ODSC East 2020

Training | Machine Learning | ML for Programmers | Advanced

scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some of the more advanced aspects of scikit-learn, such as building complex machine learning pipelines and advanced model evaluation. Model evaluation is an underappreciated aspect of machine learning, but using the right metric to measure success is critical. Practitioners are often faced with imbalanced classification tasks, where accuracy can be uninformative or misleading. We will discuss other metrics, when to use them, and how to compute them with scikit-learn. We will also look into how to build processing pipelines using scikit-learn, to chain multiple preprocessing techniques together with supervised models, and how to tune complex pipelines…more details

Training | Machine Learning | MLOps & Data Engineering | Beginner-Intermediate

In this half-day hands-on training, we will use free-tier resources in the Google Cloud Platform (GCP) to introduce learners to the practical use of cloud computing resources in data science and machine learning. Learners should have some experience with data analytics, data science or machine learning. Learners should also have a Gmail account with no former GCP use associated with it, or be willing to create such an account. While fluency in R or Python will be very helpful, it is not rigorously required, as well-annotated scripts will be provided. No previous exposure to or use of cloud computing is required; this is introductory-level in terms of its cloud computing assumptions. This training will be useful for those considering cloud adoption, interested in data engineering, or interested in working with public data as citizen scientists…more details

Bootcamp | Kickstarter | Open-source | Beginner

By completing this workshop, you will develop an understanding of relational models of data, how SQL is used to retrieve that data, and how to join tables, aggregate information, and answer data science questions. You will also become familiar with many of the common types of SQL databases, how to access information in a database from the command line, and how to integrate database access from within Python.

Lesson 1: Relational Databases and Foundational SQL
Familiarize yourself with relational databases and the SQL syntax necessary to retrieve information from tables in a database. At the end of this lesson, you will be able to comfortably explore a database and retrieve filter, and sort information from a table…more details

Training | Kickstarter | Machine Learning | Beginner

Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detect the Higgs boson, machine learning excels at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to “learn” a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formulating a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. This workshop should give you all the necessary background to start using machine learning yourself…more details

Training | Kick-starter | Machine Learning | All Levels

Linear regression is the foundation of supervised learning, though it has its limits. During this workshop we extend regression using penalization for automated variable selection and increased flexibility. We then introduce trees, and in particular boosted trees, via xgboost to get incredibly powerful predictions. We will go over some of the theory and also practical considerations such as hyperparameters…more details

Full-Day Training | Kick-starter | Open-source | Intermediate

Whether in R, MATLAB, Stata, or Python, modern data analysis, for many researchers, requires some kind of programming. The preponderance of tools and specialized languages for data analysis suggests that general purpose programming languages like C and Java do not readily address the needs of data scientists; something more is needed.

In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for interactive data analysis. Pandas is a massive library, so we will focus on its core functionality, specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals of Pandas, be aware of common pitfalls, and be ready to perform your own analyses…more details

Training | Open-source | Kick-starter | Beginner

Recommendation systems are fundamental to essentially every business on the planet. From providing the shows and movies you should watch, the books and articles you should read, the products you should purchase, or the people you should date making good recommendations can make or break your business. This workshop will give a general introduction to recommendation systems, as well as introduce practical tools for you to apply recommendation systems to your own use cases. Though the primary focus of this workshop will be to understand the different methods you might use to make recommendations, we will also touch briefly on common difficulties with building effective recommendation systems, how to deploy your recommendations, and how to evaluate whether your recommendations are effective…more details

Training | Machine Learning | R-programming | All Levels

Temporal data requires special care to model as it violates several principles of standard machine learning models. R has long had top-of-the-line forecasting tools, though recently new ones have been developed which greatly ease working with time series data. We use the tsibble package for manipulating time series data, feasts for visualization , and fable for building forecasting models such as ETS and ARIMA…more details

Training | MLOps & Data Engineering | ML for Programmers | Beginner-Intermediate

By attending this workshop, you will learn how you can become a contributor to Apache Airflow project. You will learn how to setup development environment, how to pick your first issue, how to communicate effectively within the community and how to make your first PR – experienced committers of Apache Airflow project will give you step-by-step instructions and will guide you in the process. When you finish the workshop you will be equipped with everything that is needed to make further contributions to the Apache Airflow project.

