AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. In the following example, we show how to fine-tune the latest Meta Llama 3.1

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AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. He focuses on architecting and implementing large-scale generative AI and classic ML pipeline solutions.

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Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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ODSC - Open Data Science

FEBRUARY 9, 2023

It is often too much to ask for the data scientist to become a domain expert. However, in all cases the data scientist must develop strong domain empathy to help define and solve the right problems. Nina Zumel and John Mount, Practical Data Science with R, 2nd Ed. But this statement also goes upstream.

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Marktechpost

NOVEMBER 16, 2024

Generative AI Fundamentals Specialization This specialization offers a comprehensive introduction to generative AI, covering models like GPT and DALL-E, prompt engineering, and ethical considerations. It includes five self-paced courses with hands-on labs and projects using tools like ChatGPT, Stable Diffusion, and IBM Watsonx.ai.

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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

JANUARY 31, 2024

Data scientists and ML engineers often need help to build full-stack applications. These professionals typically have a firm grasp of data and AI algorithms. It is a Python-based framework for data scientists and machine learning engineers. This is where Taipy comes into play.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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Unite.AI

JANUARY 27, 2025

End users should also seek companies that can help with this testing as often an ML Engineer can help with deployment vs. the Data Scientist that created the model. Quite often deployment of a large model is too expensive to make it practical for use.

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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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Unite.AI

JUNE 28, 2023

Xavier Conort is a visionary data scientist with more than 25 years of data experience. He began his career as an actuary in the insurance industry before transitioning to data science. He’s a top-ranked Kaggle competitor and was the Chief Data Scientist at DataRobot before co-founding FeatureByte.

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AWS Machine Learning Blog

APRIL 8, 2025

With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services such as Amazon SageMaker to efficiently create and deploy ML models. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. This also led to a backlog of data that needed to be ingested.

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. Identify areas of interest We begin by illustrating how SageMaker can be applied to analyze geospatial data at a global scale.

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Unite.AI

FEBRUARY 12, 2025

The AI/ML engine built into MachineMetrics analyzes this machine data to detect anomalies and patterns that might indicate emerging problems. The platforms strength lies in its powerful data processing pipeline that cleans and contextualizes plant floor data in real-time, making it analysis-ready.

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Unite.AI

JUNE 29, 2023

You can also explore the Google Cloud Skills Boost program, specifically designed for ML APIs, which offers extra support and expertise in this field. Optimizing workloads and costs To address the challenges of expensive and complex ML infrastructure, many companies increasingly turn to cloud services.

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AWS Machine Learning Blog

JANUARY 23, 2025

The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. This approach helps you achieve machine learning (ML) governance, scalability, and standardization.

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IBM Journey to AI blog

AUGUST 11, 2023

Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

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Eugene Yan

JANUARY 16, 2019

What's the difference between a data scientist, data engineer, and ML engineer? A panel at Google.

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AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. An effective approach that addresses a wide range of observed issues is the establishment of an AI/ML center of excellence (CoE). What is an AI/ML CoE?

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AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Your client applications invoke this endpoint to get inferences from the model.

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Towards AI

NOVEMBER 8, 2024

Edited Photo by Taylor Vick on Unsplash In ML engineering, data quality isn’t just critical — it’s foundational. Since 2011, Peter Norvig’s words underscore the power of a data-centric approach in machine learning. Yet, this perspective often gets sidelined and there was never a consensus in the ML community about it.

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TheSequence

MARCH 8, 2024

Join industry leaders from LangChain, Meta, and Visa for insights to master AI and ML in production. VEW SPEAKER LINEUP Here’s a sneak peek of the agenda: LangChain Keynote: Hear from Lance Martin, an ML leader at LangChain, a leading orchestration framework for large language models (LLMs). Stay tuned for the full agenda!

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AWS Machine Learning Blog

FEBRUARY 19, 2025

In this example, the ML engineering team is borrowing 5 GPUs for their training task With SageMaker HyperPod, you can additionally set up observability tools of your choice. Data scientist experience Data scientists are the second persona interacting with SageMaker HyperPod clusters. HyperPod CLI v2.0.0

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The MLOps Blog

DECEMBER 28, 2022

The onset of the pandemic has triggered a rapid increase in the demand and adoption of ML technology. Building ML team Following the surge in ML use cases that have the potential to transform business, the leaders are making a significant investment in ML collaboration, building teams that can deliver the promise of machine learning.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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TheSequence

NOVEMBER 3, 2023

Join the global ML community at this virtual event—speakers from companies like HelloFresh, Lidl Digital, Meta, PepsiCo, Riot Games, and more will share best practices around building platforms and architectures for production ML. apply(ops) is just around the corner!

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AWS Machine Learning Blog

FEBRUARY 11, 2025

Get started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. These models are fully customizable for your use case with your data. Marc Karp is an ML Architect with the Amazon SageMaker Service team. In his free time, he enjoys traveling and photography.

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Unite.AI

FEBRUARY 22, 2024

” Integrity: Upholding Reliability and Ethical Accountability AI/ML integrity stands as a pivotal pillar for responsible AI. It revolves around accountability, ensuring that AI products, machine learning models, and the organizations behind them are responsible for their actions.

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Towards AI

MARCH 14, 2024

So, before we look at how to learn data science, we need to know: what really is a data scientist? I mean, ML engineers often spend most of their time handling and understanding data. So, how is a data scientist different from an ML engineer?

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ODSC - Open Data Science

APRIL 2, 2025

Roles like Data Scientist, ML Engineer, and the emerging LLM Engineer are in high demand. Jupyter notebooks remain a staple for data scientists. ML engineers are expected to work within Docker and Kubernetes environments.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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Marktechpost

OCTOBER 10, 2024

Data Visualization : Presenting findings via charts and graphs. Predictive Modeling : Using data to predict future outcomes. Data scientists need to be skilled in programming, statistics, and domain knowledge. They play a critical role in transforming raw data into actionable business insights. fraud detection).

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. There were noticeable challenges when running ML workflows in the cloud.

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Unite.AI

JUNE 19, 2024

In world of Artificial Intelligence (AI) and Machine Learning (ML), a new professionals has emerged, bridging the gap between cutting-edge algorithms and real-world deployment. Meet the MLOps Engineer: the orchestrating the seamless integration of ML models into production environments, ensuring scalability, reliability, and efficiency.

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AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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