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”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Some are my 3–4 year bets.

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DevOps Metadata Software Engineer Data Scientist 59

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. Exciting News!

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Large Language Models DevOps Prompt Engineer Prompt Engineering 52

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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Generative AI Prompt Engineer Prompt Engineering AI 105

AWS Machine Learning Blog

NOVEMBER 21, 2023

His team of scientists and ML engineers is responsible for providing contextually relevant and personalized search results to Amazon Music customers. Siddharth spent early part of his career working with bay area ad-tech startups. Tarun Sharma is a Software Development Manager leading Amazon Music Search Relevance.

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AWS Machine Learning Blog

DECEMBER 13, 2023

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. 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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ML Automation Metadata Software Development 102

AWS Machine Learning Blog

JUNE 8, 2023

Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. Solutions Architect at Amazon Web Services with specialization in DevOps and Observability. He leads the NYC machine learning and AI meetup. In his spare time, he enjoys offshore sailing and playing jazz. Mahesh Birardar is a Sr.

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AWS Machine Learning Blog

JUNE 27, 2023

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. Endpoints had to be deployed manually as well.

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DevOps ML Machine Learning Automation 96

AWS Machine Learning Blog

OCTOBER 20, 2023

The architecture maps the different capabilities of the ML platform to AWS accounts. 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.

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ML Data Scientist ML Engineer Data Science 104

AWS Machine Learning Blog

NOVEMBER 9, 2023

She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Prior to this role, she led multiple initiatives as a data scientist and ML engineer with top global firms in the financial and retail space. Saswata Dash is a DevOps Consultant with AWS Professional Services.

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Data Drift Auto-complete ML Automation 101

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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Data Scientist DevOps AI AI 101

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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Machine Learning Automation Data Scientist ML 59

AWS Machine Learning Blog

OCTOBER 2, 2023

Use case: Inspecting the quality of metal tags As an ML engineer, it’s important to understand the business case you are working on. With a passion for automation, Joerg has worked as a software developer, DevOps engineer, and Site Reliability Engineer in his pre-AWS life.

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DevOps Automation ML Computer Vision 93

Mlearning.ai

AUGUST 18, 2023

This is on purpose, because most common use cases can be achieved through the patterns we propose in this blog. My interpretation to MLOps is similar to my interpretation of DevOps. As a software engineer your role is to write code for a certain cause. As an ML engineer you’re in charge of some code/model.

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DevOps Python ML ML Engineer 52

The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. Related post MLOps Is an Extension of DevOps. is an experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover.

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Software Engineer Machine Learning ETL ML Engineer 52

AWS Machine Learning Blog

AUGUST 16, 2023

Harpreet Singh Baath is a Senior Cloud and DevOps Engineer at Thomson Reuters Labs, where he helps research engineers and scientists develop machine learning solutions on cloud platforms. He is passionate about efficiency and cost-effectiveness, ensuring that cloud resources are utilized optimally.

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Large Language Models Machine Learning Generative AI LLM 74

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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ML Machine Learning Data Science Data Drift 90

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. For example, Shopify published a blog post about Merlin.

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ML Data Scientist Machine Learning Data Science 52

AWS Machine Learning Blog

APRIL 11, 2023

Alberto Menendez is an Associate DevOps Consultant in Professional Services at AWS. Rajesh Ramchander is a Senior Data & ML Engineer in Professional Services at AWS. He helps accelerate customers´ journeys to the cloud.

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IDP Explainability Big Data ML 70

AWS Machine Learning Blog

AUGUST 31, 2023

His core area of focus includes Machine Learning, DevOps, and Containers. Ram Vittal is a Principal ML Solutions Architect at AWS. He is a builder who enjoys helping customers accomplish their business needs and solve complex challenges with AWS solutions and best practices.

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ML Data Scientist Machine Learning Automation 93

The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. Stefan: Thankfully, Stitch Fix had a blog that had a reasonable amount of readership. Stefan: Yeah.

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ML Data Scientist Software Engineer Machine Learning 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.

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ML Automation Data Scientist DevOps 78

The MLOps Blog

JUNE 27, 2023

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders.

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Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

AUGUST 31, 2023

His core area of focus includes Machine Learning, DevOps, and Containers. Ram Vittal is a Principal ML Solutions Architect at AWS. He is a builder who enjoys helping customers accomplish their business needs and solve complex challenges with AWS solutions and best practices.

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ML Data Scientist Machine Learning Automation 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

Data scientists collaborate with ML engineers to transition code from notebooks to repositories, creating ML pipelines using Amazon SageMaker Pipelines, which connect various processing steps and tasks, including pre-processing, training, evaluation, and post-processing, all while continually incorporating new production data.

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LLM Data Scientist Algorithm Generative AI 85

The MLOps Blog

DECEMBER 28, 2022

Hence, the ML teams must have a mix of strong data architects and engineering experts that can successfully operationalize the ML model. MLOps cycle | Source How to organize ML team Centralized ML team People from different fields like engineering, product, DevOps, and ML all come together under one big team.

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ML Data Scientist ML Engineer Machine Learning 78

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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ML Machine Learning Metadata Automation 98

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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Machine Learning Data Scientist ML Metadata 69

Mlearning.ai

MAY 3, 2023

GPT4All Demo (Image by Author) Conclusion In this blog, we walked through the Large Language Models (LLM’s) briefly. If you liked the blog post pls. I have Lead & Managed a team of data analysts, business analysts, data engineers, ML engineers, DevOps engineers, and Data Scientists.

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Large Language Models Python Auto-complete Natural Language Processing 52

AWS Machine Learning Blog

JANUARY 13, 2023

Bring a single pane of glass for ML activities. TR’s AI Platform microservices are built with Amazon SageMaker as the core engine, AWS serverless components for workflows, and AWS DevOps services for CI/CD practices. TR automated this deployment using AWS DevOps services like AWS CodePipeline and AWS CodeBuild.

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ML Data Scientist Machine Learning Metadata 74