ODSC East 2020 Virtual Conference

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 modelling and rationally visualizing networks,
– able to load node and edge data from a Pandas dataframe,
– 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 diffusion of information on networks..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 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 | 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 | Open-source | Kick-starter | Beginner

The goal of this tutorial is to guide new learners into R. We’ll introduce how to program in R by cleaning and processing data as the use case. This will help transition new learners who work with data in spreadsheets but want to utilize the power of R in their work.

The only pre-requsites needed for the tutorial is having R installed with the `tidyverse`, `data.table` packages installed. The basics of the R language will be taught from the perspective of data processing. The course will be mainly focused around the “tidyverse”, however, where appropriate, we will draw similarities to base R and data.table for completeness...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 | 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

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

Career Lab Talk

Given the proliferation of options for education in data analytics and data science, it is not easy to choose the right program to help one achieve his/her goals. Credit vs. non-credit; degree vs. non-degree; online vs. face-to-face vs. hybrid; quick vs. protracted are all questions facing those that want to further their education. In this session, I will help you learn what questions to ask of different programs in order to determine the best fit for YOU.

When it comes to education there is no one answer to fit all. Masters program makes the most sense for one person, while a bootcamp might better fit another. What are some questions you should ask yourself before exploring different options, and what are some questions you should ask program staff before enrolling? Which programs fit which learning style? How do your objectives inform your choice? What about the cost?..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 | 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

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 | 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

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 | Kick-starter | Beginner

Curious about Data Science? Self-taught on some aspects, but missing the big picture? Well you’ve got to start somewhere and this session is the place to do it. This session will cover, at a layman’s level, some of the basic concepts of data science. In a conversational format, we will discuss: What are the differences between Big Data and Data Science – and why aren’t they the same thing? What distinguishes descriptive, predictive, and prescriptive analytics? What purpose do predictive models serve in a practical context? What kinds of models are there and what do they tell us? What is the difference between supervised and unsupervised learning? What are some common pitfalls that turn good ideas into bad science? During this session, attendees will learn the difference between k-nearest neighbor and k-means clustering, understand the reasons why we do normalize and don’t overfit, and grasp the meaning of No Free Lunch...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 | Kick-starter | All Levels

The emergence of data science as a discipline has impacted businesses in a range of different ways. One primary impact has been to elevate the use of data in decision-making by using statistical methods to assess the ever-growing datasets companies are collecting. This workshop will review and introduce statistical techniques and touch on more advanced methods for dealing with noisy data and applying real-world constraints to analyses. This workshop assumes a working knowledge of standard statistical methods and will aim to connect theory to practice using real-world examples.

Lesson 1: Descriptive statistics and exploring data statistically

– (Re)familiarize yourself with basic descriptive statistics
– Use simple data exploration techniques to identify problems and limitations of a new dataset

Lesson 2: Statistical analyses...more details

Training | Machine Learning | R-programming | 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

Career Lab Talk

What’s the quickest way to pivot to data science? Learn from those who’ve made a career transition about some of the paths they’ve taken to becoming data scientists and from some of the current research on career transitions. Find out what educational opportunities there are for early-career and mid-career professionals that can help accelerate a pivot, and understand the value of these different educational opportunities…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

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

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 | 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 | 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

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

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

Career Lab Talk

Coming Soon!

Career Lab Talk

“Data scientist” has been called the sexiest job of the 21st Century but it’s well on its way to becoming the most dangerous: the state of the art is a collection of siloed, ad hoc techniques developed to solve important and interesting challenges without a robust, principled approach. To paraphrase Michael I. Jordan, we are building dangerous, planetary-scale inference-and-decision making systems without a sufficiently stable engineering discipline, just as people built bridges and buildings, many of which fell before there was civil engineering. In this talk, we’ll delve into how we got where we are today, what types of large-scale systems we’re building in medicine, information technology, finance, transport and society at large, and paths forward…more details

Workshop | MLOps & Data Engineering | Machine Learning | Intermediate

CognitiveScale’s Certifai builds trust into digital systems by detecting and scoring black-box model risks. Cortex Certifai generates the first-ever composite trust score, the AI Trust Index. Certifai can be applied to any black-box model including machine learning models, statistical models, business rules, and other predictive models and works with a variety of input data.

In this workshop, we will dive into the CognitiveScale Certifai solution on Red Hat OpenShift Kubernetes Platform and demonstrate how to increase the trust of your machine learning models by evaluating fairness/bias, robustness, explainability, accuracy, compliance, and data quality. We will show how to accelerate delivering AI/ML workflows by identifying and resolving model problems before pushing them to production…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

Workshop | Deep Learning | Intermediate-Advanced

We will learn optimal path sequencing in the context of Marketing Channel Attribution and Sequencing modeling. While attempting to attribute credits to a channel, it is important to take into account Channel interactions, the number of impressions on the channel and the order in which the channel was touched in a lead’s journey.

