AWS Machine Learning Blog

JANUARY 5, 2024

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 and DevOps teams may face challenges managing these isolated tool stacks and systems.

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Data Science Data Drift DevOps Auto-complete 133

AWS Machine Learning Blog

JANUARY 23, 2025

For individual data scientists seeking a self-service experience, we recommend that you use the native Docker support in SageMaker Studio, as described in Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support. Ajay Raghunathan is a Machine Learning Engineer at AWS.

Automation 106

Automation DevOps Machine Learning ML 106

Analytics Vidhya

APRIL 2, 2022

This article was published as a part of the Data Science Blogathon. Introduction MLOps, as a new area, is quickly gaining traction among Data Scientists, Machine Learning Engineers, and AI enthusiasts. MLOps are required for anything to reach production.

Machine Learning 269

Machine Learning Data Scientist Data Science DevOps 269

Unite.AI

JUNE 19, 2024

MLOps, or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. ML Operations : Deploy and maintain ML models using established DevOps practices.

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Software Engineer DevOps Machine Learning ML 182

AWS Machine Learning Blog

NOVEMBER 26, 2024

Amazon SageMaker Studio provides a single web-based visual interface where data scientists create dedicated workspaces to perform all ML development steps required to prepare data and build, train, and deploy models. In this solution, a JupyterLab space has been included in the infrastructure stack.

Data Scientist 89

Data Scientist Python ML Automation 89

Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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ML DevOps Data Scientist ML Engineer 153

IBM Journey to AI blog

SEPTEMBER 20, 2023

Using a full-stack approach for deploying applications to the edge, a data scientist can perform fine-tuning, testing and deployment of the models. The software stack included the Red Hat OpenShift Container Platform and Red Hat OpenShift Data Science.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

Pickl AI

APRIL 1, 2025

Cloud computing and data science are interconnected, making data-related cloud skills highly valuable. Understanding the Role of a Cloud Engineer Cloud computing has changed how businesses store and access data. Understand DevOps and CI/CD Cloud Engineers often work closely with DevOps teams to ensure smooth deployments.

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DevOps Data Science Automation Big Data 52

AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

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Machine Learning DevOps Data Scientist Data Quality 136

AWS Machine Learning Blog

OCTOBER 28, 2024

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. He is a technology enthusiast and a builder with a core area of interest in AI/ML, data analytics, serverless, and DevOps. Raju Patil is a Sr.

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ML Metadata Data Scientist DevOps 108

Marktechpost

JULY 5, 2024

Programming for Data Science with Python This course series teaches essential programming skills for data analysis, including SQL fundamentals for querying databases and Unix shell basics. Students also learn Python programming, from fundamentals to data manipulation with NumPy and Pandas, along with version control using Git.

Data Science 60

Data Science Machine Learning Data Analysis Software Engineer 60

AWS Machine Learning Blog

JULY 8, 2024

MLOps practitioners have many options to establish an MLOps platform; one among them is cloud-based integrated platforms that scale with data science teams. TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively.

Data Scientist 118

Data Scientist ML Engineer Data Science Machine Learning 118

Heartbeat

FEBRUARY 1, 2023

MLOps acts as the link between data scientists and the production team’s operations (a team consisting of machine learning engineers, software engineers, and IT operations professionals) as they work together to develop ML models and supervise the use of ML models in production. They might also help with data preparation and cleaning.

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DevOps Machine Learning Data Scientist Software Engineer 96

The MLOps Blog

JANUARY 23, 2023

Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. Model monitoring tools will merge with the DevOps monitoring stack.

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DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

OCTOBER 16, 2023

Although the solution did alleviate GPU costs, it also came with the constraint that data scientists needed to indicate beforehand how much GPU memory their model would require. Furthermore, DevOps were burdened with manually provisioning GPU instances in response to demand patterns.

Data Scientist 111

Data Scientist Computer Vision DevOps ML 111

TransOrg Analytics

MARCH 17, 2025

This evolution has spurred a significant demand for specialized skills in areas such as AI, data engineering, machine learning, data science, cybersecurity, and cloud computing. Key Drivers of This Hiring Boom Evolution Beyond Support Functions GCCs are shedding their old image as mere cost-arbitrage centers.

