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 | 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 | 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

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 | 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

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

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 | 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

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 | 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

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 | 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

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 | 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 | 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 | 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 | 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 | 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

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

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

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

Tutorial | Machine Learning | Research Frontiers | Intermediate

This tutorial will cover both introductory and advanced topics. I will first introduce the uplift concept, contrast with the traditional response modeling method, and review various predictive analytics approaches to Uplift Modeling. Our discussion extends from experimental data to observational data, by integrating Uplift Modeling with Causal Inference. I will also discuss the multiple treatment situation where the optimal treatment for each person needs to be determined. Prescriptive analytics from the optimization field will be employed to handle the uncertainty of lift estimates. I will illustrate the application and methodologies with examples from multiple industries…more details

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

Tutorial | Machine Learning | Intermediate

Most implementations of supervised machine learning algorithms are designed to work with complete datasets but datasets are rarely complete. I will quickly review the three types of missing data (missing completely at random, missing at random, missing not at random) and a couple of simple but often misleading ways to impute. I will spend most of the time describing three advanced methods for handling missing data: multiple imputation, the reduced-feature (aka pattern submodel) approach, and XGBoost, which is one of the few machine learning algorithms that works with incomplete datasets. We will discuss the advantages and limitations of each of these methods. By the end of the workshop, you will have a deeper understanding of the intricate nature of missing data and you will have multiple state of the art techniques in your arsenal to deal with them. I will use python to implement and visualize the techniques, relying on packages like pandas, scikit-learn, xgboost, and matplotlib or plotly for visualizations…more details

Tutorial | Machine Learning | Data Visualization | Beginner-Intermediate

In this 90 minute tutorial, we’ll get a brief demonstration of how modeling is conducted in the tidyverse. Using an example data set, we’ll walk though the process of pre-processing and feature engineering of data, model fitting and tuning, and performance estimation.

The workshop assumes that you have used simple R modeling functions (like `lm()`) and basic tidyverse functions. You don’t need to be an expert. The materials will be on GitHub and we’ll have RStudio servers that you can use if you can’t (or don’t want to) install anything locally…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

Workshop | MLOps & Data Engineering | Intermediate

Kedro is a development workflow tool open sourced by QuantumBlack, a McKinsey company. Many data science teams have started using the library for their pipelines but are unsure how to integrate with other model tracking tools, such as MLflow. In this tutorial, we will give an overview of Kedro and MLflow and demo how to leverage the best of both. The goal of this session is to demonstrate how Kedro and MLflow fit together in a scalable AI architecture. To start, we will give an overview of Kedro and an overview of MLflow: – What are they used for? – What functionality do they provide? – How do they compare as tools? Next, we will walk through a demo of a Kedro project that has MLflow integrated into it. Finally, we will go over deployment options…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

Workshop | Data Visualization | Beginner-Intermediate

Access to high-quality information to make good decisions is necessary for society to function. Good data level the playing field for businesses and individuals alike. It is necessary at every level of economy and society—to help small businesses succeed, schools serve parents and students, and central banks make sound policy. Those data must be an accurate reflection of the people in our country.
We must design a new system that will produce public data that are useful at all levels—and make scientific, careful, and responsible use of many newly available data, such as administrative records from agencies that administer government programs, data generated from the digital lives of citizens, and even data generated within the private sector. This presentation will paint a picture of what this new system could look like, focusing on the innovations necessary to change the existing system, with the goal of providing useful and timely data from trusted sources so that we, the people, have the information necessary to make better decisions.
The presentation particularly focuses on the role of data science and computer science in driving data innovation for evidence based policy…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

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 | 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

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 | 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

Workshop | Machine Learning | Open Source | Intermediate

In this workshop, we will work hands-on using XGBoost with real-world data sets to demonstrate how to approach data sets with the twin goals of prediction and understanding in a manner such that improvements in one area yield improvements in the other. Using modern tooling such as Individual Conditional Expectation (ICE) plots and SHAP, as well as a sense of curiosity, we will extract powerful insights that could not be gained from simpler methods. In particular, attention will be placed on how to approach a data set with the goal of understanding as well as prediction…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 | 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

