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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AWS Machine Learning Blog

MAY 7, 2024

Hugging Face is an open-source machine learning (ML) platform that provides tools and resources for the development of AI projects. They are designed for real-time, interactive, and low-latency workloads and provide auto scaling to manage load fluctuations. The following figure shows the input conversation and output summary.

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IBM Journey to AI blog

APRIL 8, 2024

  Db2 Warehouse , our cloud-native data warehouse for real-time operational analytics, business intelligence (BI), reporting and machine learning (ML), is also available as a fully managed service on AWS to support customer’s data warehousing needs.  You can also set a threshold limit for automated storage scaling.

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Towards AI

APRIL 27, 2023

for e.g., if a manufacturing or logistics company is collecting recording data from CCTV across its manufacturing hubs and warehouses, there could be a potentially a good number of use cases ranging from workforce safety, visual inspection automation, etc. 99% of consultants will rather ask you to actually execute these POCs.

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Marktechpost

MARCH 16, 2024

Machine learning (ML) workflows, essential for powering data-driven innovations, have grown in complexity and scale, challenging previous optimization methods. This scenario necessitated a shift towards a more unified and efficient approach to ML workflow management. A team of researchers from Ant Group, Red Hat, Snap Inc.,

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AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

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Marktechpost

SEPTEMBER 17, 2024

Artificial Intelligence (AI) and Machine Learning (ML) have been transformative in numerous fields, but a significant challenge remains in the reproducibility of experiments. As AI continues to evolve, researchers are looking for ways to automate these tasks to expedite scientific discovery. of the sub-problems in the Masked set.

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AWS Machine Learning Blog

APRIL 20, 2023

Each machine learning (ML) system has a unique service level agreement (SLA) requirement with respect to latency, throughput, and cost metrics. Based on Inference Recommender’s instance type recommendations, we can find the right real-time serving ML instances that yield the right price-performance for this use case.

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AWS Machine Learning Blog

MARCH 1, 2023

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker.

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AWS Machine Learning Blog

APRIL 19, 2024

Amazon Personalize accelerates your digital transformation with machine learning (ML), making it effortless to integrate personalized recommendations into existing websites, applications, email marketing systems, and more. A solution version refers to a trained ML model. All your data is encrypted to be private and secure.

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AWS Machine Learning Blog

APRIL 19, 2024

The platform both enables our AI—by supplying data to refine our models—and is enabled by it, capitalizing on opportunities for automated decision-making and data processing. We use Amazon EKS and were looking for the best solution to auto scale our worker nodes. This enables all steps to be completed from a web browser.

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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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AWS Machine Learning Blog

NOVEMBER 6, 2023

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. Custom Queries provides a way for you to customize the Queries feature for your business-specific, non-standard documents such as auto lending contracts, checks, and pay statements, in a self-service way.

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IBM Journey to AI blog

JULY 17, 2023

Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. is a studio to train, validate, tune and deploy machine learning (ML) and foundation models for Generative AI. Watsonx.ai

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AWS Machine Learning Blog

JULY 3, 2023

You can use Amazon SageMaker Data Labeling in two ways to create these labels: Amazon SageMaker Ground Truth Plus – This service provides an expert workforce that is trained on ML tasks and can help meet your data security, privacy, and compliance requirements. Active learning is another concept that is closely related to auto-labeling.

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Marktechpost

JUNE 15, 2024

MAGPIE leverages the auto-regressive nature of aligned LLMs to generate high-quality instruction data at scale. In the response generation step, these queries prompt the LLM again to produce corresponding responses, resulting in complete instruction-response pairs. The model then generates diverse user queries based on these templates.

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TheSequence

MAY 22, 2023

Auto-GPT An open-source GPT-based app that aims to make GPT completely autonomous. In a short few weeks, it has accumulated over 120k stars on GitHub, eclipsing PyTorch, Scikit-Learn, HuggingFace Transformers, and any other open-source AI/ML library you can think of. What makes Auto-GPT such a popular project?

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AWS Machine Learning Blog

SEPTEMBER 11, 2023

Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. You can use Lifecycle Configurations to automate customization for your Studio environment. Create an aws_sagemaker_studio_lifecycle_config resource “ auto_shutdown ”.

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AWS Machine Learning Blog

MARCH 10, 2025

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. The Q&A handler, running on AWS Fargate, orchestrates the complete query response cycle by coordinating between services and processing responses through the LLM pipeline.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud.

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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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Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Solution overview The following diagram illustrates the ML platform architecture.

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AWS Machine Learning Blog

JULY 25, 2024

By implementing features such as proactive health monitoring and automated recovery mechanisms, organizations can create a fault-tolerant environment capable of handling hardware failures or other issues without compromising the integrity of the training process. You can get these values from the Amazon EKS console. and public.ecr.aws.

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AWS Machine Learning Blog

JUNE 11, 2024

This allows machine learning (ML) practitioners to rapidly launch an Amazon Elastic Compute Cloud (Amazon EC2) instance with a ready-to-use deep learning environment, without having to spend time manually installing and configuring the required packages. You also need the ML job scripts ready with a command to invoke them.

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AWS Machine Learning Blog

JULY 1, 2024

Organizations strive to implement efficient, scalable, cost-effective, and automated customer support solutions without compromising the customer experience. You can use QnAIntent with new or existing Amazon Lex bots to automate FAQs through text and voice channels, such as Amazon Connect. Choose Create knowledge base. Choose Next.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. AWS published Guidance for Optimizing MLOps for Sustainability on AWS to help customers maximize utilization and minimize waste in their ML workloads.

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Chatbots Life

MAY 19, 2023

Automation rules today’s world. A chatbot is a technological genie that uses intelligent automation, ML, and NLP to automate tasks. It adds a digital flavor by automating your day-to-day IT tasks to help businesses work smarter. Modern service desks offer an automated ticketing system for staff.

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AWS Machine Learning Blog

MAY 9, 2023

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.

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Marktechpost

DECEMBER 4, 2023

From completing entire lines of code and functions to writing comments and aiding in debugging and security checks, Copilot serves as an invaluable tool for developers. Trained on a large open-source code dataset, it suggests snippets to full functions, automating repetitive tasks and enhancing code quality.

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AWS Machine Learning Blog

JUNE 3, 2024

Solution overview SageMaker Canvas brings together a broad set of capabilities to help data professionals prepare, build, train, and deploy ML models without writing any code. In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing.

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. and Pandas or Apache Spark DataFrames.

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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. Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it.

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AWS Machine Learning Blog

MAY 22, 2024

Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. jpg and the complete metadata from styles/38642.json. From here, we can fetch the image for this product from images/38642.jpg

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AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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Marktechpost

MAY 3, 2024

From completing entire lines of code and functions to writing comments and aiding in debugging and security checks, Copilot serves as an invaluable tool for developers. Trained on a large open-source code dataset, it suggests snippets to full functions, automating repetitive tasks and enhancing code quality.

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AWS Machine Learning Blog

MAY 31, 2023

The Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning (ML) models on Amazon SageMaker. 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. something: '1.0'

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AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. It plays a crucial role in every model’s development process and allows data scientists to focus on the most promising ML techniques. The following diagram presents the overall solution workflow.

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