AWS Machine Learning Blog

JANUARY 26, 2024

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. All the principles of the AWS Shared Responsibility Model are applicable to generative AI solutions.

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AWS Machine Learning Blog

OCTOBER 18, 2023

The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. Solution overview Predicting animal breeds from an image needs custom ML models. It is already trained on tens of millions of images across many categories.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Prompt engineering has become an essential skill for anyone working with large language models (LLMs) to generate high-quality and relevant texts. Although text prompt engineering has been widely discussed, visual prompt engineering is an emerging field that requires attention.

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Heartbeat

SEPTEMBER 18, 2023

Data scientists and ML engineers rely heavily on these platforms because they provide the necessary resources to tackle large-scale and challenging ML problems. This key feature enables data scientists and engineers to work seamlessly with their preferred ML tools and frameworks, ensuring a familiar and efficient working environment.

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AWS Machine Learning Blog

MAY 5, 2023

To create this app, they need a high-quality dataset containing clothing images, labeled with different categories. You can use Ground Truth and FiftyOne to accelerate your data labeling project. We add a FiftyOne classification label to each sample with the field name article_type, populated by the image’s top-level article category.

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AWS Machine Learning Blog

MARCH 27, 2024

In this post, we guide you through the process of fine-tuning the Amazon Titan Image Generator model to learn two new categories: Ron the dog and Smila the cat, our favorite pets. That’s why it’s good practice to check if you actually need to fine-tune your model for your use case or if prompt engineering is sufficient.

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Solution overview By adopting this solution, you can gain the following benefits: Improved information access – RAG allows models to pull in information from vast external sources, which can be especially useful when the pre-trained model’s knowledge is outdated or incomplete. We have provided this demo in the GitHub repo.

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Heartbeat

JANUARY 29, 2024

For instance, in few-shot image classification, a MANN, having learned representations of different image attributes from diverse tasks, can promptly adapt to a new object category with only a handful of examples. We will import key libraries and modules to lay the foundation for building our meta-learning solution. & Drissi Y.

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AWS Machine Learning Blog

OCTOBER 20, 2023

This framework can help accelerate ML adoption across the organization. This framework’s federated approach allows the central platform engineering team to set some high-level policies and standards, but also gives LOB teams flexibility to adapt based on local needs. Enterprise customers with a moderate to high maturity in ML.

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AWS Machine Learning Blog

JUNE 27, 2023

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. Machine Learning Operations (MLOps) provides the technical solution to this issue, assisting organizations in managing, monitoring, deploying, and governing their models on a centralized platform. Endpoints had to be deployed manually as well.

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AWS Machine Learning Blog

MARCH 15, 2024

However, existing solutions can often fall into two categories: rule-based systems that demand substantial time and effort for setup and upkeep, or rigid systems that lack the flexibility required for human-like interactions with customers. Solution overview The following diagram illustrates our solution architecture.

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AWS Machine Learning Blog

MAY 9, 2024

The software as a service (SaaS) platform offers out-of-the-box solutions for life, annuity, employee benefits, and institutional annuity providers. Verisk has embraced this technology and has developed their own Instant Insight Engine, or AI companion, that provides an enhanced self-service capability to their FAST platform.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Scale and accelerate the impact of AI There are several steps to building and deploying a foundational model (FM). These can broadly be described in two categories: Time/cost to deploy: Each deployment consists of several layers of hardware and software that need to be installed, configured and tested prior to deployment.

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AWS Machine Learning Blog

DECEMBER 22, 2023

This blog is co-written with Josh Reini, Shayak Sen and Anupam Datta from TruEra Amazon SageMaker JumpStart provides a variety of pretrained foundation models such as Llama-2 and Mistal 7B that can be quickly deployed to an endpoint. on(Select.Record.calls[0].args.args[0]).on_output() args.args[0]).on_output() on(Select.Record.calls[0].args.args[0]).on_output()

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Building a production-ready solution in the cloud involves a series of trade-off between resources, time, customer expectation, and business outcome. This post focuses on the Cost Optimization pillar of the IDP solution. When you’re ready for production, always monitor and analyze the spending.

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Marktechpost

MAY 10, 2024

Minimal coding expertise is all that’s needed to make use of Low-Code and No-Code solutions. The skilled professionals at MakeML will assist you in developing a Computer Vision solution and incorporating it into your product. Train models more quickly on your Mac by using the hardware accelerators.

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AWS Machine Learning Blog

JANUARY 6, 2023

The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. The tools are typically used by data scientists and ML developers from experimentation to production deployment of AI and ML solutions. AWS position.

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AWS Machine Learning Blog

APRIL 19, 2024

We use Amazon EKS and were looking for the best solution to auto scale our worker nodes. Solution overview In this section, we present a generic architecture that is similar to the one we use for our own workloads, which allows elastic deployment of models using efficient auto scaling based on custom metrics.

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AWS Machine Learning Blog

DECEMBER 1, 2023

From data collection and cleaning to feature engineering, model building, tuning, and deployment, ML projects often take months for developers to complete. These tools allow Deloitte to develop ML solutions without needing to hand-code models and pipelines. And experienced data scientists can be hard to come by.

