MLOps Hands-on Workshop with Domino
Time:
11:00am - 2:00pm
Target Audience:
Data Scientists and Data Science Practitioners, Hands-on DSLs
Prerequisites:
101 content, no previous Domino experience needed. Python and JupyterLab experience is desirable.
Trainer:
Bryan Prosser
Description:
Join the MLOps Hands-on Workshop to experience first-hand how you can mature your data science lifecycle through MLOps best practices. This workshop will guide you through an end-to-end workflow while you learn about some new best practices.
What’s Included:
Lab Exercises:
• Read in data from a live source
• Prepare your data in an IDE (Integrated Development Environment)
• Train your model on various frameworks
• Compare model performance across different frameworks and select the best performing model
• Deploy a model to a containerized endpoint and web-app frontend for consumption
• Leverage collaboration and documentation capabilities throughout to make all work reproducible and shareable
• See how models can be monitored for drift and qualityLearning Objectives:
An overview of how the Domino platform can streamline your MLOps work. Experiencing the whole data science lifecycle from data ingestion and experimentation through to publishing and monitoring. At the end of the workshop, you will earn an MLOps Champion digital badge to add to your LinkedIn profile and learn how other enterprise data science teams have benefited.
Hardware & Software:
Please bring your laptop with a Chrome or Firefox browser and ensure you have Internet access
Ray - Beginner to Intermediate Technical Workshop Track
Time:
2:30pm - 4:45pm
Target Audience:
Data Scientists and Data Science Practitioners, Hands-on DSLs
Prerequisites:
Familiarity with Domino, Python, and JupyterLab.
Trainer
Nikolay Manchev
Description:
Ray is an open-source framework for building distributed applications in Python, providing scalability, parallelism, fault-tolerance, flexibility, and productivity. Learning to use Ray can help data scientists handle large datasets, write faster computations, and deploy distributed applications easily.
What’s Included:
• Ray fundamentals
• Setting up Ray in Domino
• Connecting to Ray in Domino
• Submitting Remote Tasks
• Interpreting logs and errors
• Effectively distributing work and avoiding anti-patterns
• Task Management
• Actors and Scheduling
• Object Store
• Ray Data
• ModinLearning Objectives:
• What is Ray and how Domino supports on-demand Ray clusters
• How to configure and access a Ray cluster in Domino
• How to efficiently parallelize and distribute code
• How to work with large data in Ray
• Scaling Pandas workflows with ModinHardware & Software:
Please bring your laptop with a Chrome or Firefox browser and ensure you have Internet access
Domino for Statistical Programming 101
Time:
11:00am - 12:00pm
Trainers/Presenters:
Stuart Malcolm
Target Audience/Prerequisites:
Statistical Programmers
Description:
This course will cover the basics of how to conduct the statistical analysis for a clinical submission in the Domino platform. Leaving this course, participants will know how to use SAS Studio in Domino, where files and code are stored, and how to use a standard SAS code library to generate SDTM and ADaM. Participants will follow examples from the trainer, as well as go through a series of independent exercises, using data from the CDISC pilot study and the basic features of the Domino platform to create and review outputs.
What's Included:
• Live training delivered by an Industry Expert
• Hands-on experience with executing aspects of a mock study in Domino
• Slides with notesLearning Objectives:
• How to use SAS in Domino
• How to set up a study in Domino
• How to use a standard code library in Domino
• How to create and review SDTM and ADaM from raw data in DominoHardware & Software:
Please bring your laptop with a Chrome or Firefox browser and ensure you have Internet access
Domino for Statistical Programming 102
Time:
1:00pm - 2:00pm
Trainers/Presenters:
Stuart Malcolm
Target Audience/Prerequisites:
Statistical Programmers/Domino for Statistical Programming 101
This course will build on Domino for Statistical Programming 101 and is required to complete a basic understanding of how to use SAS for clinical trials in Domino. Leaving this course, participants will be able to edit metadata to quickly change output without having to rewrite or revalidate code and execute batch jobs to generate SDTM to ADaM to TFL. Participants will be required to write non-standard SAS code during this course.
What's Included:
• Live training delivered by an Industry Expert
• Hands-on experience with executing aspects of a mock study in Domino
• Slides with notesLearning Objectives:
• Using metadata
• Executing batch jobs
• Contributing code to a study
• Using Domino efficientlyHardware & Software:
Please bring your laptop with a Chrome or Firefox browser, Excel, and ensure you have Internet access
Automating Clinical Trial Reporting with Python
Time:
2:30pm - 3:30pm
Trainers/Presenters:
Stuart Malcolm
Target Audience/Prerequisites:
Clinical Data Scientists, Statistical Programmers
In this course, participants will explore some creative uses of Domino's more advanced features to automate repetitive processes in clinical trials analysis and reporting, such as code generation, workflow automation, and study setup. Leaving this course, participants will have a basic understanding of how to call a Domino API, how to make use of open-source utilities, and how to use Snakemake, Domino Code Assist, and Domino Launchers.
