will go over what it takes to get machine learning working in a real-life feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. This involves lessons around challenges such as where to place algorithmic components, how to handle distribution and parallelism, what kinds of modularity are useful, how to support both production experimentation, and how to test machine learning systems.
20170721 future of reactive architecturesJamie Allen
Some knowledge will be difficult to scale across an entire team. How do we build applications that are reactive while still delivering business value quickly?
Anomaly Detection and Automatic Labeling with Deep LearningAdam Gibson
Adam Gibson demonstrates how to use variational autoencoders to automatically label time series location data. You'll explore the challenge of imbalanced classes and anomaly detection, learn how to leverage deep learning for automatically labeling (and the pitfalls of this), and discover how you can deploy these techniques in your organization.
CI/CD for Machine Learning with Daniel KobranDatabricks
What we call the public cloud was developed primarily to manage and deploy web servers. The target audience for these products is Dev Ops. While this is a massive and exciting market, the world of Data Science and Deep Learning is very different — and possibly even bigger. Unfortunately, the tools available today are not designed for this new audience and the cloud needs to evolve. This talk would cover what the next 10 years of cloud computing will look like.
How to Feed a Data Hungry Organization – by Traveloka Data TeamTraveloka
In Traveloka's Inaugural Data Meetup held in April 2017, Ainun Najib (Head of Data), Dr. Philip Thomas (Lead Data Scientist), and Rendy B. Junior (Lead Data Engineer) shared about the journey that Traveloka's Data Team have taken so far so that the audience can learn from the struggles and triumphs in managing Traveloka's burgeoning data.
You will learn more about:
1) Data culture in Traveloka
2) Data engineering in Traveloka
3) Data science in Traveloka
To follow our LinkedIn page, visit bit.ly/TravelokaLinkedInPage
Safe Harbor Statement
Our discussion may include predictions, estimates or other information that might be considered conclusive. While these conclusive statements represent our current judgment on the best practices, they are subject to risks and uncertainties that could cause actual results to differ materially. You are cautioned not to place undue reliance on our statements, which reflect our opinions only as of the date of this presentation. Please keep in mind that we are not obligating ourselves to revise or publicly release the results of any revision to these presentation materials in light of new information or future events.
A Microservices Framework for Real-Time Model Scoring Using Structured Stream...Databricks
Open-source technologies allow developers to build microservices framework to build myriad real-time applications. One such application is building the real-time model scoring. In this session,
we will showcase how to architect a microservice framework, in particular how to use it to build a low-latency, real-time model scoring system. At the core of the architecture lies Apache Spark’s Structured
Streaming capability to deliver low-latency predictions coupled with Docker and Flask as additional open source tools for model service. In this session, you will walk away with:
* Knowledge of enterprise-grade model as a service
* Streaming architecture design principles enabling real-time machine learning
* Key concepts and building blocks for real-time model scoring
* Real-time and production use cases across industries, such as IIOT, predictive maintenance, fraud detection, sepsis etc.
H2O World - Survey of Available Machine Learning Frameworks - Brendan HergerSri Ambati
H2O World 2015 - Brendan Herger of Capital One
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/1...MLconf
Recommendations for Building Machine Learning Software: Building a real system that uses machine learning can be a difficult both in terms of the algorithmic and engineering challenges involved. In this talk, I will focus on the engineering side and discuss some of the practical lessons we’ve learned from years of developing the machine learning systems that power Netflix. I will go over what it takes to get machine learning working in a real-life feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. This involves lessons around challenges such as where to place algorithmic components, how to handle distribution and parallelism, what kinds of modularity are useful, how to support both production experimentation, and how to test machine learning systems.
20170721 future of reactive architecturesJamie Allen
Some knowledge will be difficult to scale across an entire team. How do we build applications that are reactive while still delivering business value quickly?
Presentation of a session about how we use AWS Neptune graph database and the road that we took from 0 to production as it was given at GeekTime Code conference by Ohad Israeli Tech Engineering Architect @ Natural Intelligence
Intro - End to end ML with Kubeflow @ SignalConf 2018Holden Karau
There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers.
This is a reupload of the talk I delivered at the Spark London Meetup group, November 2016. Original link to the event: https://www.meetup.com/Spark-London/events/235626954/
I share observations and best practices.
