DataRobot Blog

MARCH 16, 2023

An AI platform that works well with a broad enterprise ecosystem: A platform that seamlessly integrates with the substantial investments businesses have already made in infrastructure, practitioner tools, data platforms and business applications.

AI 98

AI AI DevOps Neural Network 98

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 135

ML Data Scientist ML Engineer Data Science 135

Pickl AI

JUNE 29, 2023

Furthermore, The platform’s versatility extends beyond data analysis. Advantages of Using Splunk Real-time Visibility One of the significant advantages of Splunk is its ability to provide real-time data visibility. Thus, it lets users gain insights from vast data in real time.

Big Data 52

Big Data DevOps Data Analysis Machine Learning 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

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 98

Data Mining Machine Learning Data Scientist Artificial Intelligence 98

AWS Machine Learning Blog

DECEMBER 18, 2024

Claudia Sacco is an AWS Professional Solutions Architect at BIP xTech, collaborating with Fastwebs AI CoE and specialized in architecting advanced cloud and data platforms that drive innovation and operational excellence. He has expertise in AWS cloud services, DevOps practices, security, data analytics and generative AI.

Large Language Models 76

Large Language Models Data Scarcity LLM Generative AI 76

ODSC - Open Data Science

JULY 19, 2023

Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure data platforms in this diagram are neither exhaustive nor prescriptive.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

Marktechpost

JULY 17, 2023

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

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. As you’ve been running the ML data platform team, how do you do that? Stefan: Yeah. Thanks for having me.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

OCTOBER 3, 2023

I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. You shifted straight from data science, if I understand correctly.

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

MARCH 21, 2023

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. Checking at intervals to make sure that model performance isn’t degrading in production is a good MLOps practice for both teams and platforms.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69