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.
Join CEO, Yochay Ettun, as he walks you through the main components of the cnvrg.io platform so you can begin rapid experimentation, building ML pipelines, and deploying your models to production in one click. Download the recording and get a free trial!
Deployment is a major challenge facing enterprise success in AI. On premise solutions face specific difficulties that we will discuss in this webinar. We will discuss best practices of enterprise machine learning, and how to get more of your models to production. While there are many solutions that help streamline the ML deployment process for cloud enterprises, few solutions exist for on premise enterprises
Join this live webinar to examine best practices for building a machine learning pipeline that enables quick iteration, deployment and CI/CD.
Learn enterprise-level strategies for monitoring machine learning bias in a live webinar. Join data science experts as we seek ways to prevent bias in your ML models.
In this webinar, we’ll discuss how to build a system to monitor your machine learning model in production on Kubernetes. You’ll learn to keep track of different models and their model performance over time, and how to set up custom alerts for your models.
Join cnvrg.io and special guest Scale AI in a webinar on continual learning with human-in-the-lop. By creating a continuous feedback loop between human and machines, machine learning models become smarter, more confident, and more accurate over time.
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. 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.
Follow cnvrg.io CTO Leah Kolben in a step-by-step tutorial on how to deploy and connect your machine learning models with Kubernetes. In just 30 minutes she’ll show you how to get your model in production and running smoothly for your end-user in a real-time example.
This webinar will instruct data scientists and machine learning engineers on how to build manage and deploy auto-adaptive machine learning models in production. 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.
oin 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. Learn how 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.
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.
Join CTO of cnvrg.io Leah Kolben in a live workshop. Leah will walk you through each step to set up your Kubernetes cluster, so you can run Spark, TensorFlow, and any ML framework instantly.
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.
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.