Greg Werner, CEO & Founder, 3Blades.io at MLconf ATL 2017

•Download as PPTX, PDF•

0 likes•579 views

Productive Machine Learning and Deep Learning Projects Machine Learning (ML) and Deep Learning (DL), known holistically as Artificial Intelligence, are no longer luxuries but necessities if companies want to remain relevant n today’s market. Data driven organizations that encourage the development of ML and DL projects allow companies to create and deploy models to create predictions in real time. Even more exciting, these real time predictions allow organizations to trigger actions based on these predictions, which ultimately improves the bottom line. However, organizations struggle to incorporate ML and DL projects to create models that improve performance. This talk focuses on how companies can enable data science platforms so that data engineers, data scientists and business analysts can quickly explore data, create and test ML and DL models, and deploy to staging and production environments regardless of the language or framework used by the team and organization.

Technology

Data Science with Teams
Improve the efficiency of your data science teams
with platforms that enhance collaboration and
flexibility

Agenda
● Some Background
● Goals
● Data Science Project Teams
● Challenges
● Some Solutions
● Conclusions

Background
Integration experience with Oil & Gas, Financial, Insurance and Retail industries in
multiple geographies
What did these customers have in common? All had data science teams that
worked in Silos
Difficulties when taking a data science
course
Source: Wikipedia

Background (cont)
What’s going on here?
We started to do some digging!
Source: http://cesarsway.com

Data Teams - The Old Way
Department Teams Data Scientist
Data Analyst IT Manager

The Analytics Deliverable
A dashboard! An interactive dashboard is even cooler.

Data Science Teams - The New Way
Data ScientistFinance Manager
Accountant
Tax and Compliance
Treasury
Data Gurus:
- Analytics
- Data Engineers
- Business Intelligence
- Compliance
IT Manager

The Data Science Deliverable
A machine or deep learning model!

I Want GPUs - And I Just Want Them to Work
Work around for NVidia Docker Wrapper:
- nvidia-docker -d -p 8888:8888 tensorflow/tensorflow:latest-gpu
OR
- docker run -ti --rm `curl -s http://localhost:3476/docker/cli`
tensorflow/tensorflow:latest-gpu
OR
- docker run -ti --rm --volume-driver=nvidia-docker --
volume=nvidia_driver_375.82:/usr/local/nvidia:ro --device=/dev/nvidiactl --
device=/dev/nvidia-uvm --device=/dev/nvidia0 nvidia/cuda nvidia-smi

The Need for DevOps Chops
Registrator
Docker
Container
EC2 Instance
Reverse Proxy with consul-template
The old way... The new way...
Docker
Container
EC2 Instance
Reverse Proxy with static upstream location
name
$$$ $

Infrastructure Management
Uh-oh, someone has to manage this stuff!

Our Architecture - API First and Microservices

Solutions
Provide flexibility with the tools that data scientists use for exploratory
data analysis and visualizations
One central source for project files with support for version control
Share visualizations from EDA
Train and save Machine Learning and Deep Learning models with
multiple frameworks, from within the same project
Streamline deployment pipelines

Thank You!!
Email: hello@3blades.io
Web: https://3blades.io
Twitter: @3bladesio
GitHub: https://github.com/3blades
Email: gwerner@3blades.io
Twitter: @gwerner
LinkedIn: https://www.linkedin.com/in/wernergreg
GitHub: https://github.com/jgwerner

Viewers also liked

Classifying Multi-Variate Time Series at Scale: Characterizing and understanding the runtime behavior of large scale Big Data production systems is extremely important. Typical systems consist of hundreds to thousands of machines in a cluster with hundreds of terabytes of storage costing millions of dollars, solving problems that are business critical. By instrumenting each running process, and measuring their resource utilization including CPU, Memory, I/O, network etc., as time series it is possible to understand and characterize the workload on these massive clusters. Each time series is a series consisting of tens to tens of thousands of data points that must be ingested and then classified. At Pepperdata, our instrumentation of the clusters collects over three hundred metrics from each task every five seconds resulting in millions of data points per hour. At this scale the data are equivalent to the biggest IOT data sets in the world. Our objective is to classify the collection of time series into a set of classes that represent different work load types. Or phrased differently, our problem is essentially the problem of classifying multivariate time series. In this talk, we propose a unique, off-the-shelf approach to classifying time series that achieves near best-in-class accuracy for univariate series and generalizes to multivariate time series. Our technique maps each time series to a Grammian Angular Difference Field (GADF), interprets that as an image, uses Google’s pre-trained CNN (trained on Inception v3) to map the GADF images into a 2048-dimensional vector space and then uses a small MLP with two hidden layers, with fifty nodes in each layer, and a softmax output to achieve the final classification. Our work is not domain specific – a fact proven by our achieving competitive accuracies with published results on the univariate UCR data set as well as the multivariate UCI data set. Bio: Before joining Pepperdata, Ash was executive chairman for Marianas Labs, a deep learning startup sold in December 2015. Prior to that he was CEO for Graphite Systems, a big data storage startup that was sold to EMC DSSD in August 2015. Munshi also served as CTO of Yahoo, as a CEO of both public and private companies, and is on the board of several technology startups.

