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What To Expect
As we prepare our 2020 schedule, take a look at some of the most popular previous half and full day training and workshops we have hosted at the ODSC Virtual Conference for an idea of what to expect.
| Recommendation Systems in Python | Intermediate Machine Learning with scikit-learn |
| Echo State Networks for Time-Series Data | Machine Learning in R: Penalized Regression to ML optimization pipeline |
| ML Engineering for Production ML Deployments | Deep Neural Networks Assisted Simulation Surrogates for Parameter Space Exploration |
| Modern and Old Reinforcement Learning | Natural Language Processsing using Python |
| Deep Learning (with TensorFlow 2) | Responsible AI – State of the Art and Future Directions |
| Building Data Narratives: An End-to-End Machine Learning Practicum | Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems |
| An Introduction to Transfer Learning in NLP and HuggingFace Tools | Missing Data in Supervised Machine Learning |
| Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow | Streaming Decision Intelligence and Predictive Analytics with Apache Spark 3 |
| Deep Transfer Learning for Computer Vision: Real-World Applications at Nanoscale | End to End Modeling and Machine Learning |
| Machine Learning in R: Putting R-Based Machine Learning into Production with Plumber and Docker | Advanced Machine Learning: Pipelines and Evaluation Metrics |
| Data Science Best Practices: Continuous Delivery for Machine Learning | From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime |
| Introduction to Machine Learning for Time-series Forecasting | Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline |
| State of the Art Natural Language Processing at Scale | Kubernetes: Simplifying Machine Learning Workflows |
| Introduction to Machine Learning with scikit-learn | Machine Learning for Algo Trading |
| Transform your NLP Skills: Using BERT (and Transformers) in Real Life | MLOps – Take Your Data Science Workflows Into Production with MLOps |
| Simplify and Scale Data Engineering Pipelines with Open Source Delta Lake | Kedro + MLflow – Reproducible and Versioned Data Pipelines at Scale |
| Graph Powered Machine Learning | Applied Deep Learning for NLP Applications |
| Deploying Deep Learning Models as Microservices | SQL Deep Dive for Data Science |
| Multi-Channel Optimal Path Sequencing Through Bayesian Deep Learning | Deciphering the Black Box: Latest Tools and Techniques for Interpretability |
| Advances in Julia for Data Science and ML | AI Operationalization with Governance and Model Risk Management |
| Deep Learning in Intelligent Process Automation | Methods for Using Observational Data to Answer Causal Questions |
| Bayesian Data Science: Probabilistic Programming | Interpreting and Explaining XGBoost Models |
| Cloud AI Services: What They are and How to Use Them |
Confirmed Sessions & Instructors
Click on the toggle bars for information around sessions and instructors. Check full Speaker and instructor line-up here.
Half-Day Training | R-Programming | Beginner
Should a data scientist use R or should they use Python? The answer to this rather delicate question is, of course, they should know a bit each and use the most appropriate language for the task in hand. In this tutorial, we’ll take participants through the R tidyverse, one of the (many!) areas that R shines. The tidyverse is essential for any data scientist who deals with data on a day-to-day basis. By focusing on small key tasks, the tidyverse suite of packages removes the pain of data manipulation. We’ll cover some of the core features of the tidyverse, such as dplyr (the workhorse of the tidyverse), string manipulation, graphics and the concept of tidy data…more details
Half-Day Training | Data Visualization | MLOps & Data Engineering | Beginner
Learn the art and science, the theory and practical hands-on tactics of creating compelling communications experiences based on surveys, scientific research, public or proprietary data sources. In this talk, you will learn about some of the best practices for data storytelling and visualization and walk away with a new way of thinking and some practical tips you can put to work the moment the session is done…more details
Half-Day Training | Machine Learning | R-programming | Intermediate-Advanced
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
Half-Day Training | Deep Learning | Machine Learning | Intermediate-Advanced
Welcome to deep learning building blocks. This is an intermediate tutorial on deep learning that focuses on how to design neural networks for various data types.…more details
Workshops | Machine Learning | Open Source | Intermediate
For BlueVine, and indeed for any Fintech company, figuring out the client’s industry is a critical factor in making precise financial decisions. Traditional sources are invariably pricey, inaccurate and unavailable, and as such leave an opening for an ML based solution. We met that challenge building a service that predicts the industry using the business’s publicly available web data. By employing the latest innovations in NLP (BERT) and some of the most powerful scraping and deployment tools available (Scrapy and Amazon SageMaker) we were able to dramatically surpass the performance achieved by any other such tool in the space.
