Towards AI

AUGUST 7, 2024

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

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Marktechpost

JULY 18, 2023

GitHub Copilot GitHub Copilot is an AI-powered code completion tool that analyzes contextual code and delivers real-time feedback and recommendations by suggesting relevant code snippets. Tabnine Tabnine is an AI-based code completion tool that offers an alternative to GitHub Copilot.

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Marktechpost

JULY 11, 2023

Tabnine Although Tabnine is not an end-to-end code generator, it amps up the integrated development environment’s (IDE) auto-completion capability. Jacob Jackson created Tabnine in Rust when he was a student at the University of Waterloo, and it has now grown into a complete AI-based code completion tool.

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IBM Journey to AI blog

JULY 17, 2023

Generative AI auto-summarization creates summaries that employees can easily refer to and use in their conversations to provide product, service or recommendations (and it can also categorize and track trends). In another instance, Lloyds Banking Group was struggling to meet customer needs with their existing web and mobile application.

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AWS Machine Learning Blog

APRIL 25, 2024

The added benefit of asynchronous inference is the cost savings by auto scaling the instance count to zero when there are no requests to process. SageMaker features and capabilities help developers and data scientists get started with natural language processing (NLP) on AWS with ease.

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Flipboard

MARCH 13, 2023

There are two main purposes for building this pipeline: support the data scientists for late-stage model development, and surface model predictions in the product by serving models in high volume and in real-time production traffic. Amazon SNS is fully managed pub/sub service for A2A and A2P messaging.

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AWS Machine Learning Blog

MARCH 13, 2025

For a complete list of runtime configurations, please refer to text-generation-launcher arguments. SageMaker endpoints also support auto-scaling, allowing DeepSeek-R1 to scale horizontally based on incoming request volume while seamlessly integrating with elastic load balancing. GenAI Data Scientist at AWS.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

With the SageMaker HyperPod auto-resume functionality, the service can dynamically swap out unhealthy nodes for spare ones to ensure the seamless continuation of the workload. Also included are SageMaker HyperPod cluster software packages, which support features such as cluster health check and auto-resume.

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AWS Machine Learning Blog

JANUARY 5, 2024

The suite of services can be used to support the complete model lifecycle including monitoring and retraining ML models. Collaboration – Data scientists each worked on their own local Jupyter notebooks to create and train ML models. They lacked an effective method for sharing and collaborating with other data scientists.

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NVIDIA

JUNE 7, 2024

Enterprises seeking to harness the power of AI must establish a data pipeline that involves extracting data from diverse sources, transforming it into a consistent format and storing it efficiently. The company found that data scientists were having to remove features from algorithms just so they would run to completion.

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AWS Machine Learning Blog

MARCH 10, 2025

By surrounding unparalleled human expertise with proven technology, data and AI tools, Octus unlocks powerful truths that fuel decisive action across financial markets. Visit octus.com to learn how we deliver rigorously verified intelligence at speed and create a complete picture for professionals across the entire credit lifecycle.

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ODSC - Open Data Science

MAY 25, 2023

Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!) Whether you’re an experienced data scientist working with time series or a newcomer to the field, join us! We encourage you to complete your user registration here: [link].

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Marktechpost

JULY 11, 2023

Tabnine for JupyterLab Typing code is complex without auto-complete options, especially when first starting out. In addition to the spent time inputting method names, the absence of auto-complete promotes shorter naming styles, which is not ideal. For a development environment to be effective, auto-complete is crucial.

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AWS Machine Learning Blog

JANUARY 26, 2023

Data scientists train multiple ML algorithms to examine millions of consumer data records, identify anomalies, and evaluate if a person is eligible for credit. This is a common problem that data scientists face when training their models. The below workflow depicts a series of training job runs using warm pool.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Optionally, if Account A and Account B are part of the same AWS Organizations, and the resource sharing is enabled within AWS Organizations, then the resource sharing invitation are auto accepted without any manual intervention. Following are the steps completed by using APIs to create and share a model package group across accounts.

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AWS Machine Learning Blog

MAY 31, 2023

In such cases, data scientists have to provide these parameters to their ML model training and deployment code manually, by noting down subnets, security groups, and KMS keys. This puts the onus on the data scientists to remember to specify these configurations, to successfully run their jobs, and avoid getting Access Denied errors.

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The MLOps Blog

JUNE 27, 2023

Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. It provides a high-level API that makes it easy to define and execute data science workflows.

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AWS Machine Learning Blog

DECEMBER 4, 2023

With SageMaker, data scientists and developers can quickly and effortlessly build and train ML models, and then directly deploy them into a production-ready hosted environment. Overview of solution Five people from Getir’s data science team and infrastructure team worked together on this project.