Workshop | Deep Learning | Machine Learning | Beginner-Intermediate

The AWS DeepRacer service uses a 1/18th scale autonomous race car designed to get ML enthusiasts hands-on with reinforcement learning (RL) through a practical autonomous driving use case. AWS DeepRacer leverages AWS RoboMaker to create a simulation environment in which our reinforcement learning agent (i.e. our autonomous car) can learn how to drive autonomously. The learning part, or training, is facilitated using Amazon SageMaker. The AWS DeepRacer console is optimized to provide a user-friendly introduction to reinforcement learning. In this session, we will talk you through how we built a commercial learning service powered by RL as well as the technologies and architectures (simulation, neural network, application) involved. You will also learn how you can get started using the Amazon SageMaker hosted Jupyter notebook to augment the simulation environment, manipulate inputs to the neural network, modify neural network architecture, run distributed rollouts, and debug your model…more details

Workshop | Computer Vision | Deep Learning | Beginner-Intermediate

This workshop will begin with an overview of common real-world tasks in the CV domain, including examples of problems our customers have faced in recent years. We will then give a brief introduction to deep learning models for CV. The main part of this session will demonstrate how to train and evaluate CV models by executing notebooks based on PyTorch’s Fast.ai and Torchvision libraries. We will start with image classification, how to fine-tune a pre-trained ImageNet model on a custom dataset, and show how to deploy the model to the cloud. Next, we will train an object detection model and extend the model to segmentation masks and keypoints. Finally, we will build an image similarity system and demo a fast image retrieval solution that can handle large amounts of images. We conclude by providing links to all examples and material for the audience to continue their learning journey…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

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

Workshop | R-programming | Data Visualization | Intermediate

The tutorial will be broken into five sections:

1) Tidyverse basics: We’ll review briefly some of the concepts the tidyverse is founded on as well as some programming with {dplyr} and {ggplot2}.
2) Intermediate data processing: We’ll examine the {stringr} and {lubridate} packages to see how to work with strings and dates and also how to do more advanced techniques with {dplyr}.
3) Nesting and unnesting in data frames: We’ll explore how to use the {tidyr} package to reshape data and create list-columns in your data frames to make iterating through your analyses simpler and less error-prone.
4) Functional programming: We’ll get to the point where we are writing functions and using the {purrr} package designed to make functional programming easier. This also ties nicely into using the tidyverse %>% for beginning-to-end workflows…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

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 | NLP

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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

Tutorial | Research Frontiers | Intermediate-Advanced

Thanks to advances in imitation and reinforcement learning techniques, we can now train intelligent agents to accomplish a diverse range of goals. But if we want to create household robots or personal assistants that can take advantage of this diversity, we need to give users some way to tell them what to do! This tutorial will focus on humans’ favorite tools for communicating goals and plans: natural language. We’ll assume basic familiarity with supervised learning and RL, and begin with a review of core machine learning techniques useful for natural language instruction following problems. The body of the talk will focus on modeling techniques for instruction following problems in different kinds of environments and data conditions. We’ll conclude with a survey of other applications for the tools we’ve built, including instruction generation, interpretability, and machine teaching…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

Workshop | ML for Programmers | Deep Learning | Intermediate

Powerful Machine Learning models trained using various frameworks such as scikit-learn, PyTorch, TensorFlow, Keras, and others can often be challenging to deploy, maintain, and performantly operationalize for latency-sensitive customer scenarios. Using the standard Open Neural Network Exchange (ONNX) model format and the open source cross-platform ONNX Runtime inference engine, these models can be scalably deployed to cloud solutions on Azure as well as local devices ranging from Windows, Mac, and Linux to various IoT hardware. Once converted to the interoperable ONNX format, the same model can be served using the cross-platform ONNX Runtime inference engine across a wide variety of technology stacks to provide maximum flexibility and reduce deployment friction.

In this workshop, we will demonstrate the versatility and power of ONNX and ONNX Runtime by converting a traditional ML scikit-learnpipeline to ONNX, followed by exporting a PyTorch-trained Deep Neural Network model to ONNX. These models will then be deployed to Azure as a cloud service using Azure Machine Learning services, and to Windows or Mac devices for on-device inferencing…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

Workshop | NLP | Machine Learning | Intermediate-Advanced

This workshop teaches you the use of transformer neural networks and their incarnations (BERT, RoBERTa, GPT-2) for solving real-world natural language use cases. NLP has advanced tremendously over the last few years and BERT is at the forefront of this success having achieved state-of-the-art results on 11 different NLP tasks. For businesses, BERT has unlocked new NLP use cases that have been previously unattainable.

This workshop will teach you what transformers and systems like BERT and GPT-2 are and how to use and modify them for your needs. Organizations have a wealth of unstructured text sources in every line of business, such as employee feedback in human resources, purchase orders and legal documents in contracting and procurement, communication records throughout the org, and many more. Making sense of this information and organizing it into knowledge and actionable insights to improve business outcomes is a key function every data scientist should be aware of…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 | 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 | 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