AWS Server Migration Service Reviews

Jupyter Reviews

AWS Server Migration Service mentions (6)

• Future AI Deployment: Automating Full Lifecycle Management with Rollback Strategies and Cloud Migration

  AWS Application Migration Service - Replication and Failover: AWS Application Migration Service. - Source: dev.to / 2 months ago

• Microsoft Exchange Migration

  Check out AWS Migration Service: https://aws.amazon.com/server-migration-service/. Source: about 2 years ago

• {Help]Moving local linux PC to VM in the cloud

  Https://aws.amazon.com/server-migration-service/ claims to do what you want I know of a similar tool from vmware but it's one of those things that should be easy but isnt because the cloud sometimes bleed abstractions that makes it hard to simply do a lift and shift. Source: almost 3 years ago

• AWS Migration

  Server Migration Service - install an agent on the source VM, and it'll replicate all data and ongoing changes into an EC2 instance that can then be launched in AWS: https://aws.amazon.com/server-migration-service/. Source: over 3 years ago

• Complete Cloud Migration Guide

  AWS Server Migration Service — This service makes it easy and quick to move workloads to AWS, particularly when dealing with large-scale server migrations. - Source: dev.to / over 3 years ago

Jupyter mentions (216)

• The 3 Best Python Frameworks To Build UIs for AI Apps

  Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago

• LangChain: From Chains to Threads

  LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago

• Applied Artificial Intelligence & its role in an AGI World

  Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago

• Jupyter Notebook for Java

  Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago

• JIRA Analytics with Pandas

  One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago