AWS Machine Learning Blog

NOVEMBER 16, 2023

One of the key drivers of Philips’ innovation strategy is artificial intelligence (AI), which enables the creation of smart and personalized products and services that can improve health outcomes, enhance customer experience, and optimize operational efficiency. Once deployed, model performance is continuously monitored.

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AWS Machine Learning Blog

FEBRUARY 22, 2023

These tools integrate via API into Boomi’s core service offering. Boomi’s machine learning (ML)-powered solution facilitates the rapid development of integrations on their platform, and enables faster time to market for their customers. As the developer begins making progress on the blank canvas, their productivity is still low.

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AWS Machine Learning Blog

APRIL 3, 2024

Search engines and recommendation systems powered by generative AI can improve the product search experience exponentially by understanding natural language queries and returning more accurate results. Amazon SageMaker Studio – It is an integrated development environment (IDE) for machine learning (ML).

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Iguazio

DECEMBER 14, 2023

After reading the book, ML practitioners and leaders will know how to deploy their ML models to production and scale their AI initiatives, while overcoming the challenges many other businesses are facing. This led to wasted resources and time, and ultimately contributed to the failure of many data science projects.

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ODSC - Open Data Science

OCTOBER 15, 2023

Institute of Analytics The Institute of Analytics is a non-profit organization that provides data science and analytics courses, workshops, certifications, research, and development. The courses and workshops cover a wide range of topics, from basic data science concepts to advanced machine learning techniques.

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AWS Machine Learning Blog

MAY 25, 2023

This post was co-authored by Brian Curry (Founder and Head of Products at OCX Cognition) and Sandhya MN (Data Science Lead at InfoGain) OCX Cognition is a San Francisco Bay Area-based startup, offering a commercial B2B software as a service (SaaS) product called Spectrum AI. Finally, the code is run using Step Functions.

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Heartbeat

FEBRUARY 29, 2024

We’ll explore how LLMs reshape how we write code, their potential for increasing developer productivity, and what lies beyond LLMs in the ever-evolving programming world. Furthermore, the software development process has evolved to embrace Agile methodologies, DevOps practices, and continuous integration/continuous delivery (CI/CD) pipelines.

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ODSC - Open Data Science

JULY 18, 2023

Programmers use Microsoft Copilot because it allows them to boost their productivity and efficiency while writing code by generating code snippets, completing entire functions, and even providing contextual suggestions based on the code being written. Finally, these programs are great at basic coding but not advanced integration.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

They offer a large assortment of over 50,000 products across 1,000 brands, and are operating in more than 500 cities and towns. We were facing the following challenges to operate their existing setup: With the continuous introduction of new products, the computer vision model needed to continuously incorporate new product information.

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Flipboard

FEBRUARY 15, 2023

JumpStart APIs unlock the usage of JumpStart capabilities in your workflows, and integrate with tools such as the model registry that are part of MLOps pipelines and anywhere else you’re interacting with SageMaker via SDK. SageMaker projects are provisioned using AWS Service Catalog products.

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AWS Machine Learning Blog

APRIL 11, 2023

Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As a data scientist, you may want to showcase your findings for a dataset, or deploy a trained model. We use the SageMaker IDE to clone our example and the system terminal to launch our app.

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AWS Machine Learning Blog

JUNE 8, 2023

Data scientists need a consistent and reproducible environment for machine learning (ML) and data science workloads that enables managing dependencies and is secure. Studio provides an extensive list of first-party images with common frameworks and packages, such as Data Science, TensorFlow, PyTorch, and Spark.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. As with all IDE applications in SageMaker Studio, ML developers and engineers can select the underlying compute on demand, and swap it based on their needs without losing data.

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Hacker News

MARCH 9, 2023

Ken Van Haren and Chris Stanley were data scientists at Google and Square, respectively, who found themselves frustrated by how much time they were spending wrangling infrastructure versus doing actual data science. In surveying their colleagues, they found that it was a common problem. The Patterns platform.

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Alation

SEPTEMBER 28, 2022

In addition, automated processes are often more accurate and reliable than manual processes, which leads to fewer errors and data that can be trusted. Integrate Diverse Technologies. DataOps aims to integrate diverse technologies in order to provide a more comprehensive solution. DataOps Helps Improve Data Quality.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

Another example is independent software vendors (ISVs) that make their products and services available in different cloud platforms to benefit their end customers. Key concepts Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning. image and Python 3.0

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AWS Machine Learning Blog

APRIL 5, 2023

You can use CLIP to encode your products’ images or description into embeddings , and then store them into an OpenSearch Service k-NN index. Then your customers can query the index to retrieve products that they’re interested in. Generate embeddings of product images using a SageMaker batch transform job.

