Dataconomy

MAY 2, 2023

Familiarity with machine learning frameworks, data structures, and algorithms is also essential. Additionally, expertise in big data technologies, database management systems, cloud computing platforms, problem-solving, critical thinking, and collaboration is necessary.

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ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

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phData

MAY 25, 2023

Snowflake is a cloud computing–based data cloud company that provides data warehousing services that are far more scalable and flexible than traditional data warehousing products. Here are some of our best practices for building data models in Power BI to optimize your Snowflake experience: 1.

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Pickl AI

NOVEMBER 28, 2024

A solid foundation in mathematics enhances model optimisation and performance. Familiarity with cloud computing tools supports scalable model deployment. Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data.

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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phData

JUNE 27, 2023

The modern data stack (MDS) has seen massive changes over the past few decades, fueled by technological advances and new platforms. As a result, we are presented with specialized data platforms, databases, and warehouses. All of which have a specific role used to collect, store, process, and analyze data. Proceed as you see fit.

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Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption.

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Data Warehouse Data Lakes Hadoop Big Data 59

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. What is Microsoft Azure?

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ODSC - Open Data Science

NOVEMBER 18, 2024

Dimensional Data Modeling in the Modern Era Dustin Dorsey |Principal Data Architect |Onix With the emergence of big data, cloud computing, and AI-driven analytics, many wonder if the traditional principles of dimensional modeling still hold value.

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IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Dataconomy

MARCH 28, 2023

Implementing robust security measures:  Implementing robust security measures, such as encryption, firewalls, and intrusion detection systems, can help protect sensitive data. Leveraging cloud computing:  Cloud computing can provide scalable and cost-effective data storage and processing solutions for IoT ecosystems.

Data Science 70

Data Science Internet of Things Analytics Analytics 70

Dataconomy

MARCH 28, 2023

Implementing robust security measures:  Implementing robust security measures, such as encryption, firewalls, and intrusion detection systems, can help protect sensitive data. Leveraging cloud computing:  Cloud computing can provide scalable and cost-effective data storage and processing solutions for IoT ecosystems.

Data Science 70

Data Science Internet of Things Analytics Analytics 70

Mlearning.ai

FEBRUARY 16, 2023

Therefore, you’ll be empowered to truncate and reprocess data if bugs are detected and provide an excellent raw data source for data scientists. Use Multiple Data Models With on-premise data warehouses, storing multiple copies of data can be too expensive.

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DrivenData Labs

MARCH 30, 2023

Denis Loginov is a Principal Security Engineer at Broad Institute with the Data Sciences Platform with expertise in application security and cloud computing. David Froelicher is a Postdoctoral Researcher at MIT and Broad Institute with expertise in distributed systems, applied cryptography, and genomic privacy.

Machine Learning 162

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Pickl AI

OCTOBER 3, 2024

Predictive Analytics : Models that forecast future events based on historical data. Model Repository and Access Users can browse a comprehensive library of pre-trained models tailored to specific business needs, making it easy to find the right solution for various applications.

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Pickl AI

JULY 8, 2024

Limited Schema Flexibility Some TSDBs prioritise optimised time-series data storage and retrieval and may offer less flexibility in schema design compared to relational databases. Data Modelling Challenges Effectively modelling complex time series data with various attributes and relationships can be challenging in some TSDBs.

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The MLOps Blog

JUNE 27, 2023

Model versioning, lineage, and packaging : Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream? The vendor offerings are divided into two classes: GPU Cloud Servers are long-running (but possibly pre-emptible) machines.

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Precisely

OCTOBER 16, 2023

Consequently, managers now oversee IT costs for their operations and engage directly in cloud computing contracts. This shift has influenced how cloud resources are designed and marketed, focusing on easy access, modularity, and straightforward deployment.

Analytics 72

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Dataconomy

MARCH 19, 2025

Hadoop as a Service (HaaS) offers a compelling solution for organizations looking to leverage big data analytics without the complexities of managing on-premises infrastructure. As businesses increasingly turn to cloud computing, HaaS emerges as a vital option, providing flexibility and scalability in data processing and storage.

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