AWS Machine Learning Blog

APRIL 8, 2025

Additionally, the integration of SageMaker features in iFoods infrastructure automates critical processes, such as generating training datasets, training models, deploying models to production, and continuously monitoring their performance. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.

Machine Learning 103

Machine Learning ML Auto-complete Data Scientist 103

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

ML 153

ML DevOps Data Scientist ML Engineer 153

AWS Machine Learning Blog

DECEMBER 19, 2023

Many businesses already have data scientists and ML engineers who can build state-of-the-art models, but taking models to production and maintaining the models at scale remains a challenge. Machine learning operations (MLOps) applies DevOps principles to ML systems. It’s much more than just automation.

Machine Learning 131

Machine Learning ML Engineer DevOps ML 131

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.

Data Science 133

Data Science Data Drift DevOps Auto-complete 133

The MLOps Blog

JANUARY 23, 2023

Lived through the DevOps revolution. Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. I don’t see what special role ML and MLOps engineers would play here. –

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

OCTOBER 22, 2024

It accelerates your generative AI journey from prototype to production because you don’t need to learn about specialized workflow frameworks to automate model development or notebook execution at scale. About the Authors Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS.

Automation 87

Automation LLM DevOps Python 87

AWS Machine Learning Blog

JULY 8, 2024

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. Amazon CloudWatch – Collects and visualizes real-time logs that provide the basis for automation. Used to deploy training and inference code.

Data Scientist 120

Data Scientist ML Engineer Machine Learning Data Science 120

AWS Machine Learning Blog

OCTOBER 28, 2024

For automated alerts for model monitoring, creating an Amazon Simple Notification Service (Amazon SNS) topic is recommended, which email user groups will subscribe to for alerts on a given CloudWatch metric alarm. Ajay Raghunathan is a Machine Learning Engineer at AWS. About the Authors Joe King is a Sr. Raju Patil is a Sr.

ML 111

ML Metadata Data Scientist Machine Learning 111

Heartbeat

FEBRUARY 1, 2023

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. They may also be involved in the deployment process’s automation.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

JUNE 27, 2023

They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

DevOps 116

DevOps ML Machine Learning ML Engineer 116

AWS Machine Learning Blog

FEBRUARY 27, 2024

Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. This is made possible by automating tedious, repetitive MLOps tasks as part of the template.

Machine Learning 137

Machine Learning DevOps Data Scientist Data Quality 137

Pickl AI

JANUARY 30, 2023

Data Automation: Automate data processing pipelines and workflows using Python scripting and libraries such as PyAutoGUI and Task Scheduler. Scripting: Use Python as a scripting language to automate and simplify tasks and processes. Data Visualization: Use libraries such as Matplotlib, Seaborn, Plotly, etc.,

Python 97

Python DevOps Machine Learning Data Science 97

AWS Machine Learning Blog

OCTOBER 2, 2023

An MLOps pipeline allows to automate the full ML lifecycle from data labeling to model training and deployment. Implementing an MLOps pipeline at the edge introduces additional complexities that make the automation, integration, and maintenance processes more challenging due to the increased operational overhead involved.

DevOps 121

DevOps Automation ML Computer Vision 121

AWS Machine Learning Blog

OCTOBER 20, 2023

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake. The reference architecture for the ML platform with various AWS services is shown in the following diagram.

ML 132

ML Data Scientist ML Engineer Data Science 132

AWS Machine Learning Blog

AUGUST 22, 2024

As you move from pilot and test phases to deploying generative AI models at scale, you will need to apply DevOps practices to ML workloads. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

Generative AI 127

Generative AI Metadata Python ML 127

AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Security SMEs review the architecture based on business security policies and needs.

Generative AI 127

Generative AI Prompt Engineer Prompt Engineering ML 127

AWS Machine Learning Blog

FEBRUARY 29, 2024

The AI platform team’s key objective is to ensure seamless access to Workbench services and SageMaker Studio for all Deutsche Bahn teams and projects, with a primary focus on data scientists and ML engineers. Vishwanath Bhat is a DevOps Architect at AWS Professional Services, based in Germany.

Data Scientist 129

Data Scientist DevOps AI AI 129

AWS Machine Learning Blog

DECEMBER 13, 2023

To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle.

ML 124

ML Automation Metadata Software Development 124

AWS Machine Learning Blog

AUGUST 31, 2023

Implementing these guardrails is getting harder for enterprises because the ML processes and activities within enterprises are becoming more complex due to the inclusion of deeply involved processes that require contributions from multiple stakeholders and personas. His core area of focus includes Machine Learning, DevOps, and Containers.

