AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. Forethought is a leading generative AI suite for customer service. The following diagram illustrates our legacy architecture. 2xlarge instances.

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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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Towards AI

APRIL 27, 2023

Last Updated on May 2, 2023 by Editorial Team Author(s): Puneet Jindal Originally published on Towards AI. garbage in garbage out for AI model accuracy….blah By this time, it's already months or years of efforts that have gone by without concrete results where AI is working at scale with its impact driving the bottom or top line.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Optimizing the artificial intelligence and machine learning (AI/ML) data preparation workload on AWS with sustainability best practices helps reduce the carbon footprint and the cost. While data preparation isn’t unique to AI/ML workloads, the model training and tuning workflow is specific to AI/ML.

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Flipboard

MARCH 13, 2023

We orchestrate our ML training and deployment pipelines using Amazon Managed Workflows for Apache Airflow (Amazon MWAA), which enables us to focus more on programmatically authoring workflows and pipelines without having to worry about auto scaling or infrastructure maintenance. Applied AI Specialist Architect at AWS.

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DataRobot Blog

JANUARY 10, 2023

Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. The new DataRobot Notebooks offering plays a crucial role in providing a collaborative environment for AI builders to use a code-first approach to accelerate one of the most time-consuming parts of the machine learning lifecycle.

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The MLOps Blog

JUNE 27, 2023

Google Cloud Vertex AI Google Cloud Vertex AI provides a unified environment for both automated model development with AutoML and custom model training using popular frameworks. Can you see the complete model lineage with data/models/experiments used downstream? Is it fast and reliable enough for your workflow?

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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AWS Machine Learning Blog

MARCH 1, 2023

The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately. We define another pipeline step, step_cond.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code. In this post, we discuss the benefits of using Ray and Amazon SageMaker for distributed ML, and provide a step-by-step guide on how to use these frameworks to build and deploy a scalable ML workflow.

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TheSequence

MAY 24, 2023

data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ). Were there any research breakthroughs in StarCoder, or would you say it was more of a crafty ML engineering effort? In addition we labelled a PII dataset for code to train a PII detector.

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Pickl AI

JANUARY 23, 2023

Provides modularity as a series of completely configurable, independent modules that can be combined with the fewest restrictions possible. It is developed by Facebook’s AI Research Lab (FAIR) and authored by Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan. Pros It’s very efficient to perform auto ML along with H2O.

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The MLOps Blog

JANUARY 23, 2023

I believe the team will look something like this: Software delivery reliability: DevOps engineers and SREs ( DevOps vs SRE here ) ML-specific software: software engineers and data scientists Non-ML-specific software: software engineers Product: product people and subject matter experts Wait, where is the MLOps engineer?

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The MLOps Blog

JULY 18, 2023

Every episode is focused on one specific ML topic, and during this one, we talked to Jason Falks about deploying conversational AI products to production. Today, we have Jason Flaks with us, and we’ll be talking about deploying conversational AI products to production. What is conversational AI?

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Use case governance is essential to help ensure that AI systems are developed and used in ways that respect values, rights, and regulations. According to the EU AI Act, use case governance refers to the process of overseeing and managing the development, deployment, and use of AI systems in specific contexts or applications.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

This post is co-written with Deepali Rajale from Karini AI. Karini AI , a leading generative AI foundation platform built on AWS, empowers customers to quickly build secure, high-quality generative AI apps. Karini AI offers no-code solutions for creating Generative AI applications using RAG.

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AWS Machine Learning Blog

JULY 23, 2024

collection of multilingual large language models (LLMs), which includes pre-trained and instruction tuned generative AI models in 8B, 70B, and 405B sizes, is available through Amazon SageMaker JumpStart to deploy for inference. is an auto-regressive language model that uses an optimized transformer architecture. The Llama 3.1

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The MLOps Blog

DECEMBER 29, 2022

People will auto-scale up to 10 GPUs to handle the traffic. Does it mean that the production code has to be rewritten by, for example, ML engineers manually to be optimized for GPU with each update? Each of them may be with separate resource constraints, auto-scaling policies, and such. I would love to help better.

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Mlearning.ai

MAY 3, 2023

Large Language Models & Frameworks used — Overview Large language models or LLMs are AI algorithms trained on large text corpus, or multi-modal datasets, enabling them to understand and respond to human queries in a very natural human language way. chatgpt : ChatGPT is an AI chatbot developed by OpenAI and released in November 2022.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Organizations of every size and across every industry are looking to use generative AI to fundamentally transform the business landscape with reimagined customer experiences, increased employee productivity, new levels of creativity, and optimized business processes.

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Flipboard

NOVEMBER 27, 2024

In your AWS account, prepare a table using Amazon DataZone and Athena completing Step 1 through Step 8 in Amazon DataZone QuickStart with AWS Glue data. 1 MinContainers Minimum containers for auto scaling. 1 MaxContainers Maximum containers for auto scaling. An email address must be included while creating the user.

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AWS Machine Learning Blog

OCTOBER 24, 2024

After the model has completed training, you will be routed to the Analyze tab. Note that your numbers might differ from the ones you see in the following figure, because of the stochastic nature of the ML process. You’ll see the following after the batch prediction is complete. Now the model is being created.

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AWS Machine Learning Blog

DECEMBER 2, 2024

The generative AI landscape has been rapidly evolving, with large language models (LLMs) at the forefront of this transformation. As LLMs continue to expand, AI engineers face increasing challenges in deploying and scaling these models efficiently for inference. During our performance testing we were able to load the llama-3.1-70B

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