Lesson 1: Development environment

Learn how to setup and configure Apache Airflow development environment in less than 10 minutes. You will learn how modern tooling is used to provide consistent, reproducible environment for making contributions for distributed contributors of Apache Airflow. You will learn how to run tests and debug the code, and make sure that the tests are passing in the Continuous Integration environment as well as locally…more details

Training | Open-source | Intermediate

This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes’ rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression. By the end of this tutorial, you will be equipped with a solid grounding in Bayesian inference, able to write arbitrary models, and have experienced basic model checking workflow…more details

Training | Machine Learning | Intermediate

We will present at least 10 examples and suggested solutions of cold-start problems (i.e., that move from a bad initial random guess to a good, perhaps optimal, solution), covering a variety of different algorithms and applications, focused primarily on unsupervised learning, but with some supervised learning examples also. We will also introduce related concepts and their importance, including the objective function, genetic algorithms, backpropagation, gradient descent, and meta-learning. Those concepts represent the true keys that unlock performance in a cold-start challenge. Those are the magic ingredients in most of the examples that we will present. At the end of the workshop, you should be empowered, enabled, and emboldened to tackle similar machine learning challenges problems in other domains. After all, data are data, math is math, and good experience is transferable!..more details

Training | Deep Learning | Kick-starter | Beginner

This session will focus on the foundational concepts of neural networks and deep learning. Concrete examples will be used to illustrate abstract concepts and methods. Participating in this workshop you will gain an intuition for the principles underlying modern neural networks including multilayer perceptrons, autoencoders, convolutional neural networks, and recurrent neural networks including long short-term memory. No prior experience with neural networks or machine learning required…more details

Training | Data Visualization | Machine Learning | Intermediate

If you’ve never heard of the “good, fast, cheap” dilemma, it goes something like this: You can have something good and fast, but it won’t be cheap. You can have something good and cheap, but it won’t be fast. You can have something fast and cheap, but it won’t be good. In short, you can pick two of the three but you can’t have all three.

If you’ve done a data science problem before, I can all but guarantee that you’ve run into missing data. How do we handle it? Well, we can avoid, ignore, or try to account for missing data. The problem is, none of these strategies are good, fast, *and* cheap.

We’ll start by visualizing missing data and identify the three different types of missing data, which will allow us to see how they affect whether we should avoid, ignore, or account for the missing data. We will walk through the advantages and disadvantages of each approach as well as how to visualize and implement each approach. We’ll wrap up with practical tips for working with missing data and recommendations for integrating it with your workflow!..more details

Workshop | MLOps & Data Engineering | ML for Programmers | Intermediate

This session is a hands on workshop (with coding) to demonstrate how to gain observability (monitoring & alerting) for production machine learning pipelines. We will provide background on why observability is important to run successful MLOps, then walk through in detail how to set up a robust observability system.

The session will cover the end-to-end process, from data prep jobs running in Airflow, to model development and experimentation in Jupyter notebooks, to model serving in production.

The content covered will be of interest to data engineers and data scientists, including anyone who is working on machine learning projects.

We recommend that participants have strong backgrounds in python and at least high level knowledge of job orchestrators like Airflow, which are used to run automated data pipelines…more details

Full-Day Training | Deep Learning | Machine Learning | Beginner-Intermediate

In this workshop we will explore Reinforcement Learning, starting from its fundamentals and ending creating our own algorithms. We will use OpenAI gym to try our RL algorithms. OpenAI is a non profit organization that want committed to open source all their research on Artificial Intelligence. To foster innovation OpenAI created a virtual environment, OpenAi gym, where it’s easy to test Reinforcement Learning algorithms. We then will also explore other RL frameworks and more complex concepts like Policy gradients methods and Deep Reinforcement learning, which recently changed the field of Reinforcement Learning. In particular, we will see Actor-Critic models and Proximal Policy Optimizations that allowed OpenAI to beat some of the best Dota players…more details

Training | Data Visualization | Machine Learning | Beginner-Intermediate

Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this 3.5 hr tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.