What's Included:
• Live training delivered by an Industry Expert
• Hands-on experience with Domino
• Slides with notesLearning Objectives:
• Using the Domino API
• Using open source in Domino
• Using Domino Code Assist
• Using Domino Launchers
• Using Snakemake in DominoHardware & Software:
Please bring your laptop with a Chrome or Firefox browser and ensure you have Internet access
Building Interactive Statistical Displays with Shiny
Time:
3:45pm - 4:45pm
Trainers/Presenters:
Stuart Malcolm
Target Audience/Prerequisites:
Clinical Data Scientists, Statistical Programmers
In this course, participants will create and deploy interactive statistical outputs using the Shiny package. This course is for statistical programmers who have had some introductory training in the R language and would like to build upon that knowledge. Participants will leave this course with an understanding of how to use R Studio and open source in Domino, how to use Domino Code Assist to generate R code, and how to deploy a Shiny app.
What's Included:
• Live training delivered by an Industry Expert
• Hands on experience with Domino
• Slides with notesLearning Objectives:
• Using Domino Code Assist
• Using the Shiny package
• Using R Studio in Domino
• Deploying Shiny appsHardware & Software:
Please bring your laptop with a Chrome or Firefox browser and ensure you have Internet access
Harnessing the Power of SageMaker Autopilot and Domino for Architecting MLOps Solutions
Time:
10:00am - 11:00am
Target Audience:
Data Scientists
Trainers/Presenters:
Rumi Olson, Josh Mineroff
Description:
In this course, participants will learn how to architect an MLOps solution on Domino while leveraging the capabilities of Amazon SageMaker Autopilot. Participants will gain insight into ensuring data security when accessing data sources such as Amazon S3 from Domino. They will learn best practices utilizing Domino Cloud to harness the power of data, including training models, deploying them to Amazon Sagemaker through APIs and containers, and monitoring these models using Domino's monitoring features.
What's Included:
• Live training delivered by AWS and Domino Experts
• Hands on experience with Domino and Amazon SageMaker Autopilot
• Slides with NotesLearning Objectives:
• Learn how Domino works with SageMaker Autopilot
• Deploy and monitor a SageMaker Autopilot model using Domino
• Understand MLOps Architecture using Domino and AWSHardware & Software:
Please bring your laptop with a Chrome or Firefox browser and ensure you have Internet access
Accelerated Data Science from Exploration to Explanation with NVIDIA RAPIDS
Time:
11:00am - 12:00pm
Target Audience/Prerequisites:
Data Scientists and Data Science Practitioners, Hands-on DSLs; Familiarity with Python
Trainers/Presenters:
Wiliam Benton
Description:
NVIDIA GPUs made the AI and deep learning revolutions possible, but many enterprises aren't yet using deep learning for problems involving structured data. However, accelerated computing can also turbocharge classical machine learning and data science workflows. In this hands-on workshop, you'll learn how NVIDIA GPUs and NVIDIA RAPIDS can make every task crossing a data scientist's desk more productive, faster, and more fun. You'll experience the benefits of acceleration for exploratory data analysis, model training, and model interpretability in the context of an end-to-end workflow involving detecting payments fraud. You'll come away from this session with a better understanding of how to get started taking advantage of accelerated computing for your data science initiatives.
What's Included:
• Overview of NVIDIA Rapids
• Understanding the place of GPU acceleration in the data science workflow
• Using GPU-acceleration for exploratory data analysis and model trainingLearning Objectives:
• Architecture of the NVIDIA RAPIDS Suite
• Data movement and transformation in the context of GPU processing
• The role of cuML, cuDF, cuGraph, and other components in the RAPIDS SuiteHardware & Software:
Please bring your laptop with a Chrome or Firefox browser and ensure you have internet access
Conda Basics: Quickstart Guide
Time:
1:00pm - 2:00pm
Target Audience:
AI/ML/DS (artificial intelligence/machine learning/data science) practitioners, anyone who wants to leverage Conda packages, anyone who wants to maintain an organized computational environment
Prerequisites:
Attendees should have basic machine learning knowledge and MATLAB skills
Trainer
Frank Yang
Description:
Learn the essential skills of managing software packages with conda in just one hour. You will discover why conda is a must have tool for AI/ML/DS, especially if you use Python and R. This hands-on tutorial will show you how to install packages, manage environments, and use channels. Whether you are new to conda or looking to improve your skills, this course will equip you with the knowledge you need to tap into the conda ecosystem and be more productive in your software development and data analysis workflow.
