Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. Download the RPM (Red Hat Package Management system) file for Docker Desktop ( Note: This link may change in the future.

Machine Learning 152

Machine Learning Computer Vision Deep Learning Python 152

AWS Machine Learning Blog

FEBRUARY 11, 2025

SageMaker Studio is a comprehensive IDE that offers a unified, web-based interface for performing all aspects of the machine learning (ML) development lifecycle. This approach allows for greater flexibility and integration with existing AI/ML workflows and pipelines. Deploy Meta SAM 2.1 On the endpoint details page, choose Delete.

Computer Vision 80

Computer Vision ML Python Automation 80

AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

Machine Learning 128

Machine Learning Data Scientist ML Python 128

AWS Machine Learning Blog

FEBRUARY 12, 2025

SageMaker AI starts and manages all the necessary Amazon Elastic Compute Cloud (Amazon EC2) instances for us, supplies the appropriate containers, downloads data from our S3 bucket to the container and uploads and runs the specified training script, in our case fine_tune_llm.py.

LLM 75

LLM ML Natural Language Processing Machine Learning 75

AWS Machine Learning Blog

JULY 24, 2024

Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. Solution overview Running hundreds of experiments, comparing the results, and keeping a track of the ML lifecycle can become very complex. In this example, we download the data from a Hugging Face dataset.

LLM 121

LLM ML Generative AI Machine Learning 121

AWS Machine Learning Blog

APRIL 11, 2023

Developing web interfaces to interact with a machine learning (ML) model is a tedious task. With Streamlit , developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. The no-cache-dir flag will disable the cache.

ML Engineer 83

ML Engineer ML Data Scientist Machine Learning 83

AWS Machine Learning Blog

OCTOBER 19, 2023

For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). This post demonstrates how to use Amazon SageMaker Data Wrangler and Amazon Comprehend to automatically redact PII from tabular data as part of your machine learning operations (ML Ops) workflow.

Machine Learning 104

Machine Learning Python ML Automation 104

The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

ETL 59

ETL ML Machine Learning Data Scientist 59

AWS Machine Learning Blog

APRIL 7, 2025

This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. By providing multiple access points, SageMaker JumpStart helps you seamlessly incorporate pre-trained models into your AI/ML development efforts, regardless of your preferred interface or workflow.

Machine Learning 62

Machine Learning Large Language Models Python Automation 62

AWS Machine Learning Blog

AUGUST 22, 2024

You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. SageMaker Pipelines You can use SageMaker Pipelines to define and orchestrate the various steps involved in the ML lifecycle, such as data preprocessing, model training, evaluation, and deployment. We use Python to do this.

Generative AI 114

Generative AI Metadata Python ML 114

AWS Machine Learning Blog

MAY 13, 2024

We also explore the utility of the RAG prompt engineering technique as it applies to the task of summarization. Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline. Embeddings are numerical representations of real-world objects that ML systems use to understand complex knowledge domains like humans do.

Generative AI 123

Generative AI Prompt Engineer Prompt Engineering LLM 123

AWS Machine Learning Blog

FEBRUARY 21, 2025

The concept of a compound AI system enables data scientists and ML engineers to design sophisticated generative AI systems consisting of multiple models and components. The synthetic data generation notebook automatically downloads the CUAD_v1 ZIP file and places it in the required folder named cuad_data.

LLM 77

LLM AI AI Data Scientist 77

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. Upload the dataset you downloaded in the prerequisites section. To learn more, see Secure access to Amazon SageMaker Studio with AWS SSO and a SAML application.

Generative AI 107

Generative AI Categorization Auto-complete Auto-classification 107

The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

Machine Learning 72

Machine Learning Metadata ML Python 72

AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

Generative AI 120

Generative AI Prompt Engineer Prompt Engineering ML 120

AWS Machine Learning Blog

AUGUST 1, 2024

By demonstrating the process of deploying fine-tuned models, we aim to empower data scientists, ML engineers, and application developers to harness the full potential of FMs while addressing unique application requirements. SageMaker Studio is a single web-based interface for end-to-end machine learning (ML) development.

Generative AI 115

Generative AI Machine Learning Artificial Intelligence Artificial Intelligence 115

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.

ML 117

ML Automation Metadata Software Development 117

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. You can also download the completed notebook here. For this post, we choose the Data Science 3.0

LLM 106

LLM Large Language Models ML Data Scientist 106

AWS Machine Learning Blog

FEBRUARY 29, 2024

Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. These include a fully managed AI development environment with an integrated development environment (IDE), simplifying the end-to-end ML workflow. Download the source code from the GitHub repo.