Upon completing this tutorial, you will be:
– familiar with how to use the NetworkX and nxviz Python packages for modeling and rationally visualizing networks,
– able to load node and edge data from a Pandas data frame,
– familiar with object-oriented and matrix-oriented representations of graphs,
able to find paths between nodes, interesting structures in graphs, and projections of bipartite graphs.
– (if time permits) able to use matrix operations to simulate the diffusion of information on networks…more details

Full-Day Training | MLOps & Data Engineering | Machine Learning | Intermediate-Advanced

Machine Learning and Graph Processing (e.g., Knowledge Graphs) have been two of the main trends over the past years.
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization and other NLP tasks. There are even more applications once we consider data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines. In this tutorial, we will consider the symbiosis of graphs and Machine Learning. We will start by looking at popular graph algorithm and their value for feature engineering for Machine Learning models. We know about the value of high quality and quantity for building high-quality machine learning models, but for operating a production-grade machine learning pipeline Metadata is equally important. In this part, we will look at leveraging graphs to capture metadata and provenance information of our machine learning ecosystem…more details

Training | Data Visualization | Kick-starter | All Levels

In this complete hands-on training session, you will learn to turn your data into interactive dashboards, how to create stories with data and share these dashboards with your audience. We will begin with a quick refresher of basics about design and information literacy and discussions about practices for creating charts and storytelling utilizing best visual practices. Whether your goal is to explain an insight or let your audience explore data insights, using Tableau’s simple drag-and-drop user interface makes the task easy and enjoyable.
In this session, we’ll cover intermediate and advanced tableau functionality:
1. Database connectivity
2. Blending and joins using multiple data sources, web connectors and plugins,
3. Perform data analyses and create graphs from a real-world dataset, using Tableau Public (free to use)
4. Deeper Analysis – Trends, Clustering, Distributions, and Forecasting
5. Table Calculations, Sets, Filters, Level of Detail expressions, Parameters
6. Spatial analytics – Using Maps and Geocoding
7. Right and Wrong way to build Dashboards and Best Practices…more details 

Training | MLOps & Data Engineering | Machine Learning | Beginner-Intermediate

ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To solve these challenges, MLflow, an open-source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size…more details

Workshop | ML for Programmers | Kick-starter | Beginner-Intermediate

When building a data science team it is important to document and record work flows in both R and python. A data science department can use installable git controlled packages to form a foundation of code and best practices. In addition, libraries dramatically cut down the time and effort required for a team to bring work to production. Additionally, package development is the first step for engineers and scientists aiming to contribute their unique ideas back to the community.

This workshop aims to teach the basics of package development in both R and python in 90 minutes. We will touch upon why a data science team should strive to be fully fluent in both languages. We will show simple R package development and simple Python package development. Finally, we will demonstrate how one can use an open source package to test the interface similarity between R and Python packages designed to support identical workflows…more details

Training | Deep Learning | Machine Learning | Beginner-Intermediate

Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing. This Deep Learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2, the major, cutting-edge revision of the world’s most popular Deep Learning library. To facilitate an intuitive understanding of Deep Learning’s artificial-neural-network foundations, the essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on Python code run-throughs provided in straightforward Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art Deep Learning models…more details

Training | Open-source | Kick-starter | Beginner

Data Science and Machine learning have been synonymous with languages like Python. Libraries like Numpy and Pandas have become the de facto standard when working with data. The DataFrame object provided by Pandas gives us the ability to work with heterogeneous unstructured data that is commonly used in “real world” data. New learners are often drawn to Python and Pandas because of all the different and exciting types of models and insights the language can do and provide but are awestruck when faced with the initial learning curve. By the end of this tutorial, you should have a solid foundation in working with datasets in Python. The last topic of encoding dummy variables segues into using other libraries, such as scikit-learn and stats models to fit models on your data…more details

Tutorial

We discuss the core abstractions of ParlAI, including Agents, Worlds, and Messages, and how these allow unified presentation of model training, model evaluation, and model deployment. We highlight the most important models and features, including support for general chit chat, image commenting, and seamless integration with Amazon Mechanical Turk. We show how you can start training a specialized using GPT-2 with just a single command.