What's Included:
• Overview of conda
• Hands-on exercises of essential tasks
• Guidance on best practicesLerning Objectives:
• Understand the essentials of conda usage
• How to search for and install packages
• How to create, manage, and share environmentsHardware & Software:
Command line basics (e.g. cd, ls/dir), file system basics (e.g. root directory, home directory)
Develop AI Models for the Edge with MATLAB
This session has been cancelled, however you may attend other trainings on the agenda.
Time:
2:30pm - 4:45pm
Target Audience/Prerequisites:
Data Scientists and Data Science Practitioners, Hands-on DSLs; Attendees should have basic machine learning knowledge and MATLAB skills
Trainers/Presenters:
Mehernaz Savai
Description:
Artificial Intelligence (AI) techniques like deep learning are introducing automation to the products we build and the way we do business. In addition, AI models are increasingly integrated into battery-powered consumer devices with limited resources and extremely low power which requires deliberate trade-offs between size of model, accuracy, inference speed, and power consumption. With MathWorks tools, one can leverage well-established AI workflows to bring your AI models to any edge device, taking advantage of automated code generation, datatype optimization and more.
Learning Objectives:
• Train deep neural networks in MATLAB using GPUs on the Domino Platform
• Create and explore deep learning models
• Import and export models from TensorFlow, PyTorch, and ONNX into and from MATLAB
• Use pruning in combination with network quantization to reduce the inference time and memory footprint of the network
Hardware & Software:Please bring your laptop with a Chrome or Firefox browser and ensure you have internet access
Accelerating Innovation with Domino: Partner Solution Showcase
Time:
11:00am - 12:00pm
Target Audience/Prerequisites:
Domino Partners; Business/Executive
Speakers:
• Sid Khare, Head of Partnerships, Domino
• Hari Khatavkar, Consultant, Data & Analytics, Slalom
• Rizan Ahmed Mohammed, Senior Consultant, Data & Analytics
• Paul Intrevado, San Francisco Practice Lead, Data Science & Artificial Intelligence, Capgemini
• Kolin Konjura, Data Science Senior Consultant, Capgemini
• Okeefe Niemann, Data Scientist, CapgeminiDescription:
Accelerate Retail Demand Forecasting with Artificial Intelligence: A showcase of the collaboration between Domino and Slalom for demand forecasting solutions. Speakers will demonstrate an end-to-end lifecycle of a demand forecasting solution built on Domino that will streamline customers’ supply chain and accelerate customers’ journey into artificial intelligence and becoming model-driven business.
What’s Included:
• Welcome and introductions
• Capgemini Solution Showcase | Powering Success: Unveiling the Data-Driven Journey: A showcase of the collaboration between Domino and Capgemini for clients in the energy sector. Speakers will demonstrate how varied user personas - from the highly technical to the business-focused user and manager - can collaboratively leverage Domino.Learning Objectives:
• Understand the partnership opportunity
• Best practices from Domino Partners
• Building Domino solutionsHardware & Software:
N/A
Demystifying the MLOps Ecosystem
Time:
1:00pm - 2:00pm
Target Audience/Prerequisites:
Domino Partners, High-level technical (Solution Architects, Partner Engineers)
Speakers
• Josh Mineroff | Head of Technical Alliances
• William Vick, Global Field CTO, NVIDIA
• Lior Balan, Director of Cloud & Sales, Run.ai
• Kyle Woestmann, Strategic Account Executive, Weights & BiasesDescription:
Learn the stages of the MLOps life cycle by exploring the diverse landscape of MLOps tools and technologies. Build or buy? Open source or commercial? Learn strategies and investments to scale AI from experimentation to enterprise-grade production. Gain a deeper understanding of how key MLOps ecosystem technologies can be paired, and how they enable organizations to effectively manage their machine learning projects from development to deployment to monitoring. This session will provide a comprehensive overview of key MLOps concepts, best practices, and technologies - equipping you with the knowledge and insights needed to navigate the rapidly evolving MLOps landscape with confidence.
