AWS Machine Learning Blog

JUNE 13, 2023

AWS Inferentia2 was designed from the ground up to deliver higher performance while lowering the cost of LLMs and generative AI inference. In this post, we show how the second generation of AWS Inferentia builds on the capabilities introduced with AWS Inferentia1 and meets the unique demands of deploying and running LLMs and FMs.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity AI are going all in on AWS for generative AI.

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AWS Machine Learning Blog

MAY 10, 2023

Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter. In parallel to these open-source contributions, we have AWS product teams who are working to integrate Jupyter with products such as Amazon SageMaker.

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AWS Machine Learning Blog

DECEMBER 18, 2024

Fastweb , one of Italys leading telecommunications operators, recognized the immense potential of AI technologies early on and began investing in this area in 2019. Fine-tuning Mistral 7B on AWS Fastweb recognized the importance of developing language models tailored to the Italian language and culture.

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AWS Machine Learning Blog

AUGUST 7, 2023

In an effort to create and maintain a socially responsible gaming environment, AWS Professional Services was asked to build a mechanism that detects inappropriate language (toxic speech) within online gaming player interactions. Unfortunately, as in the real world, not all players communicate appropriately and respectfully.

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Data Science 101

NOVEMBER 7, 2019

It is now possible to deploy an Azure SQL Database to a virtual machine running on Amazon Web Services (AWS) and manage it from Azure. Python support has been available for a while. It’s true, I saw it happen this week. R Support for Azure Machine Learning.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

Note that you can also use Knowledge Bases for Amazon Bedrock service APIs and the AWS Command Line Interface (AWS CLI) to programmatically create a knowledge base. Create a Lambda function This Lambda function is deployed using an AWS CloudFormation template available in the GitHub repo under the /cfn folder.

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Data Science 101

NOVEMBER 22, 2019

AWS Storage Day On November 20, 2019, Amazon held AWS Storage Day. Many announcements came out regarding storage of all types at AWS. Much of this is in anticipation of AWS re:Invent, coming in early December 2019. Much of this is in anticipation of AWS re:Invent, coming in early December 2019.

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AWS Machine Learning Blog

OCTOBER 2, 2023

On top of that, the whole process can be configured and managed via the AWS SDK, which is what we use to orchestrate our labeling workflow as part of our CI/CD pipeline. For more information about best practices, refer to the AWS re:Invent 2019 talk, Build accurate training datasets with Amazon SageMaker Ground Truth.

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AWS Machine Learning Blog

MARCH 28, 2024

For more information on Mixtral-8x7B Instruct on AWS, refer to Mixtral-8x7B is now available in Amazon SageMaker JumpStart. LangChain is an open source Python library designed to build applications with LLMs. Before you get started with the solution, create an AWS account. This identity is called the AWS account root user.

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

“Data locked away in text, audio, social media, and other unstructured sources can be a competitive advantage for firms that figure out how to use it“ Only 18% of organizations in a 2019 survey by Deloitte reported being able to take advantage of unstructured data. The majority of data, between 80% and 90%, is unstructured data.

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AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. For a more detailed explanation, refer to Time Series Classification and Clustering with Python. Chong En Lim is a Solutions Architect at AWS.

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ODSC - Open Data Science

AUGUST 4, 2023

Recently, we spoke with Emily Webber, Principal Machine Learning Specialist Solutions Architect at AWS. She’s the author of “Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS.” And then I spent many years working with customers.

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AWS Machine Learning Blog

JULY 29, 2024

Launched in August 2019, Forecast predates Amazon SageMaker Canvas , a popular low-code no-code AWS tool for building, customizing, and deploying ML models, including time series forecasting models. Python script – Use a Python script to merge the datasets. SageMaker Canvas can be accessed from the SageMaker console.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. You can create a custom transform using Pandas, PySpark, Python user-defined functions, and SQL PySpark. Choose Python (PySpark) for this use-case. And select Python (PySpark).

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AWS Machine Learning Blog

APRIL 19, 2023

Right now, most deep learning frameworks are built for Python, but this neglects the large number of Java developers and developers who have existing Java code bases they want to integrate the increasingly powerful capabilities of deep learning into. The DJL was created at Amazon and open-sourced in 2019.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Amazon Bedrock Knowledge Bases offers a streamlined approach to implement RAG on AWS, providing a fully managed solution for connecting FMs to custom data sources. LangChain is an open source Python library designed to build applications with LLMs. Amazon Bedrock makes this effortless by providing standardized API access to many FMs.

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Towards AI

APRIL 19, 2024

Amazon (AWS) 👉Industry domain: Online retail and web services provider 👉Location: Over 175 Amazon fulfillment centers globally 👉Year founded: 1994 👉Key Products developed: Amazon Bedrock, Q, Code Whisperer, Sage Maker 👉Benefits: Fully managed generative AI service options, AWS free tier for experimentation 7.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Engineers must manually write custom data preprocessing and aggregation logic in Python or Spark for each use case. Prerequisites To follow this tutorial, you need the following: An AWS account. AWS Identity and Access Management (IAM) permissions. This undifferentiated heavy lifting is cumbersome, repetitive, and error-prone.