The presentation exposes the shortcomings of RNNs and how a combination of RNNs with the Bayesian approach can not only overcome this shortcoming but also improvise the sequence-modeling behavior of RNNs. We will learn this in the context of Marketing Channel Attribution and Path modeling.

The presentation will go on to explain how we can visualize the latent space and perform ‘Next best Action’ on the potential leads, thereby maximizing the impact of channel-based treatment towards any desired outcome...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 | Intermediate

You know Kubernetes is a great platform for the applications you’re running today. Most of the applications you’ll be excited about tomorrow are intelligent applications, which collect data and rely on machine learning to support essential functionality. These capabilities often seem like magic to users, but building applications and services that leverage artificial intelligence is more accessible than you might think. This hands-on lab will show how Kubernetes, the most popular open source container orchestration platform, can increase collaboration and decrease time to value for machine learning workflows. Data scientists will be able to create workflows with ease, deploy models as micro-services and monitor performance to understand when retraining is needed. We’ll focus on the open source infrastructure, tools and processes that will help you to get meaningful results from application intelligence and show why Kubernetes is the best place for data science workloads. You’ll leave having solved a real business problem interactively with powerful machine learning techniques and Kubernetes...more details

Training | MLOps & Data Engineering | Intermediate

MLOps (Machine Learning operations) is all about helping you bring your machine learning workflows to production. This workshop will show you how to get to production faster with optimized experiences for reproducible model training, packaging, validation, deployment and monitoring. Using an MLOps process you can store, version and track ML assets and collect continuous feedback on model behavior. As a result, you can maintain quality models to keep your business and customers ahead...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 | NLP | Intermediate

Coming Soon!

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 | 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 | 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 | 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

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 | Open-source | Beginner-Intermediate

Our goal is for every data scientist to practice data ethics as part of their regular work. In this workshop, you will learn how to make data ethics actionable as a data scientist.

We will present an approach to data ethics based on integrating an ethics checklist into your existing data science workflow. We will be using deon (http://deon.drivendata.org), a lightweight, open-source command line tool designed to easily create and incorporate an ethics checklist into your work. The goal is to enable teams to flexibly carry out the ethical discussions most relevant to them, and to preemptively address issues they may otherwise overlook. There has been much ongoing reflection and dialogue within the data science profession about what ethical principles to abide by, especially in the form of ethical codes and oaths. However, there is often still a gap between those principles and the day-to-day work of data scientists—tenets are either too abstract or too rigid to apply readily. Deon and the checklist framework we present encourages an upfront and ongoing dialogue about the different ethical aspects of your project. By closely integrating data ethics into an ongoing workflow, we as data scientists can cultivate ethical intentionality in our work...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 | Kick-starter | Machine Learning | Beginner

This workshop will provide an overview of how to use machine learning to forecast complex operational problems. This workshop is a hands-on, Python-based, introduction to how machine learning can be used to tackle time-series problems. Topics to be covered include: why time-aware ML problems are different from non-time-aware ML problems; why time-series and forecasting problems in particular are challenging; and how to use to leverage both deep-learning and non-deep-learning approaches to successfully tackle real world problems. This course is a code-based workshop using a variety of Python based tools so familiarity with Python and data science fundamentals will be helpful, but time series experience is not assumed...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

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 | 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

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 | 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 | 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

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

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 | 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

Workshop | Open-source | Intermediate-Advanced

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

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

Career Lab Talk

Creating a large data science team offers many challenges as well as opportunities. From overseeing a dozen or more projects, to hiring and maintaining technical diversity, to setting a technical vision – a manager must balance every day and the big-picture. In this talk I talk about the rules I’ve developed to maximize the leadership, the science, and the relationships to make your team happy and productive…more details

Career Lab Talk

In this talk, I will present an overview of my path into data science from my start in biological engineering, to my interest in code which led me into a PhD in bioinformatics from which I transitioned into my current role doing data science and machine learning for a variety of applications. I will use data to tell you more about how to approach your career in data science. including key insights from surveying my network as well as my own insights. Attendees will learn how we transitioned into data science and tips on how to enter the field…more details

Career Lab Talk

What makes a data science project “good”? Often, both aspiring data scientists and junior data scientists focus too much on the technical – using the most cutting-edge tool and focusing on the small details to eke out slight increases in model accuracy. While technical skills are important, it’s typically the use case of a project that dictates the quality of a project. A poorly thought out project with a great model is less impressive than a simple model that thoughtfully solves a difficult real problem. Whether working on a portfolio project or working on a project for employers, it’s important to have a clear stakeholder and problem in mind. This not only makes projects more realistic, it also gives guidance for the difficult decisions that often need to be made (e.g. about how to clean the data, which data to include, etc). This talk will give guidance on how to make sure your data science project connects with a real use-case, and how that will help make your projects better…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 | 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 | 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.