Build an image data classification model with BigQuery ML
Step-by-step instructions for building an image classifier with ResNet, Cloud Storage and BQML.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Onboarding - Part 2
00:00 - Intro
01:29 - Remotely Executing Task
06:49 - Model Repository
09:10 - Workers and Queues
17:27 - Workers on K8s
19:14 - Pipelines
31:20 - Triggerscheduler
39:05 - Github CI/CD Templates
39:36 - Outro
MLOps for Generative AI with MLRun
The influx of new tools like ChatGPT spark the imagination and highlight the importance of Generative AI and foundation models as the basis for modern AI applications. However, the rise of generative AI also brings a new set of MLOps challenges. Challenges like handling massive amounts of data, large scale computation and memory, complex pipelines, transfer learning, extensive testing, monitoring, and so on. In this 9 minute demo video, we share MLOps orchestration best practices and explore open source technologies available to help tackle these challenges.
What are the Advantages of Automated Machine Learning Tools?
AutoML (Automated Machine Learning) helps organizations deploy Machine Learning (ML) models faster, by making the ML pipeline process more efficient and less error-prone. If you’re getting started with AutoML, this article will take you through the first steps you need to find a tool and get started. If you’re at an advanced stage, it will help you validate you’re on the right track.
Introducing ClearGPT from ClearML
The first secure, enterprise-grade generative AI platform We have an exciting announcement! On Thursday, May 18, we released ClearGPT, the first generative AI platform that transcends enterprise ChatGPT challenges. ClearGPT is the only secure, enterprise-grade platform offering state-of-the-art LLMs, tailored to your business data and running securely on your network, to power enterprise AI transformation. Prefer to watch the 2-minute video and see how it works? Watch now.
When are you ready for artificial intelligence and machine learning?
How to chart a roadmap to the pinnacle of data science.
Machine-learning life-cycle management using MLflow
Are you looking to streamline your machine learning projects from start to finish? Look no further than MLflow! In this blog, we'll discuss how to use MLflow to manage the entire lifecycle of your ML project – from creating and training models to comparing results and deploying them.
Live: Replacing PySpark with Snowpark - Sentiment Analysis [Demo]
Join Snowflake's Nick Akincilar, Phani Raj, and host Felipe Hoffa for a live conversation and demo showcasing the use of Snowpark in place of PySpark for data engineering and machine learning.
ODSC East 2023 MLOps Keynote: MLOps in the Era of Generative AI
ChatGPT sparks the imagination and highlights the importance of Generative AI and foundation models as the basis for modern AI applications. However, this also brings a new set of AI operationalization challenges. Challenges like handling massive amounts of data, large scale computation and memory, complex pipelines, transfer learning, extensive testing, monitoring, and so on. In this talk, we explore the new technologies and share MLOps orchestration best practices that will enable you to automate the continuous integration and deployment (CI/CD) of foundation models and transformers, along with the application logic, in production.
Introducing ClearGPT
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