Accelerating Apache Spark by Several Orders of Magnitude with GPUs and RAPIDS...Databricks
GPU acceleration has been at the heart of scientific computing and artificial intelligence for many years now. GPUs provide the computational power needed for the most demanding applications such as Deep Neural Networks, nuclear or weather simulation. Since the launch of RAPIDS in mid-2018, this vast computational resource has become available for Data Science workloads too. The RAPIDS toolkit, which is now available on the Databricks Unified Analytics Platform, is a GPU-accelerated drop-in replacement for utilities such as Pandas/NumPy/ScikitLearn/XGboost. Through its use of Dask wrappers the platform allows for true, large scale computation with minimal, if any, code changes.
The goal of this talk is to discuss RAPIDS, its functionality, architecture as well as the way it integrates with Spark providing on many occasions several orders of magnitude acceleration versus its CPU-only counterparts.
Building Google Cloud ML Engine From Scratch on AWS with PipelineAI - ODSC Lo...Chris Fregly
http://pipeline.ai
Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements from Kubernetes, Istio, and TensorFlow.
In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment.
This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production.
Following the Successful Netflix Culture that I lived and breathed (https://www.slideshare.net/reed2001/culture-1798664/2-Netflix_CultureFreedom_Responsibility2), I give Data Scientists the Freedom and Responsibility to extend their ML / AI pipelines and experiments safely into production.
Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong.
Learn to be fast and strong by attending this talk.
http://pipeline.ai
IBM Runtimes Performance Observations with Apache SparkAdamRobertsIBM
In this talk presented at the Spark London meetup on the 23rd of November 2016 I have detailed our findings in IBM's Runtime Technologies department around Apache Spark. I share best practices we observed by profiling Spark on a variety of workloads I have covered and help Spark users to profile their own applications. I've also touched on how anybody can develop using fast networking capabilities (RDMA) and can achieve substantial performance speedups using GPUs.
Big Data Beyond the JVM - Strata San Jose 2018Holden Karau
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
Introduction to serverless computing on Google Cloudwesley chun
This is a 15-20 minute tech talk designed for those who wish to get a broad high-level introduction to serverless computing. Tech featured includes Google App Engine, Google Cloud Functions, and Google Apps Script.
Designing flexible apps deployable to App Engine, Cloud Functions, or Cloud Runwesley chun
Many people ask, "Which one is better for me: App Engine, Cloud Functions, or Cloud Run?" To help you learn more about them, understand their differences, appropriate use cases, etc., why not deploy the same app to all 3? With this "test drive," you only need to make minor config changes between platforms. You'll also learn one of Google Cloud's AI/ML "building block" APIs as a bonus as the sample app is a simple "mini" Google Translate "MVP". This is a 45- 60-minute talk that reviews the Google Cloud serverless compute platforms then walks through the same app and its deployments. The code is maintained at https://github.com/googlecodelabs/cloud-nebulous-serverless-python
RAPIDS – Open GPU-accelerated Data ScienceData Works MD
RAPIDS – Open GPU-accelerated Data Science
RAPIDS is an initiative driven by NVIDIA to accelerate the complete end-to-end data science ecosystem with GPUs. It consists of several open source projects that expose familiar interfaces making it easy to accelerate the entire data science pipeline- from the ETL and data wrangling to feature engineering, statistical modeling, machine learning, and graph analysis.
Corey J. Nolet
Corey has a passion for understanding the world through the analysis of data. He is a developer on the RAPIDS open source project focused on accelerating machine learning algorithms with GPUs.
Adam Thompson
Adam Thompson is a Senior Solutions Architect at NVIDIA. With a background in signal processing, he has spent his career participating in and leading programs focused on deep learning for RF classification, data compression, high-performance computing, and managing and designing applications targeting large collection frameworks. His research interests include deep learning, high-performance computing, systems engineering, cloud architecture/integration, and statistical signal processing. He holds a Masters degree in Electrical & Computer Engineering from Georgia Tech and a Bachelors from Clemson University.
LCU14 310- Cisco ODP
---------------------------------------------------
Speaker: Robbie King
Date: September 17, 2014
---------------------------------------------------
★ Session Summary ★
Cisco to present their experience using ODP to provide portable accelerated access to crypto functions on various SoCs.
---------------------------------------------------
★ Resources ★
Zerista: http://lcu14.zerista.com/event/member/137757
Google Event: https://plus.google.com/u/0/events/ckmld1hll5jjijq11frbqmptet8
Video: https://www.youtube.com/watch?v=eFlTmslVK-Y&list=UUIVqQKxCyQLJS6xvSmfndLA
Etherpad: http://pad.linaro.org/p/lcu14-310
---------------------------------------------------
★ Event Details ★
Linaro Connect USA - #LCU14
September 15-19th, 2014
Hyatt Regency San Francisco Airport
---------------------------------------------------
http://www.linaro.org
http://connect.linaro.org
Deploying signature verification with deep learningAdam Gibson
Presentation covered building a signature verification system and deploying it to production. This includes resources usage as well as how the model was picked.
