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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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. –

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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.

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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.

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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.

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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.

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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.

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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.

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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.,

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.

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The MLOps Blog

OCTOBER 3, 2023

I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. Mailchimp seeks to make it easier and to automate it.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

With these tools in hand, the next challenge is to integrate LLM evaluation into the Machine Learning and Operation (MLOps) lifecycle to achieve automation and scalability in the process. Those metrics serve as a useful tool for automated evaluation, providing quantitative measures of lexical similarity between generated and reference text.

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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.

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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.

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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.

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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.

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