Amazon SageMaker Reviews and details

Social recommendations and mentions

• 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 / 14 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 / about 1 month 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 / 4 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

• Live object recognition system using Kinesis and SageMaker

  In this blog, you will see how to ingest audio/video feeds from any live recording camera into the AWS Kinesis Video Stream and apply various machine learning algorithms on the video stream to analyze these video feeds using Amazon SageMaker. We'll also use a few other services like Lambda, S3, EC2, IAM, ECS, CloudFormation, ECR, CloudWatch, etc. To power our model. Source: about 1 year ago

• Instance type or cost for an NLP server?

  First, can you use a different AWS service, such as Comprehend or SageMaker? You only "pay for what you use" instead of paying for an idle server. This is especially helpful for a start up, since you don't pay a lot if you don't have a lot of customers.. Source: about 1 year ago

• Can we all say thank you to AUTOMATIC1111 real quick? Not the app, but the person. And NKMD and all the other open source developers who are constantly working hard to give us theses amazing free AI tools. With constant updates and tons of hard work, all for FREE, they deserve it!

  We’re thrilled to announce that Stability AI has selected AWS as its preferred cloud provider to power its state-of-the-art AI models for image, language, audio, video, and 3D content generation. Stability AI is a community-driven, open-source artificial intelligence (AI) company developing breakthrough technologies. With Amazon SageMaker, Stability AI will build AI models on compute clusters with thousands of GPU... Source: about 1 year ago

• What do I use if I want to use my own Linux server?

  Are you looking for Machine Learning purposes? If so you probably actually want something like SageMaker. Source: over 1 year ago

• ML experiment tracking with DagsHub, MLFlow, and DVC

  In practice, having some infrastructural setup, which can be referred to as a “workbench,” within the development pipeline is the way to go. It structures the workflow, but this is easier said than done. Although some cloud platforms have provided various out-of-the-box workbench platforms/services (like Vertex AI, Sagemaker, AzureML) ready for use, it doesn’t always cover all the use cases. - Source: dev.to / over 1 year ago

• AWS NLP newsletter November 2022

  Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker . Learn how to train a 30B parameter GPT-2 model on SageMaker with ease with Sharded data parallelism on Amazon SageMaker, a new memory-saving distributed training technique in the SageMaker model parallel (SMP) library. Sharded data parallelism is purpose-built for extreme-scale models and uses Amazon in-house MiCS... - Source: dev.to / over 1 year ago

• Using AWS for Text Classification Part-1

  Amazon SageMaker makes it easy to train and optimize an unsupervised subword embedding model on your own corpus of domain-specific text data with the built-in BlazingText algorithm. We can also download existing general-purpose models trained on large datasets of online text, such as the following English language models available directly from fastText. From your SageMaker notebook instance, simply run the... - Source: dev.to / over 1 year ago

• Running R in Jupyer notebook

  If it doesn't have to be local and you can live with colab, you can use R runtime there too - https://colab.research.google.com/#create=true&language=r , it installs dependencies quite fast. Or perhaps https://aws.amazon.com/sagemaker/ . Or binder - https://github.com/binder-examples/r. Source: over 1 year ago

• How to Build Your Own GPT-J Playground

  Awesome! The other blog article goes into a lot more detail about the underlying technology. I wonder how much you should expect to pay for SageMaker with this setup? Source: almost 2 years ago

• Advantages of Using AWS as Your Cloud Platform in Real World

  Amazon SageMaker – a cloud machine learning program that develops, trains, and sends AI models on a scale. Source: almost 2 years ago

• Parsing logs from multiple data sources with Ahana and Cube

  We will use Ahana to join Amazon Sagemaker Endpoint logs stored as JSON files in S3 with the data retrieved from a PostgreSQL database. - Source: dev.to / almost 2 years ago

• [D] Build, train and track machine learning models using Superwise and Layer

  This illustrates how you can use Layer and Amazon SageMaker to deploy a machine learning model and track it using Superwise. Amazon SageMaker enables you to build, train and deploy machine learning models. Source: almost 2 years ago

• Recommended hardware and infrastructure for startup doing ML research

  Researchers generally do high-computational projects on university clusters and supercomputers. Companies tend to use cloud services like GCP or AWS. For example, you could look into training/courses for Amazon Sagemaker. Be careful with the cloud options though, because you appear to be in the medical field and data protection agreements generally limit/forbid such options. So make sure you have permission and... Source: about 2 years ago

• Databricks 2022 vs Databricks 2025

  I'm biased, but giving my honest personal opinion here, I think this sounds like a bad idea. I'm not optimistic about Databricks long term. They are a data prep company masquerading as a data science company. Nothing wrong with that, but Spark resources are expensive compared with SQL, and they are at risk from all fronts (Cloud providers, Snowflake, AI/ML platform players, etc.). I see their Databricks controlled... Source: about 2 years ago

• Ask HN: Building vision systems without knowing ML?

  In the past, I've worked on tons of personal projects where I was using Google's Video Intelligence API (https://cloud.google.com/video-intelligence) to track people, objects, and more after recording stuff live on a Raspberry Pi and sending it up to Google. Works great for personal projects (I get 1k minutes free, etc) but do people actually use these APIs in production? $0.15 / min for a single model feels... - Source: Hacker News / about 2 years ago

• How to load data from S3 to AWS SageMaker

  AWS Sagemaker is a great way to analyse data in the cloud and train machine learning models. Most convenient way to store data for machine learning abd analysis is S3 bucket, which could contain any types of data, like csv, pickle, zip or photos and videos. - Source: dev.to / over 2 years ago