Ashfaq Munshi, ML7 Fellow, Pepperdata

MLconf

Personalized User Recommendations at Tinder: The TinVec Approach: With 26 million matches per day and more than 20 billion matches made to date, Tinder is the world’s most popular app for meeting new people. Our users swipe for a variety of purposes, like dating to find love, expanding social networks and meeting locals when traveling. Recommendation is an important service behind-the-scenes at Tinder, and a good recommendation system needs to be personalized to meet an individual user’s preferences. In this talk, we will discuss a new personalized recommendation approach being developed at Tinder, called TinVec. TinVec embeds users’ preferences into vectors leveraging on the large amount of swipes by Tinder users. We will discuss the design, implementation, and evaluation of TinVec as well as its application to personalized recommendations. Bio: Dr. Steve Liu is chief scientist at Tinder. In his role, he leads research innovation and applies novel technologies to new product developments. He is currently a professor and William Dawson Scholar at McGill University School of Computer Science. He has also served as a visiting research scientist at HP Labs. Dr. Liu has published more than 280 research papers in peer-reviewed international journals and conference proceedings. He has also authored and co-authored several books. Over the course of his career, his research has focused on big data, machine learning/AI, computing systems and networking, Internet of Things, and more. His research has been referenced in articles publishing across The New York Times, IDG/Computer World, The Register, Business Insider, Huffington Post, CBC, NewScientist, MIT Technology Review, McGill Daily and others. He is a recipient of the Outstanding Young Canadian Computer Science Researcher Prizes from the Canadian Association of Computer Science and is a recipient of the Tomlinson Scientist Award from McGill University. He is serving or has served on the editorial boards of ACM Transactions on Cyber-Physical Systems (TCPS), IEEE/ACM Transactions on Networking (ToN), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Vehicular Technology (TVT), and IEEE Communications Surveys and Tutorials (COMST). He has also served on the organizing committees of more than 38 major international conferences and workshops. Dr. Liu received his Ph.D. in Computer Science with multiple honors from the University of Illinois at Urbana-Champaign. He received his Master’s degree in Automation and BSc degree in Mathematics from Tsinghua University.

Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017

MLconf

The Role of AI and Machine Learning in Creativity: I’ll discuss Magenta, a Google Brain project investigating music and art generation using deep learning and reinforcement learning. I’ll describe the goals of Magenta and how it fits into the general trend of AI moving into our daily lives. One crucial question is: Where does AI and Machine Learning fit in the creative process? I’ll argue that it’s about augmenting and extending the artist rather than just creating artifacts (songs, paintings, etc.) with machines. I’ll talk about two recent projects. In the first, we explore the use of recurrent neural networks to extend musical phrases in different ways. In the second we look at teaching a neural network to draw with strokes. This will be a high-level overview talk with no need for knowledge of AI or Machine Learning. Bio:Doug leads Magenta, a Google Brain project working to generate music, video, image and text using deep learning and reinforcement learning. A main goal of Magenta is to better understanding how AI can enable artists and musicians to express themselves in innovative new ways. Before Magenta, Doug led the Google Play Music search and recommendation team. From 2003 to 2010 Doug was faculty at the University of Montreal’s MILA Machine Learning lab, where he worked on expressive music performance and automatic tagging of music audio.

Doug Eck, Research Scientist, Google Magenta, at MLconf SF 2017

MLconf

Machine Learning Based Attack Vector Modeling for CyberSecurity: Connections have behavioural patterns that are unique to protocols, loads, window sizes, bandwidth, and mainly the type of traffic. A CDN enterprise behaves completely differently than how a Cloud service company would behave and they both would be different from a corporation. This also means that attack vectors and attack landscapes are different in all these places. In this talk we speak about modeling different kinds of attacks and build a model that is able to identify these different kinds of attacks using ML. The method we use is to identify different profiles based on many variables that specifically but robustly identify attacks of different kinds. The variables are specific to business, network profile, traffic. The variables are also high-level i.e. aggregate, and packet-level. This way the models are specifically picking up on constant variations in traffic, and create machine learning models to identify these attacks. Using the power of H2O these analyses are not just limited to a research and analysis of the traffic and concluding with a “OH, this was what it was.” moment but to actually deploy code, besides existing IDS and IPS, or deploying highly optimized, independent programs that can handle high thruputs at the rate of 1.2 Million decisions per second making it one of the fastest implementations of ML to identify, defend and protect critical infrastructure that are potentially under threat.

Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017

MLconf

Tensor Decomposition: A Mathematical Tool for Data Analysis: Tensors are multiway arrays, and tensor decompositions are powerful tools for data analysis. In this talk, we demonstrate the wide-ranging utility of the canonical polyadic (CP) tensor decomposition with examples in neuroscience and chemical detection. The CP model is extremely useful for interpretation, as we show with an example in neuroscience. However, it can be difficult to fit to real data for a variety of reasons. We present a novel randomized method for fitting the CP decomposition to dense data that is more scalable and robust than the standard techniques. We further consider the modeling assumptions for fitting tensor decompositions to data and explain alternative strategies for different statistical scenarios, resulting in a _generalized_ CP tensor decomposition. Bio: Tamara G. Kolda is a member of the Data Science and Cyber Analytics Department at Sandia National Laboratories in Livermore, CA. Her research is generally in the area of computational science and data analysis, with specialties in multilinear algebra and tensor decompositions, graph models and algorithms, data mining, optimization, nonlinear solvers, parallel computing and the design of scientific software. She has received a Presidential Early Career Award for Scientists and Engineers (PECASE), been named a Distinguished Scientist of the Association for Computing Machinery (ACM) and a Fellow of the Society for Industrial and Applied Mathematics (SIAM). She was the winner of an R&D100 award and three best paper prizes at international conferences. She is currently a member of the SIAM Board of Trustees and serves as associate editor for both the SIAM J. Scientific Computing and the SIAM J. Matrix Analysis and Applications.

