Unite.AI

JUNE 19, 2024

Understanding MLOps Before delving into the intricacies of becoming an MLOps Engineer, it's crucial to understand the concept of MLOps itself. ML Experimentation and Development : Implement proof-of-concept models, data engineering, and model engineering.

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Software Engineer DevOps Machine Learning ML 182

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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Machine Learning ML Engineer DevOps ML 130

AWS Machine Learning Blog

OCTOBER 28, 2024

You can use this framework as a starting point to monitor your custom metrics or handle other unique requirements for model quality monitoring in your AI/ML applications. Data Scientist at AWS, bringing a breadth of data science, ML engineering, MLOps, and AI/ML architecting to help businesses create scalable solutions on AWS.

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ML Metadata Data Scientist DevOps 108

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. ML model experimentation is one of the sub-components of the MLOps architecture.

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Data Scientist ML Engineer Machine Learning Data Science 118

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

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 might also help with data preparation and cleaning. What is MLOps?

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DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

FEBRUARY 27, 2024

Each product translates into an AWS CloudFormation template, which is deployed when a data scientist creates a new SageMaker project with our MLOps blueprint as the foundation. These are essential for monitoring data and model quality, as well as feature attributions.

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Machine Learning DevOps Data Scientist Data Quality 136

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 129

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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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.

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AWS Machine Learning Blog

DECEMBER 1, 2023

As we continue to innovate to increase data science productivity, we’re excited to announce the improved SageMaker Studio experience, which allows users to select the managed Integrated Development Environment (IDE) of their choice, while having access to the SageMaker Studio resources and tooling across the IDEs.

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Machine Learning ML DevOps Data Science 122

AWS Machine Learning Blog

AUGUST 22, 2024

You can write your ML function as you would for any ML project. After being tested locally or as a training job, a data scientist or practitioner who is an expert on SageMaker can convert the function to a SageMaker pipeline step by adding a @step decorator.

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Pickl AI

JANUARY 30, 2023

Since the rise of Data Science, it has found several applications across different industrial domains. However, the programming languages that work at the core of Data Science play a significant role in it. Hence for an individual who wants to excel as a data scientist, learning Python is a must.

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Python DevOps Machine Learning Data Science 97

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. For instance, data scientists might want to monitor and work with their familiar notebook environment. So if you have a DevOps challenge or want to go for a run: let him know.

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DevOps Automation ML Computer Vision 119

AWS Machine Learning Blog

FEBRUARY 7, 2025

In this post, we assign the functions in terms of the ML lifecycle to each role as follows: Lead data scientist Provision accounts for ML development teams, govern access to the accounts and resources, and promote standardized model development and approval process to eliminate repeated engineering effort.

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ML Data Scientist ML Engineer Data Science 64

The MLOps Blog

JUNE 27, 2023

Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. Check out the Kubeflow documentation.

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Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These data owners are focused on providing access to their data to multiple business units or teams. Data science team – Data scientists need to focus on creating the best model based on predefined key performance indicators (KPIs) working in notebooks. The following figure depicts an example architecture.

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Generative AI Prompt Engineering Prompt Engineer ML 127

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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ML Data Scientist Machine Learning Data Science 120

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. She has a decade of experience in DevOps, infrastructure, and ML.

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ML Computer Vision Data Scientist Machine Learning 87

Flipboard

MARCH 7, 2023

The main benefit is that a data scientist can choose which script to run to customize the container with new packages. There are also limited options for ad hoc script customization by users, such as data scientists or ML engineers, due to permissions of the user profile execution role.

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Python Data Science Data Scientist Automation 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. About the Authors Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS.

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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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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. By adopting these patterns, data scientists can dedicate more attention to analyzing the model’s impact and performance.

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Software Engineer Machine Learning ETL Data Scientist 52

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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Machine Learning Data Drift Data Science Data Scientist 52

Snorkel AI

MARCH 9, 2023

Containerizing slows iteration speed, which can be a particular challenge for data scientists and ML engineers. For ML engineers, working around the nuances of containerization, storage, and logging in a k8s environment is challenging. Using k8s also requires significant DevOps overhead.

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DevOps ML Engineer ML Data Scientist 52

Snorkel AI

MARCH 9, 2023

Containerizing slows iteration speed, which can be a particular challenge for data scientists and ML engineers. For ML engineers, working around the nuances of containerization, storage, and logging in a k8s environment is challenging. Using k8s also requires significant DevOps overhead.

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DevOps ML Engineer ML Data Scientist 52

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 124

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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Large Language Models DevOps Machine Learning Prompt Engineering 52

The MLOps Blog

OCTOBER 3, 2023

So I tell people honestly, I’ve spent the last eight years working up and down the data and ML value chain effectively – a fancy way of saying “job hopping.” How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right? And later, an MLOps engineer.

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ML Data Scientist Machine Learning Data Science 52

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

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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Machine Learning ML Automation Data Scientist 52

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. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

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ML Data Scientist Software Engineer Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

Machine Learning Engineer with AWS Professional Services. 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.

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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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Machine Learning Data Scientist Automation ML 59

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 Data Scientist Automation DevOps 101

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

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. 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.

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