Webinars

Workshop: Monitor your ML models in production with Kubernetes
Wednesday, December 11th | @12pm EST

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. We’ll discuss what types of variants to monitor, and how to measure its performance. Join CTO of cnvrg.io, Leah Kolben in this hands-on workshop on critical practices for monitoring your machine learning models in production. Using the power of Kubernetes, we’ll build a complete system for model tracking that ensures high performing models in production.

Webinar: Continual Learning with Human-in-the-loop
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Join cnvrg.io and special guest Scale AI in a webinar on continual learning with human-in-the-lop. The reality of AI applications relies on the synchronization of human and machine intelligence. By creating a continuous feedback loop between human and machines, machine learning models become smarter, more confident, and more accurate over time. Get an inside look at how data science experts are using the Human-in-the-Loop approach in sync with continual learning to build better models in production. Continual learning technology enables models to auto-adapt and retrain with new data, closing the ML loop. 

Webinar: MLOps for Production-ready Machine Learning
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MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. 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.

Webinar: CI/CD for Machine Learning
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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.

Workshop: Deploy your machine learning models to production with Kubernetes
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Deploying machine learning models to production is a key pain point for data scientist. Ensuring a stable deployment is often not a one step task, and requires DevOps operations and IT time. 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. Leah will use a real-time example to show you deployment best practices and how to ensure your model is running with the ability to scale in production.

Webinar: Build Auto-Adaptive Machine Learning Models with Kubernetes
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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. 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.

Webinar: How to run Spark on Kubernetes like a pro
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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. 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. 

Webinar: How to better manage your data science team’s workflow
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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. 

Online Workshop: How to set up Kubernetes for all your machine learning workflows
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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. 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.

Webinar: How to use continual learning in your ML models
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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.

Webinar: Build your own AutoML computer vision pipeline
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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.