We briefly highlight some of our recent advances in dialogue research with deep learning, including how we are teaching chatbots to be personable, knowledgeable, and empathetic. We discuss our methods for robustly evaluating the quality of research chatbots, how we teach models to respond safely. We show off some of our recently deployed research bots, which you can talk to today!

This tutorial is targeted at a technical audience without an existing background in dialogue research. Familiarity with deep learning is recommended, but not required, for the second half of the talk…more details

Tutorial | NLP | Machine Learning | Intermediate

David Talby presents the open-source Spark NLP package for training distributed custom natural language machine-learned pipelines on Apache Spark. The library natively extends Spark ML and includes state-of-the-art deep learning models, language models, and 30+ pre-trained NLP models. The talk walks through the library’s goals, design and API’s, using Jupyter notebooks that will be made publicly available after the talk. Best practices and industry use cases where the library has been applied will be discussed as well…more details

Training | R-programming | Data Visualization | Advanced

In this interactive workshop, you will make ggplot2 data visualizations clearer and more communicative. You’ll learn to declutter plots using highlighting, direct labeling, and by designing figures with better legends (or no legends at all). To make beautiful, expressive plots, we’ll view strategies you already know, like faceting, from a design perspective; hack geoms, legends, and the plot itself; and learn extension packages that let us do incredible things, like cowplot, gghighlight, and ggrepel. Along the way, we’ll take advantage of useful tools for working with color. We’ll also explore storytelling with data by annotating plots and combining plots with the patchwork package in a way that builds your narrative…more details

Workshop | MLOps & Data Engineering | Machine Learning | Beginner-Intermediate

The speed in which intelligent decision making can take place is changing the very fabric of the modern industry. From autonomous routing decisions for real-time traffic avoidance to intelligent integrated systems for customer service and assisted analysis or forecasting, the time to correct decision can be a huge differentiator. During the course of this workshop, you will learn how to harness the power of Apache Spark to build up a data ingestion pipeline that can process and learn from streaming data. These learnings can then be applied to make simple decisions using SparkSQL in a parallel streaming system…more details

Training | Machine Learning | Open-source | Beginner-Intermediate

The rapid progress in machine learning (ML) and the massive increase in the availability and diversity of data has enabled novel approaches to quantitative investment. It has also increased the demand for the application of data science to develop both discretionary and algorithmic trading strategies.
In this workshop, we will cover popular use cases for ML in the investment industry, and how data science and ML fit into the workflow of developing a trading and investment strategy from the identification and combination of alpha factors to strategy backtesting and asset allocation.
The workshop uses Python and various standard data science and machine learning libraries like pandas, scikit-learn, gensim, spaCy as well as TensorFlow and Keras. The code examples will be presented using jupyter notebooks and are based on my book ‘Machine Learning for Algorithmic Trading’…more details

Training | Machine Learning | Intermediate

How can an idea from an 18th-century Presbyterian minister be used to estimate the mass density function of galaxies across the Universe? How can a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a Formula 1 race from then 1950’s inspire one of the greatest data science use cases for the Internet of Things? How can a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? This workshop will answer these questions, and more, by presenting several examples of one of the key aptitudes of successful data science practice, which is adaptability. In particular, I will present several well-known algorithms (including some that we would not even call “algorithms”) that may have been adopted for specific use cases or applied in specific business domains, and then I will show how each one can be adapted to a novel use case that may be less obvious, perhaps producing significantly surprising results in some other domain. Plus, I will suggest some new opportunities that may come from interesting combinations of data and algorithms. The point of these exercises is to demonstrate how data scientists can create even more value, beyond that which is expected, from our data assets and our algorithmic talents…more details

Training | Machine Learning | Intermediate

An ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Often ML applications are developed using tools and systems which suffer from inherent limitations in testability, scalability across clusters, training/serving skew, and the modularity and reusability of components. In addition, ML application measurement often emphasizes top level metrics, leading to issues in model fairness as well as predictive performance across user segments.

We discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX), as well as the advantages of containerizing pipeline architectures using platforms such as Kubeflow. Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology including testability, hot versioning, and deep performance analysis…more details

Training | Machine Learning | ML for Programmers | Advanced

scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some advanced topics in using scikit-learn and how to build your own models or feature extraction methods that are compatible with scikit-learn. We will also discuss different approaches to feature selection and resampling methods for imbalanced data. Finally, we’ll discuss how to do the classification of text data using the bag-of-words model and its variants…more details

Training | Machine Learning | R-programming | All Levels

After we build various machine learning models we need to make them accessible to others. We use modeldb so we can store models in a database and plumber to expose our model as a REST API that can be hosted in a Docker container…more details

Half-Day Training | NLP | Deep Learning | Intermediate

Natural language processing (NLP) has become an important field with interest from many important sectors that leverage modern deep learning methods for approaching several NLP problems and tasks such as text summarization, question answering, and sentiment classification, to name a few. In this tutorial, we will introduce several of the fundamental NLP techniques and more modern approaches (BERT, GTP-2, etc.) and show how they can be applied via transfer learning to approach many real-world NLP problems. We will focus on how to build an NLP pipeline using several open-source tools such as spaCy and TensorFlow. Then we will learn how to use the NLP model to search over documents based on semantic relationships. We will use open-source technologies such as BERT and Elasticsearch for this segment to build a proof of concept. In essence, the learner will take away the important theoretical pieces needed to build practical NLP pipelines to address a wider variety of problems in the real world…more details

Training | Machine Learning | R-programming | All Levels

Modern machine learning is mostly brute forcing through a multitude of hyperparameters. We look at new tools for building and tuning models in R, some so new they are only available on GitHub. We use xgboost and glmnet models (learned in Part I) as motivation for learning how to split data and conduct cross-validation with rsample, perform feature engineering with recipes, build model specifications with parsnip, tune over hyperparameters with dials and tune, evaluate performance with yardstick and put it all together with workflows…more details

Training | NLP

Coming Soon!

Training | Computer Vision | Deep Learning | Intermediate

In this tutorial, we will introduce how to build an object detection model. Specifically, we will build an object detection model that identifies chess pieces (a custom dataset provided by the presenter). In doing so, participants will gain insight into the fundamentals of computer vision: structuring a good problem for object detection, dataset collection and annotation, data preparation through preprocessing, data augmentation to support a well-fit model, training a model, debugging a model’s fit, and using the model for inference.

We will introduce Keras and TensorFlow as our specific libraries for writing computer vision models and train a Yolov3 (You Only Look Once) model for real-time detection. Participants will leverage Colab for GPU compute.

Session Outline
– Introduction to Computer Computer Vision Problems
– Data Preparation
– Data Preprocessing and Augmentation
– Training Our Yolov3 Model
– Inference and Q&A…more details

Workshop | ML for Programmers | MLOps & Data Engineering | Intermediate-Advanced

Organizations have long used relational databases for a wide variety of data-intensive applications. Data scientists and analysts need to understand how to work with relational databases, particularly for common data science tasks, such as finding, exploring, analyzing and extracting data within a relational database. SQL is an expressive declarative language designed for working with tabular data. Since its inception, new features have been added that allow for complex expressions and computation, such as regular expressions, sliding window functions, and analytical operations that create non-relational results. Understanding the advanced features of SQL will help data scientists reduce the need for programming custom data manipulation functionality…more details

Training | Deep Learning | Beginner

Over the past couple of years, PyTorch has been increasing in popularity in the Deep Learning community. What was initially a tool used by Deep Learning researchers has been making headway in industry settings. In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner – understanding what is going on “under the hood”, coding the layers of our networks, and implementing backpropagation – to more advanced material on RNNs, CNNs, LSTMs, & GANs. Attendees will leave with a better understanding of the PyTorch framework. Furthermore, a link to a clean documented GitHub repo with the solutions of the examples covered will be provided…more details

