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 261

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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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.

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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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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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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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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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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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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

Towards AI

FEBRUARY 10, 2024

Photo by CDC on Unsplash The Data Scientist Show, by Daliana Liu, is one of my favorite YouTube channels. Unlike many other data science programs that are very technical and require concentration to follow through, Daliana’s talk show strikes a delicate balance between profession and relaxation.

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Data Scientist Software Development Data Science Machine Learning 98

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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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.

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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 Prompt Engineer Prompt Engineering 52

O'Reilly Media

OCTOBER 19, 2021

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. Data Science Layers.

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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

Heartbeat

JULY 31, 2023

Comet is an all-inclusive cloud-based platform that can help data scientists and machine learning engineers streamline their workflow and improve the efficiency of their experimentation process. However, training and deploying large-scale machine learning models can be a complex and time-consuming process. This is where Comet comes in.

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Heartbeat

FEBRUARY 29, 2024

Furthermore, the software development process has evolved to embrace Agile methodologies, DevOps practices, and continuous integration/continuous delivery (CI/CD) pipelines. Written in older languages, legacy codebases can be particularly challenging to work with and update.

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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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Mlearning.ai

MARCH 16, 2023

You have probably heard the term DevOps in conventional software development. Data Scientists and Machine Learning engineers in an act of “jealousy” adopted the concept and changed the term to MLOps. Is there a way or a method or a tool that can help bring order to this chaotic situation?

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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 112

AWS Machine Learning Blog

DECEMBER 13, 2023

Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle.

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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? Quite fun, quite chaotic at times.

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ML Data Scientist Machine Learning Data Science 52

Pickl AI

JULY 8, 2024

Features and Capabilities of Time Series Databases TSDBs offer a rich set of functionalities that empower developers and data scientists to effectively manage and analyse time series data. Here are some key features: High-performance Write and Read Operations TSDBs are optimised for rapid data ingestion and retrieval.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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Heartbeat

MARCH 5, 2024

Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We're committed to supporting and inspiring developers and engineers from all walks of life.

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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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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 ML Engineer 52

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 AI Modeling Automation 240

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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AWS Machine Learning Blog

APRIL 21, 2023

These alarms may need additional analysis by a data scientist, or you can implement additional automation supporting an automatic retraining strategy using new ground truth data by integrating the model training and baselining pipeline with Amazon EventBridge.

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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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Metadata Machine Learning Data Scientist Data Quality 134

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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The MLOps Blog

APRIL 17, 2023

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use. And since you are reading this article, the data scientists you support have probably reached out for help.

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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 98

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

ML 52

ML Data Scientist Software Engineer Machine Learning 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.

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Chatbots Automation Machine Learning DevOps 112

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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