Meetup held in Tokyo with Deep learning Otemachi.
Recent presentation on deeplearning4j's new features as well as some underused features of the AI framework like arbiter,datavec's transform process and libnd4j.
This talk was on deep learning use cases outside of computer vision. It also covered larger scale patterns of what good deep learning use cases typically look like. We end up on an explanation of anomaly detection and various kinds of anomaly use cases.
Distributed deep rl on spark strata singaporeAdam Gibson
This talk briefly covers deep reinforcemeent learning on spark and the benefits of using large scale commodity compute with gpus for ease of running simulations as well as distributed training for use cases that aren't games such as network intrusion and risk. This talk also briefly mentions rl4j and our work with openai gym.
Deep learning in production with the bestAdam Gibson
Getting deep learning adopted at your company. The current landscape of academia vs industry. Presentation at AI with the best (online conference):
http://ai.withthebest.com/
Strata Beijing - Deep Learning in Production on SparkAdam Gibson
Recent talk at strata beijing - half english half chinese covering use cases of deep learning, deep learning in production and the different components of deeplearning4j.
Gave a talk at:
www.meetup.com/SF-Bayarea-Machine-Learning/events/221739934/
Covers basic architecture of a scientific lib and my take on it with nd4j.
These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
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.
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
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
4. ● “Startup production”: Cloud, small scale problems, care more about finding
product market fit than optimal solution
● “Enterprise Production”: Lots of teams and heads, this is the scale you think
about static typing mattering
● “Research Production”: Not much care about software engineering, focused
on quick results and prototyping to prove ideas
| DEFINING PRODUCTION
5. | VARIOUS KINDS OF GPU CLUSTERS
● On Prem Research: Typically a small cluster ~4 to 5 nodes, python HPC
stack
● Cloud Research: AWS/Azure Spin up resources as needed. Typically just 1
machine
● On Prem Production: HPC, Video Transcoding, Big Data Hybrid
(Mesos,YARN,..)
● Self Driving Cars
● Google/FB/Baidu scale ←- You don’t have this
6. Credit: NVIDIA
SPARK / FLINK
Credit: Data Artisans:
http://www.nextplatform.com/2015/02/22/flink-sparks
-next-wave-of-distributed-data-processing/
| BIG DATA HYBRID CLUSTER
9. | INFERENCE
DATA SOURCE DATA SPOUT
Logs
IoT
RDBMS
SKIL:
TRAINED MODEL
GPUs
CUDA
CPUs
MKL
MODEL MODEL
VECTORIZATION
DROPWIZARD
WEB
APPLICATION
10. | PROBLEM TO THINK ABOUT JOBS ON GPU CLUSTER
● Memory management
● Throughput (Jobs are more than matrix math!)
● Resource provisioning: number of gpus, cpus, RAM
● GPU allocation per job
● Runtime (Python <-> Java is expensive and defeats the point of GPUs)
11. ● Javacpp for memory management
● We have our own GC for CUDA
and CUDNN as well (JIT collection
on gpu by tracking references via
JVM)
| KEY FOR BIG DATA CLUSTERS
(HOW WE DO IT: OWN THE STACK)
● We track buffers allocated transparently
via nd4j
● If on cluster Run everything as spark
(soon flink!) job
● (In alpha: Run parameter server!)
12. | WORKFLOW FOR DATA SCIENTISTS
● Import via keras (with explicit transfer learning api!)
● Use keras as front end backed by JVM stack (1 runtime not 2 to deal with
and debug: being python and java)
● Run training on spark or local via ParallelWrapper (parameter
averaging/multi gpu for single node) while using training UI
13. | WORKFLOW FOR DATA ENGINEERS AND
MICROSERVICES DEVELOPERS
● Define pipeline via datavec (same pipeline can be used for production and
training)
● Optionally import model from data scientist
● Create model with scalnet or deeplearning4j in java
● Deploy with spring boot/lagom/play (android also supported)
● Training and inference can run on gpu with nd4j (no code changes)
● Flexibility allows for scenarios like kafka -> tensorrt
14. | RECENT CASE STUDY: NASA JPL
● https://github.com/USCDataScience/dl4j-kerasimport-examples
● Apache Tika and other ETL tools written for JVM (pretty common)
● Data Science team using keras in python
● Hassle integrating, switched to dl4j after model import came out
● Reason: Embeddable (not a gigantic runtime with lots of dependencies)