What Virtual Data Science Training Will be Like at ODSC Europe 2020
ConferencesFeatured PostEurope 2020posted by ODSC Team May 12, 2020 ODSC Team
Virtual training offers an opportunity to learn new in-demand skills – and invest in your future – in a way that reflects your time and travel constraints. This fall, ODSC will bring its unique strengths to the virtual platform to create an even better experience during the ODSC Europe 2020 Virtual Conference.
[Related article: What to Expect from the Europe 2020 Virtual Conference]
At the ODSC Europe 2020 Virtual Conference, we will have opportunities for attendees at every point in their career. For data scientists who are just getting started, or who are contemplating a career change, we have the Bootcamp, with its pre-conference training on September 16th, as well as career support and certification. These two programs will help you not only decide on your next steps, but also implement them.
For data scientists looking to build new skills and take on new projects and challenges to expand on their existing skill set, we have hundreds of training sessions and workshops, on topics such as Explainable ML, Reproducible Pipelines, and Cloud AI, for participants at every level: beginner, intermediate, and advanced.
We’re still putting together our schedule of training sessions, but below you’ll find information on some of our top sessions from past conferences that showcase what you can expect to experience at ODSC Europe in September:
An Introduction to Transfer Learning in NLP and HuggingFace Tools
Explore recent breakthroughs in NLP that resulted from the combination of Transfer Learning and Transformer architectures and learn to use the open-source tools released by HuggingFace like the Transformers and Tokenizers libraries and the distilled models.
ML Engineering for Production ML Deployments
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.
Introduction to Machine Learning for Time-series Forecasting
This hands-on, Python-based workshop will provide an introduction to how machine learning can be used to tackle time-series problems.
State of the Art Natural Language Processing at Scale
This session presented the open-source Spark NLP package for training distributed custom natural language machine-learned pipelines on Apache Spark, walking through the library’s goals, design and API’s using Jupyter notebooks.
Introduction to Machine Learning with scikit-learn
A general introduction to machine learning, as well as an introduction of practical tools for you to apply machine learning in your research, with a focus on one particularly important subfield of machine learning, supervised learning.
Transform your NLP Skills: Using BERT (and Transformers) in Real Life
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, and how to modify them for your own needs.
Machine Learning in R: Penalized Regression to ML optimization pipeline
Learn how to extend regression using penalization for automated variable selection and increased flexibility, then introduce trees, and in particular boosted trees, via xgboost to get incredibly powerful predictions.
From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime
Explore the versatility and power of ONNX and ONNX Runtime by converting a traditional ML scikit-learn pipeline to ONNX, exporting a PyTorch-trained Deep Neural Network model to ONNX, and deploying the models to Azure as a cloud service using Azure Machine Learning services, and to Windows or Mac devices for on-device inferencing.
MLOps – Take Your Data Science Workflows Into Production with MLOps
Learn how to get to production faster with optimized experiences for reproducible model training, packaging, validation, deployment and monitoring
ODSC Europe Speakers
The topics above are representative of what these confirmed speakers will bring to the table. Here are a few of the speakers coming to the event:
- Daria Stepanova, PhD, Research Scientist at Bosch Center for AI
- Alan Rutter, Founder of Fire Plus Algebra
- Dr. Natasha Latysheva, Machine Learning Research Engineer at Welocalize
- Ido Shlomo, Senior Data Scientist at BlueVine
- Veysel Kocaman, PhD, Sr Data Scientist at John Snow Labs
- Andras Zsom, PhD, Lead Data Scientist at Brown University
- Dr. Kirk Borne, Principal Data Scientist at Booz Allen Hamilton
- Jared Lander, Chief Data Scientist at Lander Analytics
- Matt Brems, Global Lead Data Science Instructor at General Assembly
- Bill Shander, Founder at Beehive Media
The topics and speakers above are indicative of what to expect from the ODSC Europe 2020 Virtual Conference. Prices go up Friday, so get your ticket now to save 70% on your pass. You’ll also get access to a series of curated videos from ODSC East, featuring some of these speakers when you purchase an All Access or Bootcamp pass.