Name: How to better manage your data science team's workflow
Start: 2019-12-20T19:00:00Z
End: 2019-12-20T19:40:00.000Z
Location: BrightTALK
Rating: 0

Learn from various data science and engineering experts about key topics for successful machine learning. cnvrg.io will provide you with hands-on tutorials and workshops about the top methods in data science team management, and MLOps getting you from research to production. Stay ahead with the latest developments in auto-adaptive machine learning and CI/CD for machine learning. Learn the latest methods for machine learning model management and deployment with open source tools. Find answers on how to enhance team collaboration in your data science department, and smoothly bridge science to engineering.

Developing, experimenting, and deploying ML models at scale requires substantial tooling, scripting, tracking, versioning, and monitoring.

Data scientists want to do data science – and are slowed down by MLOps and DevOps tasks.

They lack user friendly tools needed to track experiments, attach resources, manage datasets and launch multiple ML pipelines.

In this webinar cnvrg.io CEO, Yochay Ettun will host a special guest from NVIDIA, Sr. Product Manager for NVIDIA DGX systems, Michael Balint, and discuss how to optimize the use of any NVIDIA DGX and NVIDIA GPU asset both on-prem or in the cloud with the cnvrg.io machine learning platform.

We will show best practices to reach high utilization of NVIDIA DGX systems, while conducting meta-scheduling across multiple heterogeneous Kubernetes/OpenShift/Linux server clusters.

In addition, we will introduce the concept of production flows, which automate hundreds of models from the data hub to deployment. We will wrap up with a real-life demo of flows, exercising many experiments across DGX platforms.

What you will learn:

- Creating a data science flow: from data to deployment, while attaching different NVIDIA DGX Kubernetes clusters to each step of the flow
- The concept of meta-scheduler: scheduling experiments disperse resources or other schedulers, accomplishing high utilization at scale
- How the NVIDIA DGX ecosystem with cnvrg.io makes GPU assets consumed easily, with one-click, bypassing complexity of MLOps
- How to leverage NGC containers in ML pipelines

Scaling MLOps on NVIDIA DGX Systems (Special guest from NVIDIA)

Join our hands-on workshop series and deep dive into MLOps and machine learning with cnvrg.io CEO. Each session cnvrg.io will guide you through a hands on ML project using cnvrg.io CORE – a free ML platform we released for the data science community – and tools and frameworks like TensorFlow, Spark on Kubernetes, GitHub and more to help you build and deploy high impact machine learning models. You'll learn about MLOps and how to reduce technical complexity so you can focus on the real magic - algorithms.

How to train your ML models directly from GitHub with MLOps

In part III of the Kubernetes Series workshop we will instruct data scientists and machine learning engineers how to build manage and deploy auto-adaptive machine learning models in production. Data is ever changing, leaving your models outdated and built on old data. This can lead to underperforming models and a lot of manual work to fix it. By allowing your models to continually learn you’ll ensure that they run at peak performance. Using state of the art Kubernetes infrastructure, we’ll show you how to automatically track and manage your auto-adaptive machine learning models while in production. By building auto-adaptive machine learning models, data engineers can bridge the gap between research and production. After this webinar you’ll be able to build and deploy machine learning pipelines that automatically adapt and retrain based on any validation trigger you choose.

Key webinar takeaways:
- How to build auto-adaptive machine learning pipelines
- How to use Kubernetes to manage and scale models in production
- How to automatically monitor for peak performance
- How to set up continuous deployment of ML pipeline

Kubernetes Series (III) Build auto-adaptive ML models with Kubernetes

In part II of the Kubernetes workshop series, we will go over how to run Spark on Kubernetes. As your company accumulates more data, it’s important to leverage all of it to develop new advanced machine learning models. And now, you can scale Spark using Kubernetes. Thanks to the new native integration between Apache Spark’s and Kubernetes, scaling data processing has never been easier. Apache Spark is a well designed high level application that can increase your data processing speed and accuracy. It can handle batch and real-time analytic and data processing workloads. This high level and efficient technology can be used with Java/Spark/Python and R. Joined with Kubernetes, you can get twice the efficiency. Kubernetes is a great engine with the most popular framework for managing compute resources. Unfortunately, running Apache Spark on Kubernetes can be a pain for first-time users. 

Join CTO of cnvrg.io Leah Kolben as she brings you through a step by step tutorial on how to run Spark on Kubernetes. You’ll have your Spark up and running on Kubernetes in just 30 minutes. 

Running Spark on Kubernetes will help you:

- Process larger amounts of data
- Segment your data into sub groups

Kubernetes Series (II): How to run Spark on Kubernetes like a pro

Follow along with our weekly series of workshops focused on Kubernetes for machine learning! 

Kubernetes is an orchestration platform that can be deployed anywhere and can serve any kind of machine and deep learning environment. Kubernetes is a great tool for data scientists to use to stay productive and for data engineers to get production-ready results.  In this free workshop you’ll learn how to build your own Kubernetes to use in your next machine learning pipeline.

Join CTO of cnvrg.io Leah Kolben where she will walk you through each step to set up your Kubernetes cluster, so you can run Spark, TensorFlow, and any ML framework instantly. She’ll touch on the entire machine learning pipeline from model training to model deployment. As a bonus, you will also get pre-configured YAML files to launch your own end-to-end machine learning on a Kubernetes cluster.