Training | Machine Learning | ML for Programmers | Intermediate

scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This workshop will go beyond the basics and show how to effectively evaluate and tune algorithms. We will also discuss the most important machine learning algorithms that you’re likely to see in practice, how and when to use them, and some details about how they work internally. The session will focus on linear models for classification and regression and tree-based models, including random forests…more details

Training | MLOps & Data Engineering | Intermediate

Ray is an open-source distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications from a laptop to a cluster, with an emphasis on ML/AI systems, such as reinforcement learning. It is now used in many production deployments. In this tutorial, we’ll use several hands-on examples to explore the problems that Ray solves and the useful features it provides, such as rapid distribution, scheduling, and execution of “tasks” and management of distributed stateful “serverless” computing. We’ll see how it’s used in several ML libraries (and play with examples using those libraries). You’ll learn when to use Ray and how to use it in your projects…more details

Workshop | Deep Learning | Machine Learning | Advanced

Uncertainty is a critical consideration in any application of deep learning, particularly when safety is on the line. Conventional deep learning approaches lead to models that are overconfident, fragile under domain shift, helpless against adversarial attacks, and unable to convey their implicit understanding of uncertainty in data. This talk will survey methods for quantifying and handling different sources of uncertainty, with a focus on practical, scalable techniques for a variety of common use cases. From principled Bayesian approaches to post-hoc calibration, we’ll cover the theory, tools, techniques, and tips you need to better handle uncertainty in your own deep learning applications…more details

Training | Deep Learning | Intermediate-Advanced

In this session, we will focus on the application and practice of building a multi-layer neural network in Python code. We will walk through a lightweight neural network framework and discuss how the concepts from the morning’s session were reduced to code, including optimization, regularizerization, and backpropagation. We will step through a concrete example of building an autoencoder for image compression. Participants will have the option to download and run all of the neural network code on their own computers. No GPU required. Participants are also welcome to observe and absorb…more details

Training | Machine Learning | MLOps & Data Engineering | Intermediate-Advanced

In this workshop, you will learn the best techniques that can be applied manually or automatically to validate and monitor statistical models.
Techniques below will be discussed and demonstrated to perform a full model validation:
Techniques used for initial validation:
Bias and variance
Model selection
Hyperparameters selection
Model interpretation
Adversarial validation o
Techniques used for model monitoring
Bias and variance consistence
Model degradation
Model interpretation consistence…more details

Training | Open-source | MLOps & Data Engineering | Beginner-Intermediate

The intention of this VERY hands on workshop is to get you introduced and playing with some of the great features you never knew about in PostgreSQL. You know, and probably already love, PostgreSQL as your relational database. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will add some nice statistical work with R embedded in PostgreSQL. Finally, we will bring this all together using the gold standard in spatial databases, PostGIS. Unless you have a specialized use case, PostgreSQL is the answer. The session will be very hands on with plenty of interactive exercises.

This is a VERY hands on workshop introducing you to other great features in PostgreSQL to use with PostGIS. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will also work through R embedded in PostgreSQL...more details

Traning | Data Vizualization | ML for Programmers | Beginner-Intermediate

This workshop will take one through the steps associated with an end-to-end machine learning campaign: data retrieval; data curation; model construction, evaluation, selection and interpretation; and reporting. Particular attention will be paid to reporting, i.e., building a narrative. Examples will be presented demonstrating how one might generate multiple output formats (e.g., HTML pages, presentation slides, PDF documents) starting with the same code base.

The workshop will have three main foci:
1. infrastructure: instantiating the computational environment; loading packages; loading data
2. computation: data curation, transformation, and analysis; model construction, evaluation, and interpretation
3. communication: creating tables, charts, and graphs; weaving all components into data narrative…more details

Training | MLOps & Data Engineering | Beginner-Intermediate

Machine learning is usually taught from tutorials using small, clean datasets put into data-frames and orchestrated with Jupyter notebooks; all done in one, in-memory, local environment. This is a fine style for presenting a new topic and teaching the main ideas, but unfortunately, these patterns are not conducive to the delivery of real production applications at scale. In this workshop, we show how to maintain data science productivity as well as collaborate effectively and deliver value continuously and seamlessly. We demonstrate and guide the participants through CI/CD practices for machine learning and a new pattern of working that avoids most of the pitfalls of the typical approach.

