Analytics Vidhya

DECEMBER 11, 2022

Introduction Machine learning (ML) has become an increasingly important tool for organizations of all sizes, providing the ability to learn and improve from data automatically. However, successfully deploying and managing ML in production can be challenging, requiring careful coordination between data scientists and […].

Machine Learning 360

Machine Learning Data Scientist Data Science ML 360

AWS Machine Learning Blog

FEBRUARY 21, 2025

Steep learning curve for data scientists: Many of Rockets data scientists did not have experience with Spark, which had a more nuanced programming model compared to other popular ML solutions like scikit-learn. This created a challenge for data scientists to become productive.

Data Science 70

Data Science Data Scientist Data Ingestion DevOps 70

IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring. Generative AI relies on foundation models to create a scalable process.

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Data Science DevOps Machine Learning Data Scientist 170

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.

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Machine Learning Data Scientist Data Science DevOps 269

AWS Machine Learning Blog

JANUARY 13, 2025

The following diagram shows the reference architecture for various personas, including developers, support engineers, DevOps, and FinOps to connect with internal databases and the web using Amazon Q Business. You can also assume a persona such as FinOps or DevOps and get personalized recommendations or responses. The best part?

DevOps 100

DevOps Generative AI Automation Data Scientist 100

Unite.AI

NOVEMBER 17, 2023

AI recommends safer libraries, DevOps methods, and a lot more. Effective implementation of AI demands a collaborative effort across multiple disciplines, uniting developers, security experts, data scientists, and quality assurance professionals. Finding the right balance between automated and manual oversight is vital.

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Artificial Intelligence Artificial Intelligence DevOps Automation 299

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.

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Data Scientist Python ML Automation 87

AWS Machine Learning Blog

MARCH 21, 2025

We provide the following request: sample_prompt = f""" You are a data scientist expert who has perfect vision and pay a lot of attention to details. Renu has a strong passion for learning with her area of specialization in DevOps. We use the following graph. samples/2003.10304/page_5.png"

Metadata 106

Metadata Convolutional Neural Networks Generative AI Data Scientist 106

Flipboard

NOVEMBER 22, 2024

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

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ML Data Science Metadata DevOps 130

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.

Software Engineer 182

Software Engineer DevOps Machine Learning ML 182

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 151

AWS Machine Learning Blog

OCTOBER 16, 2024

This pipeline provides self-serving capabilities for data scientists to track ML experiments and push new models to an S3 bucket. It offers flexibility for data scientists to conduct shadow deployments and capacity planning, enabling them to seamlessly switch between models for both production and experimentation purposes.

Machine Learning 81

Machine Learning Data Scientist Deep Learning DevOps 81

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.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

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.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

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. About the Authors Joe King is a Sr.

ML 104

ML Metadata Data Scientist DevOps 104

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 131

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.

Machine Learning 134

Machine Learning DevOps Data Scientist Data Quality 134

AWS Machine Learning Blog

SEPTEMBER 4, 2024

DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the quality of the obtained model is satisfactory, uploaded to a model registry. curl for transmitting data with URLs. They often work with DevOps engineers to operate those pipelines.

DevOps 101

DevOps ML Engineer ML Metadata 101

IBM Journey to AI blog

SEPTEMBER 20, 2023

This allows for sensitive labeled data (or enterprise crown-jewel data) to safely stay within the enterprise operational environment while also reducing data transfer costs. Using a full-stack approach for deploying applications to the edge, a data scientist can perform fine-tuning, testing and deployment of the models.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

MARCH 10, 2025

The use of multiple external cloud providers complicated DevOps, support, and budgeting. Operational consolidation and reliability Post-migration, our DevOps and SRE teams see 20% less maintenance burden and overheads. These operational inefficiencies meant that we had to revisit our solution architecture.

DevOps 84

DevOps Metadata Auto-complete Automation 84

Flipboard

FEBRUARY 10, 2025

About the Authors Surya Kari is a Senior Generative AI Data Scientist at AWS, specializing in developing solutions leveraging state-of-the-art foundation models. He is currently focused on combining his background in software engineering, DevOps, and machine learning to help customers deliver machine learning workflows at scale.

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LLM AI AI Python 159

Marktechpost

JUNE 25, 2024

Gemini for Data Scientists and Analysts This course teaches you how to use Gemini to analyze customer data, predict product sales, and develop marketing strategies in BigQuery. It includes videos and hands-on labs to improve data analysis and machine learning workflows.

DevOps 107

DevOps Data Analysis Large Language Models Generative AI 107

Marktechpost

FEBRUARY 27, 2024

Feast offers a comprehensive solution by managing an offline store for historical data processing, a low-latency online store for real-time predictions, and a feature server for serving pre-computed features online. In conclusion, Feast emerges as a robust solution to the challenges of managing and serving machine learning features.

Machine Learning 103

Machine Learning DevOps Data Scientist ML 103

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 107

Data Scientist Computer Vision DevOps ML 107

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.

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Machine Learning ML Engineer DevOps ML 127

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.

Data Drift 124

Data Drift Machine Learning Data Scientist ML 124

IBM Journey to AI blog

SEPTEMBER 5, 2024

Automated classification : Classifies data based on predefined categories, such as personal identifiable information (PII), financial data, intellectual property or confidential information. DevOps and DataOps are practices that emphasize developing a collaborative culture.

Metadata 188

Metadata Data Quality Auto-classification DevOps 188

AWS Machine Learning Blog

FEBRUARY 29, 2024

Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. 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.

Data Scientist 123

Data Scientist DevOps AI AI 123

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. It improves data scientists’ productivity and model development processes.

Data Scientist 114

Data Scientist ML Engineer Machine Learning Data Science 114

Becoming Human

MAY 11, 2023

Some of the main challenges include: Lack of Integration: MLOps projects require close collaboration between data scientists, software developers, and IT operations teams. DevOps and DataOps: DevOps and DataOps are related approaches that emphasize collaboration between software developers and IT operations teams.

Machine Learning 78

Machine Learning DevOps Software Development Data Scientist 78

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 121

TransOrg Analytics

MARCH 17, 2025

Enhancing Digital Transformation Capabilities: As global innovation accelerates, TransOrg Analytics is e expanding its digital consulting and transformation services to help enterprises navigate cloud adoption, cybersecurity challenges, and the complexities of DevOps, LLMOps, and MLOps. Why Partner with TransOrg Analytics ?

Machine Learning 52

Machine Learning Data Science DevOps Automation 52

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.

Chatbots 125

Chatbots Automation Machine Learning DevOps 125

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.

DevOps 89

DevOps Data Science Data Scientist ML 89

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.

ML 126

ML Data Scientist ML Engineer Data Science 126

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. Figure 1: Venn diagram showing the relationship among the MLOps-related fields [Wikipedia].

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Machine Learning DevOps Software Development ML 98

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. For instance, data scientists might want to monitor and work with their familiar notebook environment.

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DevOps Automation ML Computer Vision 115