From this workshop you’ll learn to: 
- Create a Kubernetes cluster on AWS
- Connect your local development machine to the cluster
- Run any machine learning models (Spark, TensorFlow, and more) on your cluster
- Managing different environments for your Kubernetes cluster (Deep Learning and Big Data Analytics on the same cluster)
- Scale a Kubernetes cluster

Kubernetes Series (I): How to set up Kubernetes for all your machine learning w

MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.

In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.

* Reduce friction between science and engineering
* Deploy your models to production faster
* Health, diagnostics and governance of ML models
* Kubernetes as a core platform for MLOps
* Support advanced use-cases like continual learning with MLOps

MLOps for production-level machine learning

This webinar will instruct data scientists and machine learning engineers on how to build manage and deploy auto-adaptive machine learning models in production. Data is ever-changing, leaving your models outdated and built on old data. This can lead to underperforming models and a lot of manual work to fix it. By allowing your models to continually learn you’ll ensure that they run at peak performance. Using state of the art Kubernetes infrastructure, we’ll show you how to automatically track and manage your auto-adaptive machine learning models while in production. By building auto-adaptive machine learning models, data engineers can bridge the gap between research and production. After this webinar, you’ll be able to build and deploy machine learning pipelines that automatically adapt and retrain based on any validation trigger you choose.

Key webinar takeaways:

- How to build auto-adaptive machine learning pipelines
- How to use Kubernetes to manage and scale models in production
- How to automatically monitor for peak performance
- How to set up continuous deployment of ML pipeline

Build auto-adaptive machine learning models with Kubernetes

CI/CD (Continuous Integration/ Continuous Deployment) has long been a successful process for most software applications. The same can be done with Machine Learning applications, offering an automated and continuous training and continuous deployment of machine learning models. Using CI/CD for machine learning applications creates a truly end-to-end pipeline that closes the feedback loop at every step of the way, and maintains high performing ML models. It can also bridge science and engineering tasks, causing less friction from data, to modeling, to production and back again. Join CEO of cnvrg.io Yochay Ettun as he brings you through how to create a CI/CD pipeline for machine learning, and set up continuous deployment in just one click. With a depth of knowledge in all the latest research, Yochay will share with you today's top methods for applying CI/CD to machine learning.

Webinar takeaways:

- Configure and execute continuous training and continuous deployment for ML
- Define dependencies and triggers
- Automatically connect data pipeline, machine learning pipeline and deployment pipelines
- Integrate model bias detection or fairness and accuracy validations
- Build monitoring infrastructure to close the data feedback loop
- Collect live data for improved model performance

CI/CD for Machine Learning

Scaling AI starts with proper management of data science teams. A common problem data science managers face is how to structure teams for efficiency and communicating results to business leaders. The hard part is streamlining the data science process to eliminate wait time, and easily transition between science and engineering, and business goals. While a standard agile has never been created for data science teams there are many methods that can make machine learning development easier. 

This workshop will give you the proper tools and tactics to manage the entire lifecycle of your machine learning projects, from research to exploration to development and production. Yochay will go over the different roles and responsibilities of a data science team and how to better collaborate on machine learning projects. You’ll learn to manage models, bridge between science and engineering, and save time with reproducible results. In addition, you’ll leave with the tools to more effectively communicate results to your business unit. 

What you’ll learn:

- How to build a data science workflow for reproducibility
- Model management and experiment tracking
- Tools for easy collaboration
- Tools to communicate results to business unit
- How to transition between science and engineering

How to better manage your data science team's workflow

Computer vision is rapidly enhancing how technology reacts with the world around us. Whether it’s autonomous vehicles, handwritten text recognition, face recognition, or detecting disease from x rays, computer vision is greatly improving all industries. Teamed up with the capabilities of AutoML, data science teams can accelerate model development by automating the end-to-end process. AutoML makes machine learning workflows simpler, allowing data scientists to build more complex models. 

In this webinar, data science expert Yochay Ettun will present a step-by-step use case so you can build your own AutoML computer vision pipelines. Yochay will go through the essentials for research, deployment and training using Keras, PyTorch and TensorFlow. He’ll provide an overview of infrastructure and MLOps using Docker, Kubernetes and elastic cloud services. This webinar will enable you to build your own custom AutoML computer vision pipeline and help your business apply machine learning on more use-cases, problems and projects. The aim of this webinar is to democratize machine learning so software developers, engineers, and data scientists can feel confident building a computer vision pipeline.

Key webinar takeaways: 

- How to identify production value of computer vision projects
- How to manage datasets for computer vision and deep learning applications
- How to use AutoML for computer vision
- How to save time and achieve top performing results with transfer learning and reusable machine learning components
- How to build a proper MLOps setup so you can focus more on research and less on IT
- Monitor and track training with specific parameters and metrics
- Integrate computer vision into your application by deploying it as a REST endpoint

Build your own AutoML computer vision pipeline

In this webinar, data science expert and CEO of cnvrg.io Yochay Ettun discusses continual learning in production. This webinar examines continual learning and will help you apply continual learning into your production models using tools like Tensorflow, Kubernetes, and cnvrg.io. This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.

Key webinar takeaways:

- Understanding of continual learning
  - Optimizing your models for accuracy with continual learning
  - How to use TensorFlow, Kubernetes and cnvrg.io to apply CL to your models
- How you can build automatically adaptive machine learning
  - Adapting to shifting data distributions
  - Coping with outliers
  - Retraining in production
  - Adapting to new tasks
  - A/B test your models
- Deploying your machine learning pipeline to production

How to use continual learning in your machine learning models

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How to better manage your data science team's workflow

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