What’s Included:
• Overview of ML lifecycle
• MLOps technologies across ML lifecycle steps
• Panel discussion from industry expertsLearning Objectives:
• Understand technology investments for MLOps at enterprise scale, including decisions around build/buy and open source/commercial
• Knowledge to guide prospects and customers on MLOps technology stack
• Panel discussion with industry expertsHardware & Software:
N/A
Journey to the AI Center of Excellence
Time:
2:30pm - 3:30pm
Target Audience/Prerequisites:
Domino Partner; Business/Executive (Non-technical)
Speakers:
• Niraj Juneja, AI Specialist Leader, Deloitte
• Paul Intervardo, Sr. Manager - Data Science & AI, Capgemini
• Soumya Ghosh, Senior Director, Data & Analytics, Slalom
• Andy Lin, VP Strategy & Innovation, CTO, Mark IIIDescription:
Embark on a transformative journey with us as we explore the essential elements for helping clients establish an AI Center of Excellence. Our panel of industry experts will discuss the critical components of a successful AI CoE, including strategic vision, infrastructure and tooling investments, people and process development, and robust governance. Learn how to overcome common challenges and enable your customers' organizations to unlock the full potential of artificial intelligence and machine learning. By sharing best practices, real-world case studies, and valuable insights, this session aims to explore the knowledge, tools, and processes needed to create a thriving AI Center of Excellence.
What’s Included:
• Overview of AI Center of Excellence model
• Drive adoption and time to value for data science investments
• Become a trusted advisor for enterprise clients
• Panel discussion with industry expertsLearning Objectives:
• Advise prospects and clients on investments to drive data science adoption
• Best practices for building a thriving data science function
• Understand the AI/ML Center of Excellence model and best practicesHardware & Software:
N/A
Fireside Chat: Future of MLOps
Time:
3:45pm - 4:45pm
Target Audience/Prerequisites:
Domino Partners, Business/Executive (non-technical)
Trainers/Presenters:
Thomas Robinson, COO, Domino
Description:
Join us for a forward-looking session dedicated to the future of MLOps, where we will explore emerging industry trends, Domino's ambitious product roadmap, and identify exciting opportunities for our valued partners. Our executive leadership speakers will discuss the latest trends in the MLOps ecosystem and present overviews of Domino's recent product innovations, including Code Assist and Nexus. As we delve into the vast potential of our partner ecosystem, we will highlight the vital role our partners play in driving adoption, providing comprehensive solutions, and pushing the boundaries of what's possible in MLOps. Don't miss this unique opportunity to gain valuable insights, shape the future of MLOps, and unlock new avenues for collaboration and growth.
What’s Included:
• Hear directly from Domino’s executive leadership team
• Brief history of MLOps landscape
• Recent MLOps trends and innovations
• Forward-looking MLOps predictionsLearning Objectives:
• Understand the latest trends in MLOps
• Latest Domino product innovations and roadmap preview
• Understand where MLOps is headingHardware & Software:
N/A
Rev 4 Training
Wednesday, May 31st
Online registration currently closed.
On-site registration begins May 31st at 8AM on the 6th Floor.
Time | MLOps Platform Track | Pharma Track | Data Science Track | MLOps Partner Track |
---|---|---|---|---|
10:00am - 11:00am | • Harnessing the Power of SageMaker Autopilot and Domino for Architecting MLOps Solutions | |||
11:00am - 12:00pm | • MLOps Hands-on Workshop with Domino | • Domino for Statistical Programming 101 | • Accelerated data science from exploration to explanation with NVIDIA RAPIDS | • Accelerating Innovation with Domino: Partner Solution Showcase |
12:00pm - 1:00pm | Lunch Break | |||
1:00pm - 2:00pm | • MLOps Hands-on Workshop with Domino cont. | • Domino for Statistical Programming 102 | • Conda Basics: Quickstart Guide | • Demystifying the MLOps Ecosystem |
2:00pm - 2:30pm | Break | |||
2:30pm - 3:30pm | • Ray - Beginner to Intermediate Technical Workshop | • Automating Clinical Trial Reporting with Python | • Develop AI Models for the Edge with MATLAB This session has been cancelled, however you may attend other trainings on the agenda. |
• Journey to the AI Center of Excellence |
3:30pm - 3:45pm | Break | |||
3:45pm - 4:45pm | • Ray - Beginner to Intermediate Technical Workshop cont. | • Building Interactive Statistical Displays with Shiny | • Develop AI Models for the Edge with MATLAB cont. This session has been cancelled, however you may attend other trainings on the agenda. |
• Fireside Chat: Future of MLOps |
MLOps Platform Track
Pharma Track
Data Science Track
MLOps Partner Track
The enterprise MLOps landscape is fueled by a rich ecosystem of partners across technology, services, and solutions. Learn from industry experts - including leaders from Domino and partners. Free registration for this track using "PartnerSummit" promo code.
Online registration currently closed.
On-site registration begins May 31st at 8AM on the 6th Floor.