Participants will learn how to utilize new patterns of repeatable continuous model development to collaborate effectively and deliver value continuously and seamlessly in industrial data science projects using Continuous Integration (CI) and Continuous Delivery (CD) practices….more details

Workshop

Text classification is one of the most commonly studied tasks in natural language processing. In addition to its theoretical importance, the classification task is widely used in various applications including spam detection, sentiment analysis, language identification.

FastText is an open-source library developed by Facebook AI Research (FAIR), that has the purpose of simplifying text classification.

As often, the “secret sauce” is in the details. This tutorial will give participants more insights about these details, helping them to understand the subtleties of the model…more details

Workshop | ML for Programmers | Open-source | Beginner-Intermediate

From a consumption perspective, we discuss how to work with the httr, curl, and jsonlite packages. We show examples of constructing API requests, submitting them, and working with the responses. We discuss tips and tricks to optimize the workflow.
to interface with web REST APIs for many purposes.

From a command & control perspective, we discuss several packages included in the cloudyr (http://cloudyr.github.io/) project, which allow you to manage and execute tasks in the cloud. We show examples of creating resources and augmenting your data using APIs.

Last, we discuss the plumber R package, which allows users to expose existing R code as a service available to others on the Web. We discuss how to take models and functions and expose them as APIs so others can use them…more details

Workshop | NLP | Deep Learning | Intermediate-Advanced

The speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization. He will also cover why and how NLP was used, what deep learning models and libraries were used, and what was achieved. Key takeaways for attendees will include important considerations for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger and scalable machine learning and deep learning pipelines in a distributed environment…more details

Talk | Machine Learning | MLOps & Data Engineering | Beginner-Intermediate

At LinkedIn, we have been working on the next generation of our data science platform as a service. This talk will describe our journey to build a centralized infrastructure platform that scales to hundreds of users, thousands of metrics and supports applications ranging from simple dashboarding to experimentation and anomaly detection. We will discuss how a combination of technology and processes has allowed us to scale our user base while preserving trust in our data and metrics. We will also cover in detail how our platform solves challenges such as:
(a) Efficiently managing operations of thousands of data pipelines, data dependencies and SLA’s
(b) Building and managing data constructs across streaming and batch
(c) Enforcing and maintaining data craftsmanship
(d) Preserving trust, governance, and consistency across data science ecosystem

Talk | Data for Good | Open Source | Beginner-Intermediate

I begin with a high-level introduction to differential privacy, a mathematical framework for analyzing and proving privacy guarantees. I introduce the notion of generative adversarial networks (GANs), which are used to create the synthetic data, and I illustrate how GANs are trained with a toy example using handwritten digits. I explain a small modification to the procedure for training GANs that ensures differential privacy.

I offer an implementation of a differentially private generative adversarial network (DPWGAN) using PyTorch. To illustrate how this method works, I apply the DPWGAN to ACS PUMS data, a collection of individual-level from the U.S. Census Bureau. I show that the DPWGAN models correlations in the data, and that cross tabs on the synthetic data are close to those in the original data. The PyTorch code is available and open source.

Participants will come away with an understanding of the basics of differential privacy and generative adversarial networks, as well as the ability to apply the DPWGAN code to their own data sets.

Workshop | MLOps & Data Engineering | Beginner

DevOps tools for getting code reliably to production have proven to be effective in the software engineering world. Today, ML Engineers are working at the intersection of data science and software engineering, and can leverage DevOps best practices to streamline their workflow and delivery process. This is what MLOps is all about.