Machine Learning 52

Machine Learning Data Science DevOps Automation 52

AWS Machine Learning Blog

OCTOBER 3, 2024

This approach led to data scientists spending more than 50% of their time on operational tasks, leaving little room for innovation, and posed challenges in monitoring model performance in production. This feature integrates with Amazon SageMaker Experiments to provide data scientists with insights into the tuning process.

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DevOps Data Science Data Scientist ML 92

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. Data scientists from ML teams across different business units federate into their team’s development environment to build the model pipeline.

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ML Data Scientist ML Engineer Data Science 129

Unite.AI

JUNE 24, 2024

It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. Model Training Frameworks This stage involves the process of creating and optimizing predictive models with labeled and unlabeled data.

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Data Drift Machine Learning Data Scientist ML 124

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

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. It provides a high-level API that makes it easy to define and execute data science workflows.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

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

Towards AI

AUGUST 16, 2023

Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). The ideal MLOps engineer would have some experience with several MLOps and/or DevOps platforms. Without a central place to collaborate and manage the model lifecycle, data science teams will encounter challenges managing model stages.

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Machine Learning DevOps Software Development ML 98

AWS Machine Learning Blog

MAY 1, 2024

Kara Yang is a data scientist at AWS Professional Services, adept at leveraging cloud computing, machine learning, and Generative AI to tackle diverse industry challenges. Praveen Kumar Jeyarajan is a Principal DevOps Consultant at AWS, supporting Enterprise customers and their journey to the cloud.

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Chatbots Automation Machine Learning DevOps 129

AWS Machine Learning Blog

AUGUST 22, 2024

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. 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.

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Generative AI Metadata Python ML 125

AWS Machine Learning Blog

OCTOBER 2, 2023

For instance, data labeling and training has a strong data science focus, edge deployment requires an Internet of Things (IoT) specialist, and automating the whole process is usually done by someone with a DevOps skill set. 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

AUGUST 31, 2023

In addition to data engineers and data scientists, there have been inclusions of operational processes to automate & streamline the ML lifecycle. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

ML 120

ML Data Scientist Machine Learning Data Science 120

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. Choose Open Launcher.

Python 123

Python Data Science Data Scientist Automation 123

Marktechpost

JULY 17, 2023

The company’s H20 Driverless AI streamlines AI development and predictive analytics for professionals and citizen data scientists through open source and customized recipes. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.

Machine Learning 98

Machine Learning Data Mining Data Scientist Data Science 98

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.

Generative AI 127

Generative AI Prompt Engineer Prompt Engineering ML 127

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. Conduct exploratory analysis and data preparation.

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ML Machine Learning Data Science Data Drift 114

Mlearning.ai

JULY 31, 2023

Extension Of Devops MLOps is an extension of DevOps. DevOps aims to streamline the development and operation of software applications, while MLOps focuses on the machine learning lifecycle. MLOps extends DevOps by including data science practices, like model training and data preprocessing.

DevOps 52

DevOps Machine Learning Data Scientist NLP 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

Data science teams often face challenges when transitioning models from the development environment to production. Usually, there is one lead data scientist for a data science group in a business unit, such as marketing. ML Dev Account This is where data scientists perform their work.

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ML Data Scientist ML Engineer Data Science 64

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

ODSC - Open Data Science

SEPTEMBER 11, 2024

This containerization minimizes the complexity of managing dependencies and configurations, allowing data scientists to focus on model development rather than infrastructure concerns. DevOps Integration OpenShift is designed with DevOps in mind, providing a seamless pipeline for continuous integration and continuous deployment (CI/CD).

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ML DevOps Data Science Machine Learning 40

AWS Machine Learning Blog

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account).

Data Scientist 91

Data Scientist Data Quality Python ML 91

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. She has a decade of experience in DevOps, infrastructure, and ML. These iterative challenges can hinder progress and slow down projects.

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ML Computer Vision Data Scientist Machine Learning 87