Tamara G. Kolda, Distinguished Member of Technical Staff, Sandia National Lab...

MLconf

Natural Language Understanding @ Facebook Scale: At Facebook, text understanding is the key to surfacing content that’s relevant and personalized, plus enabling new experiences like social recommendations and Marketplace suggestions. In this talk, I will introduce you to DeepText, Facebook’s platform for text understanding, and discuss the various models it supports. Bio: Rushin Shah is an engineering leader at Facebook, currently working on natural language understanding and dialog. Previously, he was at Siri at Apple for 5 years, where he built and headed the natural language understanding group, which included teams dedicated to modeling, engineering and data science. He also worked at the query understanding group at Yahoo. He has worked on a broad range of problems in the NLP area including parsing, information extraction, dialog and question answering. He holds degrees in language technologies and computer science from Carnegie Mellon and IIT Kharagpur.

Rushin Shah, Engineering Manager, Facebook at MLconf SF 2017

MLconf

Codifying Data Science Intuition: Using Decision Theory to Automate Time Series Model Selection: While models generated from cross-sectional data can utilize cross-validation for model selection, most time series models cannot be cross-validated due to the temporal structure of the data used to create them. It is possible to employ a rolling cross-validation technique, however this process is computationally expensive and provides no indication of the long-term forecast accuracies of the models. The purpose of this talk is to elaborate how decision theory can be used to automate time series model selection in order to streamline the manual process of validation and testing. By creating consecutive, temporally independent holdout sets, performance metrics for each model’s prediction on each holdout set are fed into a decision function to select an unbiased model. The decision function helps minimize the poorest performance of each model across all holdout sets in order to counteract the possibility of choosing a model that overfits or underfits the holdout sets. Not only does this process improve forecast accuracy, but it also reduces computation time by only requiring the creation of a fixed number of proposed forecasting models.

Ryan West, Machine Learning Engineer, Nexosis at MLconf ATL 2017

MLconf

Representation Learning @ Red Hat: For many companies, the vast majority of their data is unstructured and unlabeled; however, the data often contains information that could be useful in a variety of scenarios. Representation learning is the process of extracting meaningful features from unlabeled data so that it can be used in other tasks. In this talk, you’ll hear about how Red Hat is using deep learning to discover meaningful entity representations in a number of different settings, including: (1) identifying duplicate documents on the Customer Portal, (2) finding contextually similar URLs with word2vec, and (3) clustering behaviorally similar customers with doc2vec. To close, we will walk through an example demonstrating how representation learning can be applied to Major League Baseball players. Bio: Michael first developed his data crunching chops as an undergraduate at Auburn University (War Eagle!) where he used a number of different statistical techniques to investigate various aspects of salamander biology (work that led to several publications). He then went on to earn a M.S. in evolutionary biology from The University of Chicago (where he wrote a thesis on frog ecomorphology) before changing directions and earning a second M.S. in computer science (with a focus on intelligent systems) from The University of Texas at Dallas. As a Machine Learning Engineer – Information Retrieval at Red Hat, Michael is constantly looking for ways to use the latest and greatest machine learning technology to improve search.

Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017

MLconf

Counter Intuitive Machine Learning for the Industrial Internet of Things: The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world’s most valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT the cost of being wrong can be the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale through the digitalization of industrial assets, there is clearly a growing role for machine learning to help augment and automate human decision making. It is against this backdrop that traditional machine learning techniques must be adapted and need based innovations created. We see industrial machine learning as distinct from consumer machine learning and in this talk we will cover the counterintuitive changes of featurization, metrics for model performance, and human-in-the-loop design changes for using machine learning in an industrial environment. Bio: June Andrews is a Principal Data Scientist at Wise.io, From GE Digital working on a machine learning and data science platform for the Industrial Internet of Things, which includes aviation, trains, and power plants. Previously, she worked at Pinterest spearheading the Data Trustworthiness and Signals Program to create a healthy data ecosystem for machine learning. She has also lead efforts at LinkedIn on growth, engagement, and social network analysis to increase economic opportunity for professionals. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell.

Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital at MLcon...

MLconf

Large-scale Machine Learning: Deep, Distributed and Multi-Dimensional: Modern machine learning involves deep neural network architectures which yields state-of-art performance on multiple domains such as computer vision, natural language processing and speech recognition. As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. Apache MXNet is an open-source framework developed for distributed deep learning. I will describe the underlying lightweight hierarchical parameter server architecture that results in high efficiency in distributed settings. Pushing the current boundaries of deep learning requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. We present new deep learning architectures that preserve the multi-dimensional information in data end-to-end. We show that tensor contractions and regression layers are an effective replacement for fully connected layers in deep learning architectures. They result in significant space savings with negligible performance degradation. These functionalities are available in the Tensorly package with MXNet backend interface for large-scale efficient learning. Bio: Anima Anandkumar is a principal scientist at Amazon Web Services and a Bren professor at Caltech CMS department. Her research interests are in the areas of large-scale machine learning, non-convex optimization and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms. She is the recipient of several awards such as the Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google research award, ARO and AFOSR Young Investigator Awards, NSF Career Award, Early Career Excellence in Research Award at UCI, Best Thesis Award from the ACM Sigmetrics society, IBM Fran Allen PhD fellowship, and several best paper awards. She has been featured in a number of forums such as the yourstory, Quora ML session, O’Reilly media, and so on. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, an assistant professor at U.C. Irvine between 2010 and 2016, and a visiting researcher at Microsoft Research New England in 2012 and 2014.

Anima Anadkumar, Principal Scientist, Amazon Web Services, Endowed Professor,...

