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 179

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.

Machine Learning 126

Machine Learning DevOps Data Scientist Data Quality 126

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.

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Large Language Models DevOps Data Science AI Modeling 238

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

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.

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Data Science Machine Learning Data Analysis Software Engineer 62

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

Flipboard

NOVEMBER 22, 2024

For example, in the bank marketing use case, the management account would be responsible for setting up the organizational structure for the bank’s data and analytics teams, provisioning separate accounts for data governance, data lakes, and data science teams, and maintaining compliance with relevant financial regulations.

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ML Data Science Metadata DevOps 132

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 110

Data Scientist ML Engineer Machine Learning Data Science 110

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.

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Data Scientist Computer Vision DevOps Python 103

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 DevOps 122

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 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 85

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.

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Machine Learning Metadata Data Scientist Data Quality 134

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 117

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 DevOps ML Computer Vision 116

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 119

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 82

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 110

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 114

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.

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Python Data Scientist Data Science Automation 123

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.

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ML Data Scientist Machine Learning Data Science 109

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.

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Data Mining Machine Learning Data Scientist Artificial Intelligence 96

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 122

Generative AI Prompt Engineering Prompt Engineer AI 122

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.

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DevOps Machine Learning Data Scientist Automation 52

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 108

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

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

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 77

Data Scientist Python Automation ML 77

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 81

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 101

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).

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Data Scientist Data Quality Python ML 76

Pickl AI

APRIL 27, 2023

Data Analytics Trend Report 2023: Data Science is an interdisciplinary field that focuses on filtering the data, categorizing it, and deriving valuable insights. As the importance of Data Science and its role continues to grow, so does the demand for data professionals. billion by 2030.

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Data Science Artificial Intelligence Artificial Intelligence Python 52

DataRobot Blog

FEBRUARY 11, 2022

These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. And DataRobot’s MLOps accelerates getting Machine Learning into production, thereby reducing spending on infrastructure and sparing data science talent from mundane tasks.

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Data Drift Machine Learning DevOps Data Scientist 52

ODSC - Open Data Science

OCTOBER 9, 2024

AI for DevOps and CI/CD: Streamlining the Pipeline Continuous Integration and Continuous Delivery (CI/CD) are essential components of modern software development, and AI is now helping to optimize this process. In the world of DevOps, AI can help monitor infrastructure, analyze logs, and detect performance bottlenecks in real-time.

Software Engineer 40

Software Engineer Software Development DevOps Machine Learning 40

The MLOps Blog

SEPTEMBER 7, 2023

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. This situation is not different in the ML world. Aside neptune.ai

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Software Engineer Machine Learning ETL Data Scientist 52

AWS Machine Learning Blog

JULY 25, 2024

He is currently focused on combining his background in software engineering, DevOps, and machine learning to help customers deliver machine learning workflows at scale. Bobby Lindsey is a Machine Learning Specialist at Amazon Web Services. He’s been in technology for over a decade, spanning various technologies and multiple roles.

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Conversational AI Machine Learning AI AI 122