This presentation will cover the entire development pipeline hands-on: Crowdsourcing a tagged sample, building a smart and scalable web scraper, prepping and feeding the resulting raw data into BERT, fine tuning the model and finally deploying it as a cloud based service behind an API. Both model training and deployment will be through Amazon SageMaker.…more details
Workshop | NLP | Research Frontiers | 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 distributed environment…more details
Workshop | Machine Learning | | Intermediate
I will present an overview of causal inference techniques that are a good addition to the toolbox of any data scientist, especially in certain circumstances where experimentation is limited. Use of these techniques can provide additional value from historical data as well to understand drivers of key metrics and other valuable insights. The session will be practical focused with both theory and how to perform techniques in R. The end of the session will close with recent advances from combining machine learning with causal inference techniques to do things such as speed up AB testing…more details
Half-Day Training | Beginner
For professionals with previous business experience looking to begin their journey into data science, this three hour morning session on Introduction to Statistics for Data Science will focus on giving attendees terminology, mental frameworks, and practical advice on learning about statistics for data science. Attendees will leave the workshop with a high level understanding of the importance of using statistics in their data science practice, which will be framed under two main goals: quantifying uncertainty and the necessity of building models in order to make falsifiable predictions about the world around them. The session draws examples from many real-life examples in order to be industry agnostic. Materials for the workshop will be available in both R and Python, but no emphasis will be put on one programming language or another..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
Half-Day Training | Kick-starter | Open-source | Intermediate
In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for tabular data analysis. We start by learning the core Pandas data structures, the Series and DataFrame. From these foundations, we will learn to use the split-apply-combine paradigm for grouped computations, manipulate time series, and perform advanced joins between datasets. Specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals and advanced features of Pandas, be aware of common pitfalls, and be ready to perform your own analyses…more details
Half-Day Training | Machine Learning | R-programming | Beginner
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
Half-Day Training | MLOps & Data Engineering | Beginner-Intermediate
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
Half-Day Training | MLOps & Data Engineering | R-programming | Intermediate
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
Half-Day Training | Open-source | Beginner
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
Tutorial | NLP | MLOps & Data Engineering | Beginner
Advances in Natural Language Processing (NLP) over the last year have been changing the way we work with text-based data. We have seen the release of Google’s BERT, OpenAI’s GPT-2, and Hugging Face’s Transformers library that has helped many use these kinds of models. But it can still be challenging for many users to get started and apply them to their own datasets.
To address this challenge, Novetta has open sourced AdaptNLP, an intuitive framework that lowers the barrier to entry to use these advanced capabilities. This high-level framework enables users to use fine-tune pre-trained language models for text classification, question answering, entity extraction, and part-of-speech tagging. This tutorial will provide quick and easy access to a variety of embedding schemes for downstream use. The ability to stand each of these tasks up as a service for easy integration into existing workflows and applications becomes fast and straightforward....more details
Workshop | ML for Programmers | Open-source | Intermediate
Pomegranate is a Python package for probabilistic modeling that emphasizes both ease of use and speed. In keeping with the first emphasis, pomegranate has a convenient modular API for building complex models out of simple components and a simple sklearn-like API for using them. In keeping with the second emphasis, the computationally intensive parts of pomegranate are written in Cython and all models support multithreaded parallelism and out-of-core computations. In this talk I will give an overview of the features in pomegranate, including missing value support and the new data generators, and demonstrate how they can yield more accurate models. I will also demonstrate how one can use existing components to build neural probabilistic models, such as neural HMMs, using your favorite neural network package…more details
Tutorial | Machine Learning | Open-source | Beginer-Intermediate
Ever wonder how Facebook’s facial recognition or Snapchat’s filters work?
Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models.
In this tutorial, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more…more details
Half-Day Training | Data Visualization | | Intermediate
In this session, you’ll learn how to turn any matplotlib plot into an animated gif with just two extra lines of code (well… three, if you count “import gif”)!..more details
Half-Day Training | MLOps & Data Engineering | R-programming | Intermediate-Advanced
After we build various machine learning models we need to make them accessible to others. We use modeldb so we can store models in a database and plumber to expose our model as a REST API that can be hosted in a Docker container…more details
Half-Day Training | Machine Learning | Intermediate
Unsupervised learning models (including analysis of correlations, clusters, and associations in data) converge more readily to a useful solution if we start with good model parameterizations. Feature engineering is key, but selection of features often becomes guesswork. Similarly, in supervised machine learning, the choice of features in labeled data to use in training may still seem arbitrary. So, how does model-building start and move towards an optimal solution? This challenge is known as the cold-start problem! The solution to the problem is easy (sort of): We start with a guess, a totally random guess! That sounds so random, and so wrong! But there is an orderly and productive way forward from such a start, which we will describe in this workshop. We will present several machine learning modeling examples, suggested solutions to their cold-start challenges, and related concepts, including the objective function, genetic algorithms, backpropagation, gradient descent, and meta-learning…more details
Half-Day Training | Quant Finance | Machine Learning | Intermediate
The rapid progress in machine learning (ML) and the massive increase in data availability has enabled novel approaches to quantitative investment and increased the demand for the application of data science to develop discretionary and automated trading strategies.