MLconf

A Machine Learning approach for detecting a Malware: The project is to improve the way we detect script based malware using Machine Learning. Malware has become one of the most active channel to deliver threats like Banking Trojans and Ransomware. The talk is aimed at finding a new and effective way to detect the malware. We started with acquiring both malicious and clean samples. Later we performed feature identification, while building on top of existing knowledge base of malware. Then we performed automated feature extraction. After certain feature set is obtained, we teased-out feature which are categorical, interdependent or composite. We applied varying machine learning models, producing both binary and categorical outcomes. We cross validated our results and re-tuned our feature set and our model, until we obtained satisfying results, with least false-positives. We concluded that not all the extracted features are significant, in fact some features are detrimental on the model performance. Once such features are factored-out, it results not only in better match, but also provides a significant gain in performance.

Talha Obaid, Email Security, Symantec at MLconf ATL 2017

MLconf

Viewers also liked (11)

Ashfaq Munshi, ML7 Fellow, Pepperdata

Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017

Doug Eck, Research Scientist, Google Magenta, at MLconf SF 2017

Ashrith Barthur, Security Scientist, H2o.ai, at MLconf 2017

Tamara G. Kolda, Distinguished Member of Technical Staff, Sandia National Lab...

Rushin Shah, Engineering Manager, Facebook at MLconf SF 2017

Ryan West, Machine Learning Engineer, Nexosis at MLconf ATL 2017

Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017

Dr. June Andrews, Principal Data Scientist, Wise.io, From GE Digital at MLcon...

Anima Anadkumar, Principal Scientist, Amazon Web Services, Endowed Professor,...

Talha Obaid, Email Security, Symantec at MLconf ATL 2017

More from MLconf

Understanding Human Impact: Social and Equity Assessments for AI Technologies Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of new and emerging technologies. In this presentation, my goal is to advocate for and highlight the need to consult community and external stakeholder engagement to develop a new knowledge base and understanding of the human and social consequences of algorithmic decision making and to introduce principles, methods and process for these types of impact assessments.

Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...

MLconf

The Brain’s Guide to Dealing with Context in Language Understanding Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity. In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.

Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

MLconf

Applying Computer Vision to Reduce Contamination in the Recycling Stream With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly $5 billion annual recycling market could come to a halt. Purity in the recycling stream is key to this effort as contaminants in the stream can increase the cost of operations, damage equipment and reduce the ability to create pure commodities suitable for creating recycled goods. This market disruption as a result of China’s new regulations, however, provides us the chance to re-examine and improve our current disposal & collection habits with modern monitoring & artificial intelligence technology. Using images from our in-dumpster cameras, Compology has developed an ML-based process that helps identify, measure and alert for contaminants in recycling containers before they are picked-up, helping keep the recycling stream clean. Our convolutional neural network flags potential instances of contamination inside a dumpster, enabling garbage haulers to know which containers have the wrong type of material inside. This allows them to provide targeted, timely education, and when appropriate, assess fines, to improve recycling compliance at the businesses and residences they serve, helping keep recycling services financially viable. In this presentation, we will walk through our ML-based contamination measurement and scoring process by showing how Waste Management, a national waste hauler, has experienced 57% contamination reduction in nearly 2,000 containers over six months, This progress shows significant strides towards financially viable recycling services.

Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...

MLconf

Quantum Computing: a Treasure Hunt, not a Gold Rush Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations --- both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we outline what stands in the way of successful quantum ML applications.

Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush

MLconf

Data Labeling as Religious Experience One of the most common places to deploy a production machine learning systems is as a replacement for a legacy rules-based system that is having a hard time keeping up with new edge cases and requirements. I'll be walking through the process and tooling we used to help us design, train, and deploy a model to replace a set of static rules we had for handling invite spam at Slack, talk about what we learned, and discuss some problems to solve in order to make these migrations easier for everyone.

Josh Wills - Data Labeling as Religious Experience

MLconf

Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics The emergence of the upright human bipedal gait can be traced back 4 to 2.8 million years ago, to the now extinct hominin Australopithecus afarensis. Fine grained analysis of gait using the modern MEMS sensors found on all smartphones not just reveals a lot about the person’s orthopedic and neuromuscular health status, but also has enough idiosyncratic clues that it can be harnessed as a passive biometric. While there were many siloed attempts made by the machine learning community to model Bipedal Gait sensor data, these were done with small datasets oft collected in restricted academic environs. In this talk, we will introduce the ImageNet moment for human gait analysis by presenting 'Project GaitNet', the largest ever planet-sized motion sensor based human bipedal gait dataset ever curated. We’ll also present the associated state-of-the-art results in classifying humans harnessing novel deep neural architectures and the related success stories we have enjoyed in transfer-learning into disparate domains of human kinematics analysis.

Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...

MLconf

Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language Alzheimer's disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer's, using short samples of human speech. As an input to the model, features of different modalities are extracted from speech audio samples and transcriptions: (1) syntactic measures, such as e.g. production rules extracted from syntactic parse trees, (2) lexical measures, such as e.g. features of lexical richness and complexity and lexical norms, and (3) acoustic measures, such as e.g. standard Mel-frequency cepstral coefficients. I will present the ML model that detects cognitive impairment by reaching agreement among modalities. The resulting model is able to achieve state of the art performance in both supervised and semi-supervised manner, using manual transcripts of human speech. Additionally, I will discuss potential limitations of any fully-automated speech-based Alzheimer's disease detection model, focusing mostly on the analysis of the impact of a not-so-accurate automatic speech recognition (ASR) on the classification performance. To illustrate this, I will present the experiments with controlled amounts of artificially generated ASR errors and explain how the deletion errors affect Alzheimer's detection performance the most, due to their impact on the features of syntactic and lexical complexity.

Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...

MLconf

Optimized Image Classification on the Cheap In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will double the size of the dataset through image augmentation to boost the classifier’s performance. We will use Bayesian optimization to learn the hyperparameters associated with image transformations using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also focus on the features of these augmented images and the downstream implications for our image classifier. To both maximize model performance on a budget and explore the impact of optimization on these methods, we apply a particularly efficient implementation of Bayesian optimization to each of these architectures in this comparison. Our goal is to draw on a rigorous set of experimental results that can help us answer the question: how can resource-constrained teams make trade-offs between efficiency and effectiveness using pre-trained models?

Meghana Ravikumar - Optimized Image Classification on the Cheap

MLconf

The Importance of Modeling Data Collection Data sets used in machine learning are often collected in a systematically biased way - certain data points are more likely to be collected than others. We call this "observation bias". For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account for observation bias can, of course, result in poor predictions on new data. By contrast, properly accounting for this bias allows us to make better use of the data we do have. In this presentation, we discuss practical and theoretical approaches to dealing with observation bias. When the nature of the bias is known, there are simple adjustments we can make to nonparametric function estimation techniques, such as Gaussian Process models. We also discuss the scenario where the data collection model is unknown. In this case, there are steps we can take to estimate it from observed data. Finally, we demonstrate that having a small subset of data points that are known to be collected at random - that is, in an unbiased way - can vastly improve our ability to account for observation bias in the rest of the data set. My hope is that attendees of this presentation will be aware of the perils of observation bias in their own work, and be equipped with tools to address it.

Noam Finkelstein - The Importance of Modeling Data Collection

MLconf

The Uncanny Valley of ML Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we'll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.

June Andrews - The Uncanny Valley of ML

MLconf

Deep Learning Architectures for Semantic Relation Detection Tasks Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

MLconf

Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability. We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

MLconf

Vito Ostuni - The Voice: New Challenges in a Zero UI World The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query. We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.

Vito Ostuni - The Voice: New Challenges in a Zero UI World

MLconf

Anna choromanska - Data-driven Challenges in AI: Scale, Information Selection...

MLconf

Janani Kalyanam - Machine Learning to Detect Illegal Online Sales of Prescrip...

MLconf

Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection o...

MLconf

Neel Sundaresan - Teaching a machine to code

MLconf

Rishabh Mehrotra - Recommendations in a Marketplace: Personalizing Explainabl...

MLconf

Soumith Chintala - Increasing the Impact of AI Through Better Software

MLconf

Roy Lowrance - Predicting Bond Prices: Regime Changes

MLconf

More from MLconf (20)

Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...

Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...

Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush

Josh Wills - Data Labeling as Religious Experience

Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...

Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...

Meghana Ravikumar - Optimized Image Classification on the Cheap

Noam Finkelstein - The Importance of Modeling Data Collection

June Andrews - The Uncanny Valley of ML

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

Vito Ostuni - The Voice: New Challenges in a Zero UI World

Anna choromanska - Data-driven Challenges in AI: Scale, Information Selection...

Janani Kalyanam - Machine Learning to Detect Illegal Online Sales of Prescrip...

Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection o...

Neel Sundaresan - Teaching a machine to code

Rishabh Mehrotra - Recommendations in a Marketplace: Personalizing Explainabl...

Soumith Chintala - Increasing the Impact of AI Through Better Software

Roy Lowrance - Predicting Bond Prices: Regime Changes

Recently uploaded

IoT Analytics Company Presentation May 2024

IoTAnalytics

This talk offers actionable insights at an executive level for enhancing productivity and refining your portfolio management approach to propel your organization to greater heights. Key Points Covered: 1. Experience Transformation: - The core challenge remains consistent across organizations: converting budget into user-centric designs. - Strategies for deploying design resources effectively in both startups and large enterprises. 2. Strategic Frameworks: - Introduction to the "Ziggurat of Impact" model, detailing layers from basic system interactions to comprehensive customer experiences. - Practical insights on creating frameworks that scale with organizational complexity. 3. Organizational Impact: - Real-world examples of navigating design in large settings, focusing on the synthesis of consumer products and customer experiences. - Emphasis on the importance of designing systems that directly influence customer interactions. 4. Design Execution: - Detailed walkthrough of organizational layers affecting design execution, from touchpoints and customer activities to shared capabilities. - How to ensure design influences both the micro and macro aspects of customer interactions. 5. Measurement and Adaptation: - Techniques for measuring the impact of design decisions and adapting strategies based on data-driven insights. - The critical role of continuous improvement and feedback in refining customer experiences.

Structuring Teams and Portfolios for Success

UXDXConf

Intrigued by why some of the world's largest companies (Netflix, Google, Cisco, Twitter, Uber etc) are using gRPC? In this demo based talk we delve into the world of gRPC in .Net, what it does and why we should use it. We compare the interface with both Rest and graphQL. We will show you how to implement grpc server-side in .net and in the web. Finally, I will show you how the tooling helps you deliver powerful interfaces and interact with them quickly and simply.

Demystifying gRPC in .Net by John Staveley

John Staveley

ScyllaDB has the potential to deliver impressive performance and scalability. The better you understand how it works, the more you can squeeze out of it. But before you squeeze, make sure you know what to monitor! Watch our experienced Postgres developer work through monitoring and performance strategies that help him understand what mistakes he’s made moving to NoSQL. And learn with him as our database performance expert offers friendly guidance on how to use monitoring and performance tuning to get his sample Rust application on the right track. This webinar focuses on using monitoring and performance tuning to discover and correct mistakes that commonly occur when developers move from SQL to NoSQL. For example: - Common issues getting up and running with the monitoring stack - Using the CQL optimizations dashboard - Common issues causing high latency in a node - Common issues causing replica imbalance - What a healthy system looks like in terms of memory - Key metrics to keep an eye on This isn’t “Death-by-Powerpoint.” We’ll walk through problems encountered while migrating a real application from Postgres to ScyllaDB – and try to fix them live as well.

Optimizing NoSQL Performance Through Observability

ScyllaDB

Extensible Python: Robustness through Addition - PyCon 2024

Patrick Viafore

IESVE for Early Stage Design and Planning

IES VE

Oauth 2.0 Introduction and Flows with MuleSoft

shyamraj55

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

FIDO Alliance

WSO2CONMay2024OpenSourceConferenceDebrief.pptx

Jennifer Lim

The Metaverse: Are We There Yet?

Mark Billinghurst

Explore the core of Salesforce success in 'Salesforce Adoption – Metrics, Methods, and Motivation.' We will discuss essential metrics, effective methods to drive adoption, and the driving force behind user engagement and explore strategies for onboarding, training, and continuous support that empower users to navigate the platform seamlessly. By leveraging these tools, you can effectively measure adoption against your company’s goals and create an environment where users not only adopt Salesforce but actively contribute to its ongoing success.

Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom

CzechDreamin

Speed Wins: From Kafka to APIs in Minutes

confluent

Automation is transforming industries by streamlining operations and reducing costs. However, the success of automated systems hinges not only on their functionality but also on the user experience (UX). This talk, "The UX of Automated Processes," delves into the intricate relationship between automation and UX, providing a comprehensive guide to designing intuitive and effective automated systems. Key Topics Covered: 1. Introduction to Automated UX: * Understanding the importance of UX in automated systems * Real-world example: Robotic beer dispensers at a music festival 2. Step-by-Step Process for Enhancing Automated UX: * Step 0: Identifying main workflows and happy paths * Step 1: Conducting contextual inquiries to understand current user practices and edge cases * Step 2: Prioritizing issues and solutions through collaborative ideation sessions * Step 3: Prototyping and usability testing for both software and hardware 3. Prototyping and Testing: * Methods for simulating automated processes * Importance of user feedback in refining automated systems 4. Designing for Support Users: * Crafting the experience for support personnel who manage edge cases * Ensuring effective communication and issue prioritization for support users

The UX of Automation by AJ King, Senior UX Researcher, Ocado

UXDXConf

Join me in this session where I'll share our journey of building a fully serverless application that flawlessly managed check-ins for an event with a staggering 80 thousand registrations. We'll dive into three key strategies that made this possible. Firstly, by harnessing DynamoDB global tables, we ensured global service availability and data replication across regions, boosting performance and disaster recovery. Next, we'll explore how we seamlessly integrated real-time updates into the app using Appsync subscriptions, making the experience dynamic and engaging for users. Finally, I'll discuss how provisioned concurrency not only improved performance but also kept costs in check, highlighting the cost-effectiveness of serverless architectures. Through these strategies and the inherent scalability of serverless technology, our application effortlessly handled massive user loads without manual intervention. This session is a real world example to the power and efficiency of modern cloud-based solutions in enabling seamless scalability and robust performance with Serverless

How we scaled to 80K users by doing nothing!.pdf

Srushith Repakula

AI presentation and introduction - Retrieval Augmented Generation RAG 101

vincent683379

WebAssembly is Key to Better LLM Performance

Samy Fodil

This talk focuses on the practical aspects of integrating various telephony systems with Salesforce, drawing on examples from implementations in the Czech scene. It aims to inform attendees about the spectrum of telephony solutions available, from small to large scale, and their compatibility with Salesforce. The presentation will highlight key considerations for selecting a telephony provider that integrates smoothly with Salesforce, including important questions to support the decision-making process. It will also discuss methods for integrating existing telephony systems with Salesforce, aimed at companies contemplating or in the process of adopting this CRM platform. The discussion is designed to provide a straightforward overview of the steps and considerations involved in telephony and Salesforce integration, with an emphasis on functionality, compatibility, and the practical experiences of Czech companies.

Integrating Telephony Systems with Salesforce: Insights and Considerations, B...

CzechDreamin

Where to Learn More About FDO _ Richard at FIDO Alliance.pdf

FIDO Alliance

The presentation underscores the strategic advantage of treating design systems not just as technical assets but as vital business components that require thoughtful management, robust planning, and strategic alignment with organizational goals. Key Points Covered: - Understanding Design Systems as Business Entities: Conceptualizing design systems as internal business entities can streamline their integration and evolution within a company. - Adoption and Expansion: Elaborating on the importance of tactical adoption across organizational structures, enhancing product suites to cater to user needs and broadening scope to mobile and content authoring solutions. - Data-Driven Development: Utilizing data insights for component development ensures that resources are allocated to create valuable, widely used features. - Financial Modeling for Design Systems: Developing sustainable funding models is crucial for long-term support and success of design systems. - Promoting Internal Buy-In: Stressing on strategies for promoting design systems within the organization to increase engagement and investment from internal stakeholders.