At Manifold, we’ve developed processes to help ML engineers work as an an integrated part of your development and production teams, helping you to be deliberate, disciplined, and coordinated in your deployment process. In this workshop, Sourav and Alex will walk you through the some key learnings from using this Lean AI process. They’ll cover topics such as:
* Scaffolding a project with a consistent structure for ML
* Using Docker for a consistent runtime across the team and environments
* Cleanly separating ML configuration from ML source code
* Setting up an ML experiment tracking system
* Systems for rapid ML experimentation in the cloud
* Deploying ML seamlessly at production scale using Docker

Keynote

When it comes to model development and deployment, organizations should have the freedom to choose different languages, tools, techniques and environments because a modeling melting pot can fuel innovation and creativity. But without a structured process to coordinate all the different pieces, model management can feel more like the wild, wild west – no governance, no oversight, and no strategy for putting analytic insights to work. This is where SAS® Open Model Manager can help. This new offering from SAS can wrangle the model management process by enabling IT and analytics professionals to register, deploy, and monitor open source models in a consistent, repeatable manner. Users will be able to compare and assess their models, manage champion and challenger models, and access built-in performance reports to quickly evaluate whether to retrain, retire or develop new ones. SAS Open Model Manager integrates with Python and R and is intended for organizations that use open source tools to build models and need model management capabilities…more details

Workshop | Machine Learning | Intermediate

Despite several advances made in learning and decision making in recent years, unrealistic assumptions, inefficiency and lack of interpretability combined with unavoidable ethical, economic and physical constraints avoid the applicability of the existing techniques in many financial problems. This talk will aim to point on the shortcoming of the existing techniques, by focusing on three main topics: 1) the first topic will be around Bayesian optimization techniques for enhancing the reliability, speed and efficiency of design and decision making processes. These include expanding developing online/non-stationary, dimensionality reduction and multi-fidelity Bayesian optimization techniques to go beyond the existing techniques; 2) the second topic will be around reinforcement learning and how Bayesian statistical framework can enable online and efficient decision making that are highly scalable, capable of considering various sources of uncertainty, acting safe and making informative decisions in non-stationary environments; 3) the last topic will be around our developed non-stationary risk-based time-dependent classification frameworks to overcome the deterministic decision making and unrealistic assumption regarding stationarity of the data, a critical factor in dealing with most financial problems.

Talk | Machine Learning | Beginner-Intermediate

In this talk, we will present Metaflow, Netflix’s ML platform, which provides a solid end-to-end foundation for hundreds of internal ML use cases, from classical statistical analysis to large-scale applications of deep learning. Metaflow is designed with a human-centric mindset: instead of reinventing the wheel for large-scale computing or machine learning, we integrate existing solutions into a delightfully consistent and easy-to-use package. This talk focuses on our philosophy towards Machine Learning infrastructure and dives into the internals of Metaflow; it will highlight lessons that we have learned in building a platform that needs to be robust, performant, and flexible enough to solve a large set of complex real-world business problems related to machine learning.

Workshop | NLP | MLOps & Data Engineering | Intermediate – Advanced

Advances in Natural Language Processing (NLP) over the last year have been fundamentally changing the way we work with text based data. We have seen the release of Google’s BERT and OpenAI’s GPT-2. Hugging Face’s Transformers library has democratized Natural Language Understanding (NLU) and Natural Language Generation (NLG) through. While these libraries provide access to powerful capabilities, it can be challenging for many users to figure out how to get started and apply them to their own datasets.

To address this challenge, Novetta has developed an intuitive guide structured as an NLP task framework that drastically lowers the barrier to entry for developers to use these advanced capabilities. This high-level guide enables users to take advantage of open pre-trained language models to fine-tune models for text classification, question answering, entity extraction, and part-of-speech tagging. The sequence of tutorials will provide quick and easy access to a wide variety of embedding schemes for downstream use, such as in recommendation systems. The ability to stand each of these tasks up as a service for easy integration into existing workflows and applications would be fast and straightforward…more details

Workshop

Effective predictive modeling projects follow the analytics life cycle, from data and discovery to deployment and decisions. Data scientists use a variety of tools, both commercial and open-source, to collaborate and develop enterprise applications of analytics and artificial intelligence. SAS Viya provides a unified platform to perform all these from one graphical user interface or through programming APIs. In this workshop, you will load data into memory, prepare input variables for modeling and build complex analytics pipelines to demonstrate powerful machine learning models. Need to integrate open source models? No problem. We’ll show you how you to do that and deploy any model. Then you can save and package the best performing model for deployment while keeping the ability to retrain it on new data…more details