AWS Machine Learning Blog

JUNE 22, 2023

Over 500 machine events are monitored in near-real time to give a full picture of machine conditions and their operating environments. Utilizing data streamed through LnW Connect, L&W aims to create better gaming experience for their end-users as well as bring more value to their casino customers.

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AWS Machine Learning Blog

FEBRUARY 2, 2023

The AWS Professional Services team has partnered with the NFL and Biocore to provide machine learning (ML)-based solutions for identifying helmet impacts from game footage using computer vision (CV) techniques. Therefore, adding more perspectives allows our ML system to identify more impacts that aren’t visible in a single view.

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Mlearning.ai

APRIL 6, 2023

Automate and streamline our ML inference pipeline with SageMaker and Airflow Building an inference data pipeline on large datasets is a challenge many companies face. SageMaker Batch Job Allows you to run batch inference on large datasets and generate predictions in a batch mode using machine learning (ML) models hosted in SageMaker.

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IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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Heartbeat

JUNE 5, 2023

However, managing machine learning projects can be challenging, especially as the size and complexity of the data and models increase. Without proper tracking, optimization, and collaboration tools, ML practitioners can quickly become overwhelmed and lose track of their progress. This is where Comet comes in.

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ODSC - Open Data Science

SEPTEMBER 25, 2023

For readers who work in ML/AI, it’s well understood that machine learning models prefer feature vectors of numerical information. Unfortunately, our data engineering and machine learning ops teams haven’t built a feature vector for us, so all of the relevant data lives in a relational schema in separate tables.

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ODSC - Open Data Science

APRIL 13, 2023

Hands-on Data-Centric AI: Data Preparation Tuning — Why and How? Going into developing machine learning models with a hands-on, data-centric AI approach has its benefits and requires a few extra steps to achieve. Learn more here. Final ODSC East 2023 Schedule Released!

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Recent events including Tropical Cyclone Gabrielle have highlighted the susceptibility of the grid to extreme weather and emphasized the need for climate adaptation with resilient infrastructure. Data preparation SageMaker Ground Truth employs a human workforce made up of Northpower volunteers to annotate a set of 10,000 images.

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AWS Machine Learning Blog

MARCH 1, 2023

The result of these events can be evaluated afterwards so that they make better decisions in the future. With this proactive approach, Kakao Games can launch the right events at the right time. Kakao Games can then create a promotional event not to leave the game. However, this approach is reactive.

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AWS Machine Learning Blog

MARCH 8, 2023

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. Data comes from disparate sources in a number of formats.

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Heartbeat

MARCH 28, 2023

A complete guide to building a deep learning project with PyTorch, tracking an Experiment with Comet ML, and deploying an app with Gradio on HuggingFace Image by Freepik AI tools such as ChatGPT, DALL-E, and Midjourney are increasingly becoming a part of our daily lives. This is when Comet ML comes into play. Installing comet_ml # !pip

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AWS Machine Learning Blog

JANUARY 6, 2023

The vendors evaluated for this MarketScape offer various software tools needed to support end-to-end machine learning (ML) model development, including data preparation, model building and training, model operation, evaluation, deployment, and monitoring. AI life-cycle tools are essential to productize AI/ML solutions.

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AWS Machine Learning Blog

JANUARY 6, 2023

The rules in this engine were predefined and written in SQL, which aside from posing a challenge to manage, also struggled to cope with the proliferation of data from TR’s various integrated data source. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

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DataRobot Blog

MARCH 16, 2023

Data scientists run experiments. They work with partners in IT to harden ML use cases into production systems. To work effectively, data scientists need agility in the form of access to enterprise data, streamlined tooling, and infrastructure that just works. Because the current ML lifecycle process is broken.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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AWS Machine Learning Blog

JULY 31, 2023

Although machine learning (ML) can provide valuable insights, ML experts were needed to build customer churn prediction models until the introduction of Amazon SageMaker Canvas. Additional key topics Advanced metrics are not the only important tools available to you for evaluating and improving ML model performance.