A Business-Centric Approach to Design System Strategy

UXDXConf

Ever caught yourself nodding along when someone mentions "delivering value" in Agile, but secretly wondering what the heck they actually mean? You're not alone! Join us for an eye-opening session where we'll strip away the buzzwords and dive into the heart of Agile—value delivery. But what is "value"? Is it a mythical unicorn in the world of software development, or is there more to this overused term? This isn't going to be a sit-and-get lecture. We're talking about a face-to-face, interactive meetup where YOU play a crucial role. Come along to: Define It: What does "value" really mean? We’ll build a definition that’s not just words, but a compass for your Agile journey. Contextualise It: Discover what value means specifically to you, your team, your company, and your industry. Because one size does not fit all. Deliver It: Share strategies and gather new ones for uncovering and delivering true value—no more shooting in the dark!

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

David Michel

Recently uploaded (20)

IoT Analytics Company Presentation May 2024

Structuring Teams and Portfolios for Success

Demystifying gRPC in .Net by John Staveley

Optimizing NoSQL Performance Through Observability

Extensible Python: Robustness through Addition - PyCon 2024

IESVE for Early Stage Design and Planning

Oauth 2.0 Introduction and Flows with MuleSoft

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...

WSO2CONMay2024OpenSourceConferenceDebrief.pptx

The Metaverse: Are We There Yet?

Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom

Speed Wins: From Kafka to APIs in Minutes

The UX of Automation by AJ King, Senior UX Researcher, Ocado

How we scaled to 80K users by doing nothing!.pdf

AI presentation and introduction - Retrieval Augmented Generation RAG 101

WebAssembly is Key to Better LLM Performance

Integrating Telephony Systems with Salesforce: Insights and Considerations, B...

Where to Learn More About FDO _ Richard at FIDO Alliance.pdf

A Business-Centric Approach to Design System Strategy

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

Greg Werner, CEO & Founder, 3Blades.io at MLconf ATL 2017

1. Data Science with Teams Improve the efficiency of your data science teams with platforms that enhance collaboration and flexibility

2. Agenda ● Some Background ● Goals ● Data Science Project Teams ● Challenges ● Some Solutions ● Conclusions

3. Background Integration experience with Oil & Gas, Financial, Insurance and Retail industries in multiple geographies What did these customers have in common? All had data science teams that worked in Silos Difficulties when taking a data science course Source: Wikipedia

4. Background (cont) What’s going on here? We started to do some digging! Source: http://cesarsway.com

5. Data Teams - The Old Way Department Teams Data Scientist Data Analyst IT Manager

6. The Analytics Deliverable A dashboard! An interactive dashboard is even cooler.

7. Conway’s Data Scientist Venn Diagram

8. Data Science Teams - The New Way Data ScientistFinance Manager Accountant Tax and Compliance Treasury Data Gurus: - Analytics - Data Engineers - Business Intelligence - Compliance IT Manager

9. The Data Science Deliverable A machine or deep learning model!

10. I Want GPUs - And I Just Want Them to Work Work around for NVidia Docker Wrapper: - nvidia-docker -d -p 8888:8888 tensorflow/tensorflow:latest-gpu OR - docker run -ti --rm `curl -s http://localhost:3476/docker/cli` tensorflow/tensorflow:latest-gpu OR - docker run -ti --rm --volume-driver=nvidia-docker -- volume=nvidia_driver_375.82:/usr/local/nvidia:ro --device=/dev/nvidiactl -- device=/dev/nvidia-uvm --device=/dev/nvidia0 nvidia/cuda nvidia-smi

11. The Need for DevOps Chops Registrator Docker Container EC2 Instance Reverse Proxy with consul-template The old way... The new way... Docker Container EC2 Instance Reverse Proxy with static upstream location name $$$ $

12. Infrastructure Management Uh-oh, someone has to manage this stuff!

13. Our Architecture - API First and Microservices

14. 3Blades Hub for Data Scientists

15. Solutions Provide flexibility with the tools that data scientists use for exploratory data analysis and visualizations One central source for project files with support for version control Share visualizations from EDA Train and save Machine Learning and Deep Learning models with multiple frameworks, from within the same project Streamline deployment pipelines

16. Thank You!! Email: hello@3blades.io Web: https://3blades.io Twitter: @3bladesio GitHub: https://github.com/3blades Email: gwerner@3blades.io Twitter: @gwerner LinkedIn: https://www.linkedin.com/in/wernergreg GitHub: https://github.com/jgwerner

