Smart Data Collective

NOVEMBER 8, 2020

Learn Cloud Computing. The importance of cloud computing in data engineering cannot be avoided. That said, data engineers should learn how cloud platforms work. Popular cloud platforms include the Microsoft Azure, Google Cloud Platform, and Amazon Web Services. Follow Industry Trends.

Data Engineering 104

Data Engineering Data Engineering Data Engineer Data Engineering 104

AWS Machine Learning Blog

SEPTEMBER 12, 2024

Organizations that want to build their own models or want granular control are choosing Amazon Web Services (AWS) because we are helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities such as petabyte-scale networking capability, hyperscale clustering, and the right tools to help you build.

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AWS AI AI Clustering 100

AWS Machine Learning Blog

MARCH 10, 2025

It was built using a combination of in-house and external cloud services on Microsoft Azure for large language models (LLMs), Pinecone for vectorized databases, and Amazon Elastic Compute Cloud (Amazon EC2) for embeddings. Opportunities for innovation CreditAI by Octus version 1.x

AWS 115

AWS Database AI AI 115

ODSC - Open Data Science

FEBRUARY 17, 2023

Data Science Fundamentals Going beyond knowing machine learning as a core skill, knowing programming and computer science basics will show that you have a solid foundation in the field. Computer science, math, statistics, programming, and software development are all skills required in NLP projects.

Data Science 111

Data Science Deep Learning Deep Learning Natural Language Processing 111

phData

FEBRUARY 21, 2025

In this post, we will be particularly interested in the impact that cloud computing left on the modern data warehouse. The Cloud represents an iteration beyond the on-prem data warehouse, where computing resources are delivered over the Internet and are managed by a third-party provider.

Data Warehouse 52

Data Warehouse Cloud Data ETL Cloud Computing 52

The MLOps Blog

MARCH 23, 2023

Even for basic inference on LLM, multiple accelerators or multi-node computing clusters like multiple Kubernetes pods are required. But the issue we found was that MP is efficient in single-node clusters, but in a multi-node setting, the inference isn’t efficient. For instance, a 1.5B 2 Calculate the size of the model.

Natural Language Processing 115

Natural Language Processing Cloud Computing AWS Deep Learning 115

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

NOVEMBER 28, 2024

Familiarity with cloud computing tools supports scalable model deployment. Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities. It’s often used in customer segmentation and anomaly detection.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

MAY 15, 2024

You can adopt these strategies as well as focus on continuous learning to upscale your knowledge and skill set. Leverage Cloud Platforms Cloud platforms like AWS, Azure, and GCP offer a suite of scalable and flexible services for data storage, processing, and model deployment.

Data Analyst 52

Data Analyst Data Scientist Data Science Machine Learning 52

Mlearning.ai

FEBRUARY 16, 2023

The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Therefore, the tool is referred to as cloud-agnostic. Multi-Cloud Options You can host Snowflake on numerous popular cloud platforms, including Microsoft Azure, Google Cloud, and Amazon Web Services.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Becoming Human

MAY 15, 2023

The two most common types of unsupervised learning are clustering , where the algorithm groups similar data points together, and dimensionality reduction , where the algorithm reduces the number of features in the data. Three of the most popular cloud platforms are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

NOVEMBER 4, 2024

Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers. Azure Microsoft Azure offers a range of services for Data Engineering, including Azure Data Lake for scalable storage and Azure Databricks for collaborative Data Analytics.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

JULY 11, 2023

Cloud data centers: These are data centers owned and operated by cloud providers, such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform, and provide a range of services on a pay-as-you-go basis. Not a cloud computer? Alternatives to using a data center: 1.

Data Lakes 40

Data Lakes AI AI Cloud Computing 40

The MLOps Blog

JUNE 27, 2023

Microsoft Azure ML Platform The Azure Machine Learning platform provides a collaborative workspace that supports various programming languages and frameworks. Kubeflow integrates with popular ML frameworks, supports versioning and collaboration, and simplifies the deployment and management of ML pipelines on Kubernetes clusters.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Pickl AI

JANUARY 29, 2025

Summary: The evolution of cloud computing has revolutionised data storage, accessibility, and IT infrastructure. From early computing models to modern cloud services, businesses now enjoy scalable, cost-efficient, and flexible solutions. The global cloud computing market, projected to grow from USD 626.4

Cloud Computing 52

Cloud Computing Clustering Azure AWS 52

ODSC - Open Data Science

SEPTEMBER 12, 2024

Commerce Department has announced a new proposal aimed at enhancing the safety and security of advanced AI technologies and cloud computing services. The proposed rule aims to establish clear and enforceable reporting requirements to better monitor and manage the risks associated with advanced AI models and computing clusters.

Cloud Computing 40

Cloud Computing AI AI Clustering 40