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The MLOps Blog

NOVEMBER 30, 2022

Data science teams currently struggle with managing multiple experiments and models and need an efficient way to store, retrieve, and utilize details like model versions, hyperparameters, and performance metrics. ML model versioning: where are we at? The short answer is we are in the middle of a data revolution.

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Heartbeat

FEBRUARY 1, 2023

Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. They might also help with data preparation and cleaning.

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Iguazio

DECEMBER 14, 2023

Drawing from their extensive experience in the field, the authors share their strategies, methodologies, tools and best practices for designing and building a continuous, automated and scalable ML pipeline that delivers business value. The book is poised to address these exact challenges.

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DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. Exploratory Data Analysis After we connect to Snowflake, we can start our ML experiment. launch event on March 16th.

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AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. GitHub serves as a centralized location to store, version, and manage your ML code base.

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AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII).

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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 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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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Pickl AI

JULY 16, 2024

It utilizes Machine Learning (ML) and other AI techniques to streamline IT processes, improve efficiency, and free up valuable time for IT professionals. Understanding AIOps Think of AIOps as a multi-layered application of Big Data Analytics , AI, and ML specifically tailored for IT operations.

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Iguazio

FEBRUARY 8, 2024

They are characterized by their enormous size, complexity, and the vast amount of data they process. These elements need to be taken into consideration when managing, streamlining and deploying LLMs in ML pipelines, hence the specialized discipline of LLMOps. Data Pipeline - Manages and processes various data sources.

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Heartbeat

JANUARY 12, 2024

It leverages sentence transformers to embed the text data and fine-tunes the head layer to perform the classification task. SetFit's two-stage training process —  src Few-Shot Training — Data Preparation As explained, we are all set to train the SetFit model with a handful of data.

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AWS Machine Learning Blog

AUGUST 26, 2024

Users can input audio, video, or text into GenASL, which generates an ASL avatar video that interprets the provided data. The solution uses AWS AI and machine learning (AI/ML) services, including Amazon Transcribe , Amazon SageMaker , Amazon Bedrock , and FMs. He is passionate about building home automation and AI/ML solutions.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Heartbeat

NOVEMBER 28, 2023

Introducing MLOps Machine learning (ML) is an essential tool for businesses of all sizes. However, deploying ML models in production can be complex and challenging. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring. Second, ML models are constantly evolving.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Only involving necessary people to do case validation or augmentation tasks reduces the risk of document mishandling and human error when dealing with sensitive data. About the Authors Sherry Ding is a senior artificial intelligence (AI) and machine learning (ML) specialist solutions architect at Amazon Web Services (AWS).

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AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. In this post, we focus on data preprocessing using Amazon SageMaker Processing and Amazon SageMaker Data Wrangler jobs.

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AWS Machine Learning Blog

AUGUST 14, 2023

Some of the models offer capabilities for you to fine-tune them with your own data. SageMaker JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for machine learning (ML) with SageMaker. Mia Chang is an ML Specialist Solutions Architect for Amazon Web Services.

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AWS Machine Learning Blog

APRIL 26, 2024

This feature empowers you to rapidly synthesize this information without the hassle of data preparation or any management overhead. He frequently speaks at AI/ML conferences, events, and meetups around the world. or “What are the common pain points mentioned by customers regarding our onboarding process?”

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AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. SageMaker Training is a managed batch ML compute service that reduces the time and cost to train and tune models at scale without the need to manage infrastructure. SageMaker-managed clusters of ml.p4d.24xlarge

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Heartbeat

JANUARY 9, 2024

Typically, MLOps systems include capabilities for automating the whole ML lifecycle, from data preparation through model training and deployment. MLOps frameworks, on the other hand, often give less specific help for testing ML models than LLMOps frameworks. As a result, we observe an increase in the use of "LLMOps."

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