Editor's Notes

Talking points: Siloed data initiatives is a common denominator Data scientists were segregated from the rest of the organization Tooling was disparate Initially, the need to streamline Jupyter Notebook deployments with a class of students came up after many students were complaining about the time and effort involved in using specific dependencies to complete their tasks. Package managers were not enough: users also needed an integrated solution to access a consistent and reliable solution to complete their assignments using Jupyter Notebooks. We also noticed that companies, in general, did not provide a homogeneous environment for their data science teams. This led to many headaches but was considered business as usual.
Talking points: Issues encountered with the education vertical were common across industry, i.e. too much time spent con configuration Basic ROI calculations justified the implementation and support of a data science hub Data science platform “a ha” moment came when pitching a solution to consolidate project workspace environments for different people across different organizations, in particular for Exploratory Data Analysis (EDA). Educational institutions are usually constrained by budget requirements, however, after providing ROI numbers on how much time and effort Teachers Assistants (TA’s) spent on providing technical support for their users, the decision to implement a data science platform was a no brainer. Nevertheless, we had the suspicion that the enterprise (SMB’s and large enterprises alike) were encountering the same challenges but were exacerbated due to the fact that more personas were involved within data science and analytics teams.
Talking points: Disparate teams Data scientists siloed from the rest of the organization Ultimate goal is to automate certain processes within the organization Automation helps improve the top and bottom lines, improves competitiveness Organizations struggle to become ‘data driven’. What does that mean? Data driven organizations are those that wish to use the data they have available to improve insights and allow their business to become more competitive. Assuming the organization has successfully consolidated their data into central data warehouses or data lakes, and assuming this data is defined with standard schemas, data science and data analytics teams have the power to analyze the data, obtain valuable insights and start improving the agility of their organizations with ‘prescriptive analytics’ and ‘predictive analytics’. Prescriptive analytics involves creating machine learning and deep learning models that automate certain business processes, such as: Automatically tag images with classification types (cat or not a cat) Automatically classifying a customer with the probability that the customer will churn Recommendations for value added products to improve the checkout dollar amount at an e-commerce site Spam or not spam However, organizations have struggled to integrate data scientists into their organizations. Data science teams that just ‘do the math’ and create visualizations on an organization's data sets to not provide much value in and of itself. Creating a machine learning model that automatically recommends a product that is not strategic to the organization does not provide much value.
Talking points: Dashboards democratize data so that team members can quickly absorb meaningful insights and key performance indicators. Exploratory data analysis (EDA) and model creation/deployment not really a part of the picture. Traditional Business Intelligence tools have been around for years. Some tools offer specific integrations into a variety of data sources and allow users to quickly create rich and interactive visualizations of their data. SQL, a language made popular by relational databases, is a very popular language for analytics. New developments help accelerate the time from data source to dashboard with in memory calculations, GPU powered databases, among others. Big data tools, such as Hadoop and Spark allow users to create dashboards from large data sets. However, BI tools rely traditionally on structured data. Also, traditional dashboards don’t take into account how to create machine learning and deep learning models.
Just a review of a Data Scientist’s skill set.
Talking points: Organizations realize they need to automate their processes and that automation must come from real time analysis of data points The deliverable is not just a BI dashboard anymore, the deliverable is a deployable machine learning and deep learning model Embedding a data science team member into the group increases value As mentioned, historically data science teams have been isolated from the rest of the organization. Successful data driven organizations embed their data scientists into various business groups. For example: data extraction and loading into a warehouse table are done by engineering teams, however, a data science liaison, embedded within a certain department or relevant company wide project, can help data engineers improve the schema definition for the data being exposed which could save valuable time during the exploratory data analysis phase. Data engineers can create tables using their favorite Extract Transform and Load (ETL) tools to remove not-a-number (NaN) rows, remove columns that are irrelevant such as data base PK/FK’s, etc. Inversely, the data scientist could help the person telling the data story (could be anyone in the group, including herself) what features are relevant, how the certain normalizations were completed without delving into the technical details, etc. “This was the only customer that bought a widget in Atlanta so the attributes for this person were adjusted to not skew the dataset in their favor”.
Talking points: Move from prescriptive to predictive analytics Deliver a machine learning or deep learning model that will allow organizations to automate processes Visualizations are still important, but used for telling a data story for EDA and also for visualizing how models are behaving in real time Predictive analytics looks at the historical trends in data to provide insights. Organization members are then tasked to optimize processes to improve organizational results based on trends. However, companies need to automate tasks (remove the human from the actual task execution) based on certain indicators. In this case, visualizations are used in EDA to better understand the data with the goal of creating and deploying machine learning and deep learning models that can automate certain organization processes.
Talking points: Support data source imports from multiple sources EDA needed as first step to build and deliver artifacts to automate business processes. Artifacts in this context are machine learning and deep learning models. Data engineers and DevOps need access to data science hub to streamline their own processes Traditional teams use Excel spread sheets, among other tools, and are flying back and forth with emails, chat applications or external project management solutions. Even if all users work within shared environments such as Google Docs or Office 365, teams had no way of sharing all files and tools within one common environment, particularly for exploratory data analysis, since viewing and editing files within these environments are constrained to a certain set of file formats. Nevertheless, certain organizations and individuals prefer one language over the other. For example, a data science team involved with the Finance department may be more involved with using the R programming language, and the data science team involved with the marketing department may be more involved with the Python programming environment. In both cases, users may use multiple tools for one language. For example, some individuals may prefer RStudio for R, and others amy prefer using R with Jupyter Notebooks. Server management is important to optimize compute resources.
Traditional teams use Excel spread sheets, among other tools, and are flying back and forth with emails, chat applications or external project management solutions. Even if all users work within shared environments such as Google Docs or Office 365, teams had no way of sharing all files and tools within one common environment, particularly for exploratory data analysis, since viewing and editing files within these environments are constrained to a certain set of file formats. Nevertheless, certain organizations and individuals prefer one language over the other. For example, a data science team involved with the Finance department may be more involved with using the R programming language, and the data science team involved with the marketing department may be more involved with the Python programming environment. In both cases, users may use multiple tools for one language. For example, some individuals may prefer RStudio for R, and others amy prefer using R with Jupyter Notebooks. A central source for project files alleviates compliance requirements. Usually, data engineers (either due to security requirements or simply that they don’t want to surface multiple schemas/formats for different users) would rather deliver the data product to a ‘clean’ table, so data scientists can do their work using self service approaches. Having a centrally managed set of files for specific projects also helps keeps things organized when different users are accessing project files, so version control becomes important as well.

Greg Werner, CEO & Founder, 3Blades.io at MLconf ATL 2017

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (11)

More from MLconf

More from MLconf (20)

Recently uploaded

Recently uploaded (20)

Greg Werner, CEO & Founder, 3Blades.io at MLconf ATL 2017

Editor's Notes