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Data Science 101

JANUARY 6, 2020

As 2020 begins, there has been limited cloud data science announcements so I put together some predictions. For example, Azure Arc now allows you to run Azure products on a kubernetes container running anywhere (even in Amazon Web Services or Google Cloud) and AWS Outposts runs AWS on-premise. Cloud Collaboration.

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Heartbeat

MARCH 6, 2023

Examples of applications of image classification include: Automated inspection and quality control in production and manufacturing industries. After installing R, install R Studio , the preferred IDE coding R projects. Sign up for free and easily track experiments, manage models in production, and visualize your model performance.

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Flipboard

NOVEMBER 24, 2023

The BMW Group – headquartered in Munich, Germany – is driven by 149,000 employees worldwide and manufactures in over 30 production and assembly facilities across 15 countries. In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making.

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

No need to switch Integrated development environments (IDEs). Continue writing code in your choice of IDE and invoke SageMaker training jobs. Continue writing code in your choice of IDE and invoke SageMaker training jobs. In this post, we use Amazon SageMaker Studio with the Data Science 3.0

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AWS Machine Learning Blog

APRIL 16, 2024

In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. In summary, we have transitioned towards a localized architecture.

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AWS Machine Learning Blog

APRIL 25, 2023

SageMaker Training also helps data scientists with advanced tools such as Amazon SageMaker Debugger and Profiler to debug and analyze their large-scale training jobs. It integrates with the recently announced SageMaker Python SDK feature for setting default values for parameters.

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O'Reilly Media

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. However, the concept is quite abstract. Who did what and when?

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AWS Machine Learning Blog

APRIL 2, 2024

Get started with Solar models in SageMaker JumpStart To get started with Solar models, you can use SageMaker JumpStart, a fully managed ML hub service to deploy pre-built ML models into a production-ready hosted environment. This model requires an AWS Marketplace subscription. For more information, see SageMaker JupyterLab.

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AssemblyAI

NOVEMBER 9, 2023

Product teams are integrating Text Summarization APIs and AI Summarization models into their AI-powered platforms to create summarization tools that automatically summarize calls, interviews, law documents, and more. Josh Seiden is a product consultant and author who has just released a book called Outcomes Over Output.

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AWS Machine Learning Blog

OCTOBER 4, 2023

Solution overview Amazon SageMaker is built on Amazon’s two decades of experience developing real-world ML applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. Within the IDE, under SageMaker JumpStart in the navigation pane, choose Models, notebooks, solutions.

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DagsHub

DECEMBER 5, 2023

Moreover, additional complexity arises when we potentially need to reproduce the best-performing experiment before putting it into production. MLflow is language- and framework-agnostic, and it offers convenient integration with the most popular machine learning and deep learning frameworks. What is ML Experiment Tracking?

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The MLOps Blog

AUGUST 3, 2023

We want to stop the pain and suffering people feel with maintaining machine learning pipelines in production. We want to enable a team of junior data scientists to write code, take it into production, maintain it, and then when they leave, importantly, no one has nightmares about inheriting their code. Do IDEs support it?

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Flipboard

MARCH 7, 2023

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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AWS Machine Learning Blog

SEPTEMBER 11, 2023

SageMaker Studio is a fully integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct SageMaker integration. Each SageMaker pipeline is composed of steps , which correspond to individual tasks such as processing, training, or data processing using Amazon EMR.

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AWS Machine Learning Blog

MAY 4, 2023

Generative AI is gaining a lot of public attention at present, with talk around products such as GPT4, ChatGPT, DALL-E2, Bard, and many other AI technologies. Prerequisites Amazon SageMaker Studio is the integrated development environment (IDE) within SageMaker that provides us with all the ML features that we need in a single pane of glass.

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The MLOps Blog

OCTOBER 20, 2023

Jupyter notebooks have been one of the most controversial tools in the data science community. Nevertheless, many data scientists will agree that they can be really valuable – if used well. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. How has this AB test performed?

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Hacker News

JANUARY 9, 2024

And when this capability is enabled, it allows the LLM to “look at pictures from the code it generated”, see if they’re what was asked for, and potentially correct itself: The deep integration of images into Wolfram Language—and Wolfram Notebooks—yields all sorts of possibilities for multimodal LLMs. How much can LLMs help with that?

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