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ODSC - Open Data Science

FEBRUARY 17, 2023

BERT is still very popular over the past few years and even though the last update from Google was in late 2019 it is still widely deployed. NLP Programming Languages It shouldn’t be a surprise that Python has a strong lead as a programming language of choice for NLP. Knowing some SQL is also essential.

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Flipboard

FEBRUARY 2, 2023

GluonTS is a Python package for probabilistic time series modeling, but the SBP distribution is not specific to time series, and we were able to repurpose it for regression. Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season. We used the SBP distribution provided by GluonTS.

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IBM Journey to AI blog

JANUARY 10, 2023

This data will be analyzed using Netezza SQL and Python code to determine if the flight delays for the first half of 2022 have increased over flight delays compared to earlier periods of time within the current data (January 2019 – December 2021). Figure 5 – Bar graph of current flight delay data (2019 – June 2022).

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phData

JANUARY 31, 2024

Python has long been the favorite programming language of data scientists. Historically, Python was only supported via a connector, so making predictions on our energy data using an algorithm created in Python would require moving data out of our Snowflake environment.

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Mlearning.ai

JUNE 14, 2023

YouTube Introduction to Sequence Learning and Attention Mechanisms Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 — Translation, Seq2Seq, Attention — YouTube Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 — Translation, Seq2Seq, Attention — YouTube 2.

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Mlearning.ai

JUNE 28, 2023

But I have to say that this data is of great quality because we already converted it from messy data into the Python dictionary format that matches our type of work. This is the highest accuracy achieved by fine-tuning the model on AWS SageMaker with the training data of 30,000 sentences between sentences 40,000 and 70,000.

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Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). OpenAI’s GPT-2, finalized in 2019 at 1.5 The plot was boring and the acting was awful: Negative This movie was okay. For example: I love this movie.

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Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). OpenAI’s GPT-2, finalized in 2019 at 1.5 The plot was boring and the acting was awful: Negative This movie was okay. For example: I love this movie.

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Explosion

AUGUST 1, 2019

Based on the (fairly vague) marketing copy, AWS might be doing something similar in SageMaker. 2019) have shown that a transformer models trained on only 1% of the IMDB sentiment analysis data (just a few dozen examples) can exceed the pre-2016 state-of-the-art. Modern transfer learning techniques are bearing this out.

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The MLOps Blog

AUGUST 3, 2023

For example, let’s take Airflow , AWS SageMaker pipelines. What we’re targeting first is helping you replace that procedural Python code with Hamilton code that you describe, which I can go into detail a little bit more. You could almost think of Hamilton as DBT for Python functions. Where does it [DAGWorks] fit?

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phData

MAY 22, 2023

The code we will use here is Python , but you can use your programming language of choice (assuming compatibility) to call the API. This appears as a “None” value in the Python programming language. If you are using a different language, be sure to verify the NULL equivalent of that specific programming language.

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AWS Machine Learning Blog

APRIL 11, 2023

It’s a fully managed on-demand service, integrated with SageMaker and other AWS services, and therefore creates and manages resources for you. Furthermore, Pipelines is supported by the SageMaker Python SDK , letting you track your data lineage and reuse steps by caching them to ease development time and cost.

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AWS Machine Learning Blog

MARCH 30, 2023

Please use below python code to curate interactions dataset from the MovieLens public dataset. Choose the new aws-trending-now recipe. For Solution version ID , choose the solution version that uses the aws-trending-now recipe. For the interactions data, we use ratings history from the movies review dataset, MovieLens.

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AWS Machine Learning Blog

FEBRUARY 5, 2024

According to health organizations such as the Centers for Disease Control and Prevention ( CDC ) and the World Health Organization ( WHO ), a spillover event at a wet market in Wuhan, China most likely caused the coronavirus disease 2019 (COVID-19). Janosch Woschitz is a Senior Solutions Architect at AWS, specializing in geospatial AI/ML.

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Data Science Dojo

DECEMBER 31, 2023

Following earlier collaborations in 2019 and 2021, this agreement focused on boosting AI supercomputing capabilities and research. AWS launched Bedrock Amazon Web Services unveiled its groundbreaking service, Bedrock. Microsoft increased investments in supercomputing systems and expanded Azure’s AI infrastructure. OpenAI released Dall.

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AWS Machine Learning Blog

OCTOBER 11, 2024

You can set up the notebook in any AWS Region where Amazon Bedrock Knowledge Bases is available. You also need an AWS Identity and Access Management (IAM) role assigned to the SageMaker Studio domain. data # Assing local directory path to a python variable local_data_path = "./data/" On the Domains page, open your domain.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you’ll need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. We then also cover how to fine-tune the model using SageMaker Python SDK.

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