Flipboard

NOVEMBER 24, 2023

The service streamlines ML development and production workflows (MLOps) across BMW by providing a cost-efficient and scalable development environment that facilitates seamless collaboration between data science and engineering teams worldwide. This results in faster experimentation and shorter idea validation cycles.

ML 154

ML DevOps Data Scientist ML Engineer 154

ODSC - Open Data Science

JULY 24, 2023

This is done using their “3D” approach: DevOps, design, and digital transformation. Lil Projects Lil Projects provides businesses with a set of services aimed at using data science, automation, and AI to empower companies to optimize their campaigns and generate returns. Get your pass today!

Data Science 52

Data Science Chatbots Automation Big Data 52

IBM Journey to AI blog

JULY 11, 2024

Decision-making AI can help with data-driven decision-making by analyzing large datasets and providing insights that humans might miss. Applied to big data , these advanced analytics can improve strategic planning, risk management and resource allocation.

Artificial Intelligence 124

Artificial Intelligence Artificial Intelligence Automation DevOps 124

IBM Journey to AI blog

JULY 11, 2024

Decision-making AI can help with data-driven decision-making by analyzing large datasets and providing insights that humans might miss. Applied to big data , these advanced analytics can improve strategic planning, risk management and resource allocation.

Artificial Intelligence 100

Artificial Intelligence Artificial Intelligence Automation DevOps 100

AWS Machine Learning Blog

JUNE 25, 2024

She is currently focusing on combining her DevOps and ML background into the domain of MLOps to help customers deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes.

DevOps 115

DevOps Machine Learning LLM Software Engineer 115

ODSC - Open Data Science

JULY 19, 2023

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. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

NOVEMBER 7, 2024

We explored multiple big data processing solutions and decided to use an Amazon SageMaker Processing job for the following reasons: It’s highly configurable, with support of pre-built images, custom cluster requirements, and containers. He is also a cycling enthusiast.

ML 102

ML Machine Learning Automation Data Scientist 102

Marktechpost

JULY 17, 2023

IBM merged the critical capabilities of the vendor into its more contemporary Watson Studio running on the IBM Cloud Pak for Data platform as it continues to innovate. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.

Data Mining 96

Data Mining Machine Learning Data Scientist Artificial Intelligence 96

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 110

ML Data Scientist Machine Learning Automation 110

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. The architecture maps the different capabilities of the ML platform to AWS accounts.

ML 119

ML Data Scientist ML Engineer Data Science 119

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.

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134

Flipboard

MARCH 7, 2023

Check that the SageMaker image selected is a Conda-supported first-party kernel image such as “Data Science.” From the new notebook, choose the “Python 3 (Data Science)” kernel. He develops and codes cloud native solutions with a focus on big data, analytics, and data engineering.

Python 123

Python Data Scientist Data Science Automation 123

AWS Machine Learning Blog

FEBRUARY 2, 2024

Abdullahi holds a MSC in Computer Networking from Wichita State University and is a published author that has held roles across various technology domains such as DevOps, infrastructure modernization and AI. In entered the Big Data space in 2013 and continues to explore that area.

ETL 110

ETL DevOps LLM Generative AI 110

AWS Machine Learning Blog

NOVEMBER 14, 2023

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data.

LLM 109

LLM ML Natural Language Processing Machine Learning 109

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 Automation 52

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. In her spare time, she enjoys reading and being outdoors.

IDP 77

IDP Explainability Big Data DevOps 77

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 119

Data Drift Auto-complete ML Automation 119

AWS Machine Learning Blog

FEBRUARY 21, 2023

In a broader model development picture, models are typically trained in a data science development account. With a background in data science, she has 9 years of experience architecting and building ML applications with customers across industries. She is also the Co-Director of Women In Big Data (WiBD), Denver chapter.

ML 77

ML Software Development Automation Metadata 77

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). He’s a pet lover and is passionate about snowboarding and traveling.

Data Scientist 80

Data Scientist Data Quality Python ML 80

AWS Machine Learning Blog

APRIL 21, 2023

She is currently focusing on combining her DevOps and ML background into the domain of MLOps to help customers deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes.

Data Drift 90

Data Drift Metadata Data Quality ML 90

Unite.AI

MARCH 21, 2024

She has innovated and delivered several product lines and services specializing in distributed systems, cloud computing, big data, machine learning and security. Prior to Amplitude , she was VP of Engineering at Palo Alto Networks. That led me to pursue engineering at Sharif University of Technology in Iran and later get my Ph.D.

DevOps 147

DevOps Data Quality Machine Learning Generative AI 147