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The MLOps Blog

OCTOBER 3, 2023

How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right? And later, an MLOps engineer. How did you manage to jump from a more analytical, scientific type of role to a more engineering one? Quite fun, quite chaotic at times. You have to follow your interests.

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AWS Machine Learning Blog

FEBRUARY 5, 2024

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Loss of habitat – The degradation of natural habitats, particularly through deforestation, can accelerate zoonotic spillover events. health examines these as well.

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The MLOps Blog

JUNE 27, 2023

MLOps landscape One of the defining characteristics of the MLOps landscape in 2023 is the coexistence of both open-source and closed-source solutions. Open-source tools have gained significant traction due to their flexibility, community support, and adaptability to various workflows. For example, neptune.ai

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AWS Machine Learning Blog

MARCH 1, 2023

Solution overview Amazon Comprehend is a fully managed service that uses natural language processing (NLP) to extract insights about the content of documents. MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle.

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Snorkel AI

FEBRUARY 21, 2024

Through their chosen training data, data scientists and machine learning engineers can modulate the model’s response to perform better on a particular topic or task or to render responses in a particular tone. From their blog post , all you need is to add the following lines to your code to integrate PEFT into your finetuning workflow.

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Snorkel AI

FEBRUARY 21, 2024

Through their chosen training data, data scientists and machine learning engineers can modulate the model’s response to perform better on a particular topic or task or to render responses in a particular tone. From their blog post , all you need is to add the following lines to your code to integrate PEFT into your finetuning workflow.

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Data Scientist LLM Large Language Models Machine Learning 52

Marktechpost

DECEMBER 10, 2023

Minimal coding expertise is all that’s needed to make use of Low-Code and No-Code solutions. The skilled professionals at MakeML will assist you in developing a Computer Vision solution and incorporating it into your product. Train models more quickly on your Mac by using the hardware accelerators.

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AWS Machine Learning Blog

MARCH 29, 2023

Medium-density fiberboard (MDF) is an engineered wood product made by breaking down wood residuals into fibers, combining it with adhesives, and forming it into panels by applying high temperature and pressure. It has many application areas such as furniture, cabinetry, and flooring. Two types of data sources exist for this use case.

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AWS Machine Learning Blog

MAY 31, 2023

Triton supports various backends as engines to power the running and serving of different framework models , like PyTorch, TensorFlow, TensorRT, or ONNX Runtime. For demonstration purposes, we use a pre-trained ResNet50 model that can classify images into 1,000 categories. 4xlarge instance). Be sure to try it out!

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AWS Machine Learning Blog

MAY 9, 2023

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. Triton supports various backends as engines to support the running and serving of various ML models for inference. Of these options, one of the key features that SageMaker provides is real-time inference.

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AWS Machine Learning Blog

MAY 17, 2023

In this post, aimed primarily at those who are already using Snowflake, we explain how you can import both training and validation data for a facies classification task from Snowflake into Amazon SageMaker Canvas and subsequently train the model using a 3+ category prediction model. Leave Model type as 3+ category prediction.

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AWS Machine Learning Blog

MAY 2, 2023

With the ability to solve various problems such as classification and regression, XGBoost has become a popular option that also falls into the category of tree-based models. Triton supports various backends as engines to support the running and serving of various ML models for inference. astype(str).tolist() astype("float32").tolist()

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ML Auto-classification Python Machine Learning 64

Snorkel AI

MARCH 1, 2023

This differs from previous generations of machine learning architectures, which fell into two categories: supervised and unsupervised. In early 2023, Microsoft began integrating ChatGPT into the Bing search engine. In either case, the masked portion of the original data becomes the “label” and the non-masked portion becomes the input.

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The MLOps Blog

MAY 10, 2023

The model registry is a category of tools that solve this issue for you. Saving deep learning model with Pytorch Developed by Facebook, Pytorch is one of the highly used frameworks for developing DL-based solutions. When storing a model, you normally choose some sort of storage from where you can fetch your model and use it anytime.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

And global services and solutions providers like Accenture are reaping the benefits of customized generative AI applications as they empower their in-house developers with Amazon CodeWhisperer. Customers like Adobe, Deutsche Telekom, and Leonardo.ai have seen great early results and are excited to deploy their models at scale on Inf2.

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Bugra Akyildiz

MARCH 2, 2024

Articles BAIR(Berkeley AI Research) wrote rather an interesting blog post. Their main argument is that; the LLMs are not the solution to our problems, but rather “compound systems” that bring models to other systems(guardrail, etc) to solve a particular user/product problem.

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AWS Machine Learning Blog

JULY 20, 2023

The characteristics of these images make them useful for certain tasks like evaluating forest density and macro-category or finding buildings. This solution also doesn’t differ much from the previous ones in terms of services used and flow. The following are some examples of point cloud images.

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AWS Machine Learning Blog

JANUARY 17, 2024

Solution overview In this blog, we will walk through the following scenarios : Deploy Llama 2 on AWS Inferentia instances in both the Amazon SageMaker Studio UI, with a one-click deployment experience, and the SageMaker Python SDK. The configurations of pre-compiled graphs are listed in the following table.

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