This workshop covers popular ML use cases for the investment industry. In particular, it focuses on how ML fits into the workflow of developing a trading strategy, from the engineering of financial features to the development of an ML model that generates tradable signals, the backtesting of a trading strategy that acts on these signals and the evaluation of its performance.
We’ll use common Python data science and ML libraries as well as Zipline, Pyfolio, and Alphalens. The code examples will be presented using jupyter notebooks and are based on the second edition of my book ‘Machine Learning for Algorithmic Trading’…more details
Tutorial | Research Frontiers | Machine Learning | Intermediate
Many real-world phenomena such as epidemics spreading, traffic flow, team sports, and climate variation involve complex spatial patterns evolving through time. Our ability to learn and mine from large-scale spatiotemporal data is critical to real-time decision making in various science and engineering fields. However, existing machine learning methods are still insufficient for large-scale spatiotemporal data due to the presence of non-linear, non-Euclidean, multi-resolution, high-dimensional, and complicated physical characteristics. The goal of this tutorial is to (1) provide an overview of the nature of spatiotemporal data and its relevance to the data mining community (2) survey recent development in machine learning to address the challenges specific to spatiotemporal data, and (3) identify the open problems and future directions. We believe this is an emerging and high-impact topic in machine learning, which will attract both academics and practitioners in data science as well as domain scientists…more details
Half-Day Training | Deep Learning | Advanced
Although supervised learning has dominated industry machine learning implementations, unsupervised and semi-supervised methods have started to be practically applied to real world problems (outside of playing video games). Generative Adversarial Networks (GANs) are being utilized to augment data and generate dialogue, and Reinforcement Learning (RL) is helping people plan marketing campaigns and control robots.
In this training, you will develop a theoretical understanding of these and other related state-of-the-art AI methods along with the hands-on skills needed to train and utilize them. You will implement a variety of models in TensorFlow for tasks including object recognition, image generation and robotics…more details
Half-Day Training | Machine Learning | Intermediate
How could a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? How can a mathematical concept from the 18th century be used to estimate the mass density of galaxies across the Universe? How could a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a F1 race from the 1950’s inspire one of the greatest machine learning use cases for the Internet of Things? This workshop will answer these questions, and more, by presenting several examples of typical algorithms that were adopted for specific use cases or application domains, then showing how each one can be adapted to an atypical (often mind-bending) use case, producing significantly surprising results in some other domain. These exercises serve to demonstrate how data scientists can create even more value, beyond that which is expected, from our data sets and our algorithmic talents…more details
Tutorial | Machine Learning | MLOps & Data Engineering | Intermediate
ONNX Runtime is the inference engine for inferencing ML models in different HW environments. Applications of AI are everywhere. This requires ML models that are trained in the cloud to execute on small devices with low power, low compute, and low memory. Such devices are typically used in IoT scenarios. The data captured by these devices are processed before sending the telemetry to the cloud for further actions on the business application. ONNX Runtime has made enhancements to enable execution of ML models in these edge devices to power AI on the edge applications. This session will walk through the workflow to train an image classification model, package in container and deploy to IoT device…more details
Workshop | Machine Learning | Open-source | Intermediate-Advanced
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
Half-Day Training| Machine Learning | Beginner
Programming is a great way to get practical insights about math theoretical concepts. The goal of this session is to show you that you can start learning the math needed for machine learning and data science using code. You’ll learn about scalars, vectors, matrices and tensors, and see how to use linear algebra on your data. Don’t worry if you don’t have a math background, we’ll explain the mathematical notations and conventions. At the end of the session, you’ll know how to use the norm of vectors, and apply the concept to model regularization (Hands-on Session 1.) and cost functions. You’ll also see how to manipulate matrices and tensors with Numpy, and learn to use them to store and classify images (Hands-on Session 2).…more details
Tutorial | ML Ops & Data Engineering | All Levels
The workshop is aimed at data scientists and data engineers who are interested in building a production-ready data pipelines
We will go into:
- The challenges associated with creating ML models that are deployable
- Software engineering principles that should be applied to ML code to make it easier to deploy in the production environment
- How you can use an open-source Python library, called [Kedro](https://github.com/quantumblacklabs/kedro), to enhance your exploratory data analysis workflow as well as their transition to production-ready code.…more details
Tutorial | Deep Learning | All Levels
Transformer has been around for a while now, and has proven to be one of the most interesting models of modern deep learning.
It turns out that Transformer models have proven to be domain agnostic. It means that, that despite of its initial application for seq2seq NLP tasks with 1D sequences of text, the 1D transformer input can be of any form. Namely, a 2D image unrolled into long 1D sequence of pixels can be understood with notion of its 2D image characteristics involving object appearance, category, or even predicting next image appearance in very long sequences.
Recent research show that transformer originated architectures for computer vision often tends to be simpler and provide performance at worst on pair with modern architectures such as RCNNs used for the computer vision tasks. Presentation is going to discuss recent research in area of transformer applications in new domains..more details