Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

ML 151

ML DevOps Data Scientist ML Engineer 151

AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. There were noticeable challenges when running ML workflows in the cloud.

Data Scientist 117

Data Scientist ML Engineer Machine Learning Data Science 117

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 136

Machine Learning DevOps Data Scientist Data Quality 136

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.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

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.

ML 107

ML Metadata Data Scientist DevOps 107

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.

Python 97

Python DevOps Machine Learning Data Science 97

AWS Machine Learning Blog

DECEMBER 1, 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. He focuses on helping customers build and optimize their AI/ML solutions on Amazon SageMaker.

Machine Learning 121

Machine Learning ML DevOps Data Science 121

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.

ML 128

ML Data Scientist ML Engineer Data Science 128

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. What is MLOps? Learn more lessons from the field with Comet experts.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

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. Note that the architecture shown in this post assumes you want to take full control of the whole data science process.

DevOps 118

DevOps Automation ML Computer Vision 118

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.

ML 113

ML Machine Learning Data Science Data Drift 113

AWS Machine Learning Blog

AUGUST 22, 2024

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. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

Generative AI 124

Generative AI Metadata Python ML 124

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 126

Generative AI Prompt Engineering Prompt Engineer ML 126

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 engineers Develop model deployment pipelines and control the model deployment processes.

ML 63

ML Data Scientist ML Engineer Data Science 63

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

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

AUGUST 31, 2023

Architecture overview The architecture is implemented as follows: Data Science Account – Data Scientists conduct their experiments in SageMaker Studio and build an MLOps setup to deploy models to staging/production environments using SageMaker Projects. His core area of focus includes Machine Learning, DevOps, and Containers.

ML 119

ML Data Scientist Machine Learning Data Science 119

Flipboard

MARCH 7, 2023

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. Check that the SageMaker image selected is a Conda-supported first-party kernel image such as “Data Science.” Choose Open Launcher.

Python 123

Python Data Science Data Scientist ML 123

Chatbots Life

MAY 16, 2023

10Clouds is a software consultancy, development, ML, and design house based in Warsaw, Poland. Services : Mobile app development, web development, blockchain technology implementation, 360′ design services, DevOps, OpenAI integrations, machine learning, and MLOps.

Generative AI 92

Generative AI Chatbots Conversational AI Software Development 92

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. is an experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover.

Software Engineer 52

Software Engineer Machine Learning ETL Data Scientist 52

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

ML 86

ML Computer Vision Data Scientist Machine Learning 86

Mlearning.ai

JULY 29, 2023

This approach is heavily inspired by the book Designing Machine Learning Systems by Chip Huyen , a go-to resource for any ML Engineer. I used Azure DevOps for this case but Github is perfectly fine. ML inference written to the resignated table).

Machine Learning 52

Machine Learning DevOps ML ML Engineer 52

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.

Large Language Models 52

Large Language Models DevOps Machine Learning Prompt Engineering 52

AWS Machine Learning Blog

AUGUST 16, 2023

Harpreet Singh Baath is a Senior Cloud and DevOps Engineer at Thomson Reuters Labs, where he helps research engineers and scientists develop machine learning solutions on cloud platforms. Prior to this role, he was the Head of Data Science for Amazon’s EU Customer Service.

Large Language Models 93

Large Language Models Machine Learning Generative AI ML 93

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.

ML 123

ML Automation Metadata Software Development 123

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.

Data Drift 121

Data Drift Auto-complete ML Automation 121

AWS Machine Learning Blog

APRIL 11, 2023

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. Alberto Menendez is an Associate DevOps Consultant in Professional Services at AWS.

IDP 89

IDP Explainability Big Data ML 89

AWS Machine Learning Blog

AUGUST 31, 2023

Architecture overview The architecture is implemented as follows: Data Science Account – Data Scientists conduct their experiments in SageMaker Studio and build an MLOps setup to deploy models to staging/production environments using SageMaker Projects. 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.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

The MLOps Blog

OCTOBER 3, 2023

So I was able to get from growth hacking to data analytics, then data analytics to data science, and then data science to MLOps. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. How do I get this model in production?

ML 52

ML Data Scientist Machine Learning Data Science 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. As you’ve been running the ML data platform team, how do you do that? Stefan: Yeah.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

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. Kale v0.7.0.

ML 98

ML Machine Learning Metadata Data Science 98

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.

ML 100

ML Data Scientist Automation DevOps 100

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.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

The MLOps Blog

DECEMBER 28, 2022

Hence, the ML teams must have a mix of strong data architects and engineering experts that can successfully operationalize the ML model. MLOps cycle | Source How to organize ML team Centralized ML team People from different fields like engineering, product, DevOps, and ML all come together under one big team.

ML 78

ML Data Scientist ML Engineer Machine Learning 78

AWS Machine Learning Blog

JANUARY 13, 2023

Bring a single pane of glass for ML activities. TR’s AI Platform microservices are built with Amazon SageMaker as the core engine, AWS serverless components for workflows, and AWS DevOps services for CI/CD practices. TR automated this deployment using AWS DevOps services like AWS CodePipeline and AWS CodeBuild.

ML 93

ML Data Scientist Machine Learning Metadata 93

Mlearning.ai

MAY 3, 2023

ML/AI Enthusiasts, and Learners Citizen Data Scientists who prefer a low code solution for quick testing. Experienced Data Scientists who want to try out different use-cases as per their business context for quick prototyping. Data Science Professionals and Consultants involved in building Proof-Of-Concept (POC) projects.

Large Language Models 52

Large Language Models Python Auto-complete Natural Language Processing 52