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AWS Machine Learning Blog

MAY 2, 2023

This integration of model development and sharing creates a tighter collaboration between business and data science teams and lowers time to value. Business teams can use existing models built by their data scientists or other departments to solve a business problem instead of rebuilding new models in outside environments.

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AWS Machine Learning Blog

JANUARY 29, 2025

Import the model Complete the following steps to import the model: On the Amazon Bedrock console, choose Imported models under Foundation models in the navigation pane. Importing the model will take several minutes depending on the model being imported (for example, the Distill-Llama-8B model could take 520 minutes to complete).

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Flipboard

AUGUST 17, 2023

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Launch SageMaker Studio Complete the following steps to launch your SageMaker Studio domain: On the SageMaker console, choose Domains in the navigation pane.

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AWS Machine Learning Blog

DECEMBER 5, 2023

The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. When this is complete, the document can be routed to the appropriate department or downstream process. The following diagram outlines the proposed solution architecture. append(e["Text"].upper())

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AWS Machine Learning Blog

JUNE 23, 2023

Optionally, if you’re using Snowflake OAuth access in SageMaker Data Wrangler, refer to Import data from Snowflake to set up an OAuth identity provider. Data scientists should have the following prerequisites Access to Amazon SageMaker , an instance of Amazon SageMaker Studio , and a user for SageMaker Studio.

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AWS Machine Learning Blog

MAY 22, 2024

jpg and the complete metadata from styles/38642.json. About the Authors Antonia Wiebeler is a Data Scientist at the AWS Generative AI Innovation Center, where she enjoys building proofs of concept for customers. Daniel Zagyva is a Data Scientist at AWS Professional Services. bias="none", # the bias type for Lora.

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ODSC - Open Data Science

FEBRUARY 28, 2023

Going from Data to Insights LexisNexis At HPCC Systems® from LexisNexis® Risk Solutions you’ll find “a consistent data-centric programming language, two processing platforms, and a single, complete end-to-end architecture for efficient processing.” These tools are designed to help companies derive insights from big data.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

It plays a crucial role in every model’s development process and allows data scientists to focus on the most promising ML techniques. Additionally, AutoML provides a baseline model performance that can serve as a reference point for the data science team. The following diagram presents the overall solution workflow.

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Towards AI

SEPTEMBER 27, 2023

The previous blog posts, “Data Acquisition & Exploration” and “Data Transformation and Feature Engineering”, explored how AWS SageMaker’s capabilities can help data scientists collaborate and accelerate data exploration, understanding, transformation, and feature engineering. Source: Image by the author.

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AWS Machine Learning Blog

MAY 3, 2023

For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. You can also edit the auto scaling policy on the Auto-scaling tab on this page. SageMaker provides a variety of options to deploy models.

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AWS Machine Learning Blog

MAY 1, 2024

Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English. Using PyTorch Neuron gives data scientists the ability to track training progress in a TensorBoard. This results in faster restarts and workload completion.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Amazon SageMaker is a fully managed service that allows developers and data scientists to quickly build, train, and deploy machine learning (ML) models. Based on customer SLAs and data volume, we can choose batch inference, real-time hosting with auto-scaling, or serverless hosting.

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deepsense.ai

OCTOBER 8, 2023

Usually agents will have: Some kind of memory (state) Multiple specialized roles: Planner – to “think” and generate a plan (if steps are not predefined) Executor – to “act” by executing the plan using specific tools Feedback provider – to assess the quality of the execution by means of auto-reflection.

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AWS Machine Learning Blog

APRIL 27, 2023

Take for example operators such as TopK, LayerNorm, or ZeroCompression, which read data from memory and only use it for a minimal number of ALU calculations. Regular CPU systems are completely memory bound for these calculations, and performance is limited by the time required to move the data into the CPU.

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AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them.

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AWS Machine Learning Blog

JANUARY 9, 2023

The models can be completely heterogenous, with their own independent serving stack. After these business logic steps are complete, the inputs are passed through to ML models. And during idle time, it should be able to turn off compute capacity completely so that you’re not charged. ML inference options.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

This allows you to share the intended uses and assessed carbon impact of a model so that data scientists, ML engineers, and other teams can make informed decisions when choosing and running models. If your workloads can tolerate latency, consider deploying your model on Amazon SageMaker Asynchronous Inference with auto-scaling groups.

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AWS Machine Learning Blog

JUNE 13, 2023

SageMaker and Forethought SageMaker is a fully managed service that provides developers and data scientists the ability to build, train, and deploy ML models quickly. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. 2xlarge instances.

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