Amazon SageMaker VS NumPy

Amazon SageMaker Reviews

NumPy Reviews

Amazon SageMaker mentions (36)

• Observations on MLOps–A Fragmented Mosaic of Mismatched Expectations

  Damn straight. Oh, wait, some vendors have claimed to build an end-to-end solution. But, meh, that’s marketing talk. Take, for example, a well-known platform like Amazon Sagemaker, which describes itself as “a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case.” It’s a great platform. My startup has even partnered with them.... - Source: dev.to / 7 days ago

• Sentiment Analysis with PubNub Functions and HuggingFace

  At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides... - Source: dev.to / 29 days ago

• Beginning the Journey into ML, AI and GenAI on AWS

  Generative Artificial Intelligence (GenAI) is a type of artificial intelligence that can generate text, images, or other media using generative models. AWS offers a range of services for building and scaling generative AI applications, including Amazon SageMaker, Amazon Rekognition, AWS DeepRacer, and Amazon Forecast. AWS has also invested in developing foundation models (FMs) for generative AI, which are... - Source: dev.to / 3 months ago

• Technical Architecture for LLMOps

  Amazon and Azure already have much of what you're talking about in AWS SageMaker and Azure MLOps. Source: 11 months ago

• Are AI fine-tuning tools worth learning and investing?

  And there have been several platforms that help fine-tune pretrained models, such as Google Cloud AutoML and Amazon Sagemaker. These tools are often fairly easy to use, but they come at a cost. They can be expensive, depending on the size of your dataset. Another option is Finetuner+, that also fine-tunes like AutoML and Sagemaker. The big advantage is that you don't need to transfer your data to other GPUs,... Source: about 1 year ago

NumPy mentions (107)

• Element-wise vs Matrix vs Dot multiplication

  In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago

• JSON in data science projects: tips & tricks

  Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / about 2 months ago

• Introducing Flama for Robust Machine Learning APIs

  Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago

• A Comprehensive Guide to NumPy Arrays

  Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago

• Beginning Python: Project Management With PDM

  A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago