AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. AWS CDK scripts.

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AWS Machine Learning Blog

OCTOBER 27, 2023

Developers can use Amazon Personalize to build applications powered by the same type of machine learning (ML) technology used by Amazon.com for real-time personalized recommendations. With Amazon Personalize, developers can improve user engagement through personalized product and content recommendations with no ML expertise required.

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AWS Machine Learning Blog

APRIL 7, 2023

As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers to reduce latency and increase responsiveness of their applications. Even ground and aerial robotics can use ML to unlock safer, more autonomous operations.

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Mlearning.ai

JULY 7, 2023

That being said, I humbly acknowledge that creating a software application is a very complex process and me not being a software developer have only explored this at a surface level. All nuances of software development remain unexplored. Storing the data and metadata correctly is critical.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017. Then import the relevant modules.

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AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. The quality of our labels will affect the quality of our ML model. The output of a SageMaker Ground Truth labeling job is a file in JSON-lines format containing the labels and additional metadata.

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The MLOps Blog

MARCH 15, 2023

This includes the tools and techniques we used to streamline the ML model development and deployment processes, as well as the measures taken to monitor and maintain models in a production environment. Costs: Oftentimes, cost is the most important aspect of any ML model deployment. I would say the same happened in our case.

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The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers.

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The MLOps Blog

JANUARY 23, 2023

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”.

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Bugra Akyildiz

JUNE 11, 2023

Google built a new system called DIDACT (​​Dynamic Integrated Developer ACTivity), that trains a LLM for all of the software development activities. Libraries This repository includes datasets written by language models, used in their paper on "Discovering Language Model Behaviors with Model-Written Evaluations."

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deepsense.ai

JULY 30, 2023

These models offer tremendous potential but also bring a unique set of challenges when it comes to building large-scale ML projects. Naturally, training a machine learning model (regardless of the problem being solved or the particular model architecture that was chosen) is a key part of every ML project. But what happens next?

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AWS Machine Learning Blog

JUNE 8, 2023

This enables you to begin machine learning (ML) quickly. A SageMaker real-time inference endpoint enables fast, scalable deployment of ML models for predicting events. A session stores metadata and application-specific data known as session attributes. A session persists over time unless manually stopped or timed out.

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The MLOps Blog

APRIL 17, 2023

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use. Giving your data scientists a platform to track the progress of their ML projects. Experiment tracking is one such capability.

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AWS Machine Learning Blog

MARCH 12, 2024

Clone the GitHub project: cd ~/ && git clone [link] Navigate to the project folder to build the enclave_base Docker image that contains the Nitro Enclaves Software Development Kit (SDK) for cryptographic attestation documents from the Nitro Hypervisor (this step can take up to 15 minutes): cd /nitro_llm/enclave_base docker build./ -t

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AWS Machine Learning Blog

MAY 10, 2023

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry , enabling you to operationalize ML models in production. Build ML models and analyze their performance metrics.

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Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLOps aims to automate and operationalize ML models, enabling smoother transitions to production and deployment.

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The MLOps Blog

FEBRUARY 27, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects. Then we are there to help.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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AWS Machine Learning Blog

MAY 15, 2024

Next, we present the solution architecture and process flows for machine learning (ML) model building, deployment, and inferencing. Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). Burak Gozluklu is a Principal AI/ML Specialist Solutions Architect located in Boston, MA.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Combining accurate transcripts with Genesys CTR files, Principal could properly identify the speakers, categorize the calls into groups, analyze agent performance, identify upsell opportunities, and conduct additional machine learning (ML)-powered analytics.

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AWS Machine Learning Blog

MAY 15, 2023

Getir used Amazon Forecast , a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts, to increase revenue by four percent and reduce waste cost by 50 percent. As previously mentioned, CNN-QR can employ related time series and metadata about the items being forecasted.

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AWS Machine Learning Blog

AUGUST 16, 2023

MLOps is a key discipline that often oversees the path to productionizing machine learning (ML) models. MLOps tooling helps you repeatably and reliably build and simplify these processes into a workflow that is tailored for ML. It’s natural to focus on a single model that you want to train and deploy.

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AWS Machine Learning Blog

FEBRUARY 21, 2023

After you build, train, and evaluate your machine learning (ML) model to ensure it’s solving the intended business problem proposed, you want to deploy that model to enable decision-making in business operations. SageMaker deployment guardrails Guardrails are an essential part of software development.

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AWS Machine Learning Blog

MARCH 17, 2023

You can now use the Amazon Kendra connector for Microsoft Teams to index Microsoft Teams messages and documents, and search this content using intelligent search in Amazon Kendra, powered by machine learning (ML). He has over 20 years of IT experience in application development and software architecture.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

SageMaker pipeline SageMaker Pipelines offers a user-friendly Python SDK to create integrated machine learning (ML) workflows. With SageMaker Pipelines, you can streamline the end-to-end process of training and evaluating models, enhancing efficiency and reproducibility in your ML workflows.

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AWS Machine Learning Blog

AUGUST 4, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. You can build an ML model with SageMaker Autopilot representing all your data using the manifest file and use that for your ML inference and production deployment.

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AWS Machine Learning Blog

JULY 31, 2023

Machine learning (ML) methods can help identify suitable compounds at each stage in the drug discovery process, resulting in more streamlined drug prioritization and testing, saving billions in drug development costs (for more information, refer to AI in biopharma research: A time to focus and scale ). that runs run_alphafold.py

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AWS Machine Learning Blog

MAY 8, 2023

With Amazon SageMaker , you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. To automate the infrastructure deployment, we use the AWS Cloud Development Kit (AWS CDK).

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Flipboard

DECEMBER 6, 2023

Given the right context, metadata, and instructions, a well-selected general purpose LLM can produce good-quality SQL as long as it has access to the right domain-specific context. She leads machine learning (ML) projects in various domains such as computer vision, natural language processing and generative AI.

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AWS Machine Learning Blog

APRIL 19, 2024

of the SageMaker ACK Operators adds support for inference components , which until now were only available through the SageMaker API and the AWS Software Development Kits (SDKs). These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues simply by using the Kubernetes API.

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O'Reilly Media

JULY 25, 2023

Getting Started: Setup Trials and Tribulations Here’s the exact prompt I used to start my conversation with ChatGPT using GPT-4: Act as a software developer to help me build something that will play music from a time period that matches when an academic paper I am reading in the browser was written.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

Model server overview A model server is a software component that provides a runtime environment for deploying and serving machine learning (ML) models. The primary purpose of a model server is to allow effortless integration and efficient deployment of ML models into production systems. For MMEs, each model.py The full model.py

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Flipboard

NOVEMBER 24, 2023

This emergent ability in LLMs has compelled software developers to use LLMs as an automation and UX enhancement tool that transforms natural language to a domain-specific language (DSL): system instructions, API requests, code artifacts, and more. Clay Elmore is an AI/ML Specialist Solutions Architect at AWS.

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AWS Machine Learning Blog

AUGUST 8, 2023

These containers support common machine leaning (ML) frameworks (like TensorFlow, ONNX, and PyTorch, as well as custom model formats) and useful environment variables that let you optimize performance on SageMaker. Uri Rosenberg is the AI & ML Specialist Technical Manager for Europe, Middle East, and Africa.

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