ML 123

ML Data Scientist Machine Learning Data Science 123

AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. This may often be the same team as cloud engineering.

ML 115

ML Machine Learning Data Science Data Drift 115

AWS Machine Learning Blog

JANUARY 13, 2023

Standardize building and reuse of AI solutions across business functions and AI practitioners’ personas, while ensuring adherence to enterprise best practices: Automate and standardize the repetitive undifferentiated engineering effort. Bring a single pane of glass for ML activities. Model deployment.

ML 96

ML Data Scientist Machine Learning Metadata 96

Flipboard

MARCH 7, 2023

The main benefits of this approach are a high level of version control and reproducibility of an ML runtime environment and immediate availability of library packages because they’re installed in the image. You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images.

Python 123

Python Data Science Data Scientist ML 123

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. They should also offer version control capabilities to manage the changes and revisions of ML artifacts, ensuring reproducibility and facilitating effective teamwork.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Mlearning.ai

JULY 29, 2023

INTRODUCTION Recently I had the pleasure to explore different options to automate a daily ML inference job, which reads raw data from a database table and write the inference results to another table. This approach is heavily inspired by the book Designing Machine Learning Systems by Chip Huyen , a go-to resource for any ML Engineer.

Machine Learning 52

Machine Learning DevOps ML ML Engineer 52

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Chatbots Life

MAY 16, 2023

10Clouds is a software consultancy, development, ML, and design house based in Warsaw, Poland. Services : Mobile app development, web development, blockchain technology implementation, 360′ design services, DevOps, OpenAI integrations, machine learning, and MLOps.

Generative AI 92

Generative AI Chatbots Conversational AI Software Development 92

AWS Machine Learning Blog

AUGUST 16, 2023

The introduction of generative AI provides another opportunity for Thomson Reuters to work with customers and once again advance how they do their work, helping professionals draw insights and automate workflows, enabling them to focus their time where it matters most.

Large Language Models 96

Large Language Models Machine Learning Generative AI ML 96

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

ML engineers Develop model deployment pipelines and control the model deployment processes. Second, the platform gives data science teams the autonomy to create accounts, provision ML resources and access ML resources as needed, reducing resource constraints that often hinder their work.

ML 63

ML Data Scientist ML Engineer Data Science 63

AWS Machine Learning Blog

NOVEMBER 9, 2023

Automated retraining mechanism – The training pipeline built with SageMaker Pipelines is triggered whenever a data drift is detected in the inference pipeline. This will enable us to test the pattern to trigger automated retraining of the model. csv – Will be used to train the first version of model. data/ mammo-train-dataset-part2.csv

Data Drift 122

Data Drift Auto-complete ML Automation 122

Heartbeat

OCTOBER 5, 2023

MLOps, often seen as a subset of DevOps (Development Operations), focuses on streamlining the development and deployment of machine learning models. Where is LLMOps in DevOps and MLOps In MLOps, engineers are dedicated to enhancing the efficiency and impact of ML model deployment.

Large Language Models 52

Large Language Models DevOps Machine Learning Prompt Engineer 52

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. The AI/ML teams are in the services department under infrastructure teams but related to AI, and a few AI teams are working on ML-based solutions that clients can consume. On top of the teams, they also have departments.

Machine Learning 59

Machine Learning Data Scientist Automation ML 59

AWS Machine Learning Blog

AUGUST 31, 2023

Implementing these guardrails is getting harder for enterprises because the ML processes and activities within enterprises are becoming more complex due to the inclusion of deeply involved processes that require contributions from multiple stakeholders and personas. His core area of focus includes Machine Learning, DevOps, and Containers.

ML 52

ML Data Scientist Machine Learning Data Science 52

AWS Machine Learning Blog

JUNE 14, 2023

Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. SageMaker Projects helps organizations set up and standardize environments for automating different steps involved in an ML lifecycle.

ML 103

ML Data Scientist Automation DevOps 103

The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

ML 98

ML Machine Learning Metadata Data Science 98

Snorkel AI

MARCH 9, 2023

Kubernetes is an open-source container orchestration system built to automate deploying, scaling, and managing containerized applications. Horizontal Autoscaling: The horizontal autoscaler in k8s will watch the CPUs and scale up or down the number of pods running a particular service based on load, completely automated.

DevOps 52

DevOps ML Engineer ML Data Scientist 52