Data Scientist 116

Data Scientist DevOps AI AI 116

AWS Machine Learning Blog

OCTOBER 4, 2024

You can download the generated images directly from the UI or check the image in your S3 bucket. About the Authors Akarsha Sehwag is a Data Scientist and ML Engineer in AWS Professional Services with over 5 years of experience building ML based solutions. degree in Electrical Engineering.

Data Scientist 98

Data Scientist Generative AI Machine Learning ML 98

Flipboard

OCTOBER 9, 2023

Getting Used to Docker for Machine Learning Introduction Docker is a powerful addition to any development environment, and this especially rings true for ML Engineers or enthusiasts who want to get started with experimentation without having to go through the hassle of setting up several drivers, packages, and more. the image).

Machine Learning 152

Machine Learning Computer Vision Deep Learning Python 152

The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. Reusability & reproducibility: Building ML models is time-consuming by nature. Save vs package vs store ML models Although all these terms look similar, they are not the same.

Python 105

Python Metadata ML Machine Learning 105

AWS Machine Learning Blog

FEBRUARY 28, 2024

You can download the datasets and store them in Amazon Simple Storage Service (Amazon S3). About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon. Outside of his work as a Data and ML engineer at Amazon, Sanjeeb Panda is an avid foodie and music enthusiast. format('parquet').option('path',

Metadata 125

Metadata LLM Generative AI NLP 125

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.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

TheSequence

JULY 12, 2023

In 2018, I joined Cruise and cofounded the ML Infrastructure team there. We built many critical platform systems that enabled the ML teams to develop and ship models much faster, which contributed to the commercial launch of robotaxis in San Francisco in 2022. This required large end-to-end pipelines.

ML 97

ML Python Machine Learning Metadata 97

AWS Machine Learning Blog

SEPTEMBER 25, 2024

SageMaker JumpStart is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. SageMaker Studio is a comprehensive IDE that offers a unified, web-based interface for performing all aspects of the ML development lifecycle. Deploy Llama 3.2

Software Engineer 121

Software Engineer Software Development ML Python 121

AWS Machine Learning Blog

MARCH 18, 2025

You can download a sample file and review the contents. Her work has been focused on in the areas of business intelligence, analytics, and AI/ML. Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice. At this step, the interview transcripts are ready.

LLM 101

LLM NLP Data Integration AI 101

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. A public GitHub repo provides hands-on examples for each of the presented approaches.

Python 123

Python Data Science Data Scientist ML 123

AWS Machine Learning Blog

OCTOBER 22, 2024

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. Create a complete AI/ML pipeline for fine-tuning an LLM using drag-and-drop functionality. Brock Wade is a Software Engineer for Amazon SageMaker.

Automation 79

Automation LLM DevOps Python 79

Mlearning.ai

AUGUST 18, 2023

As an ML engineer you’re in charge of some code/model. Also same expertise rule applies for an ML engineer, the more versed you are in MLOps the better you can foresee issues, fix data/model bugs and be a valued team member. Running invoke from cmd: $ inv download-best-model We’re decoupling MLOps from actual ML code.

DevOps 52

DevOps Python ML ML Engineer 52

AWS Machine Learning Blog

JULY 21, 2023

Amazon SageMaker makes it easier for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. The tool makes it easier to access geospatial data sources, run purpose-built processing operations, apply pre-trained ML models, and use built-in visualization tools faster and at scale.

Machine Learning 72

Machine Learning ML ML Engineer Data Scientist 72

AWS Machine Learning Blog

FEBRUARY 9, 2023

Rather than downloading the data to a local machine for inferences, SageMaker does all the heavy lifting for you. SageMaker automatically downloads and preprocesses the satellite image data for the EOJ, making it ready for inference. This land cover segmentation model can be run with a simple API call.

Machine Learning 57

Machine Learning ML Deep Learning Robotics 57

DataRobot Blog

SEPTEMBER 13, 2022

They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. If you’re just getting started with AI and ML, technology can help you bridge gaps in your workforce and institutional knowledge. Download Now. Download Now.

Data Scientist 52

Data Scientist Automation ML Machine Learning 52

Heartbeat

JULY 17, 2023

Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards. To download it, you will use the Kaggle package. Create your API keys on your Account’s Settings page and it will download a JSON file. We pay our contributors, and we don’t sell ads.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

Data Drift 116

Data Drift Auto-complete ML Automation 116

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. Because you use p4de.24xlarge You can then take the easy-ssh.sh

Auto-complete 103

Auto-complete ML Generative AI Deep Learning 103

ODSC - Open Data Science

NOVEMBER 25, 2024

We also have plenty of slides from the virtual side of ODSC West that you can see and download here. You can check out the top session recordings here if you have a subscription to the Ai+ Training platform.

Data Science 52

Data Science AI Engineer Prompt Engineer Prompt Engineering 52