ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. This will lead to algorithm development for any machine or deep learning processes.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

Pickl AI

APRIL 21, 2025

Summary: Big Data refers to the vast volumes of structured and unstructured data generated at high speed, requiring specialized tools for storage and processing. Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

Women in Big Data

NOVEMBER 27, 2024

Snowflake Snowflake is a cloud-based data warehousing platform that offers a highly scalable and efficient architecture designed for performance and ease of use. It features Synapse Studio, a collaborative workspace for data integration, exploration, and analysis, allowing users to manage data pipelines seamlessly.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Towards AI

APRIL 4, 2023

With their groundbreaking web-based Studio platform, engineers have been able to collect data, develop and tune ML models, and deploy them to devices. This has empowered teams to quickly create and optimize models and algorithms that run at peak performance on any edge device. The Edge Impulse SDK is designed to be one of them.

ML 98

ML ML Python Machine Learning 98

The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

DECEMBER 5, 2023

Some of our most popular in-person sessions were: MLOps: Monitoring and Managing Drift: Oliver Zeigermann | Machine Learning Architect ODSC Keynote: Human-Centered AI: Peter Norvig, PhD | Engineering Director, Education Fellow | Google, Stanford Institute for Human-Centered Artificial Intelligence (HAI) The Cost of AI Compute and Why AI Clouds Will (..)

Data Science 98

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 98

Data Science Dojo

JULY 3, 2024

Data Visualization : Techniques and tools to create visual representations of data to communicate insights effectively. Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

Data Analyst 52

Data Analyst Data Scientist Data Science Machine Learning 52

AWS Machine Learning Blog

DECEMBER 20, 2023

This unified schema streamlines downstream consumption and analytics because the data follows a standardized schema and new sources can be added with minimal data pipeline changes. After the security log data is stored in Amazon Security Lake, the question becomes how to analyze it.

AWS 131

AWS ML ML Algorithm 131

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Read more to know.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

AUGUST 12, 2024

Primary activities AIOps relies on big data-driven analytics , ML algorithms and other AI-driven techniques to continuously track and analyze ITOps data. The process includes activities such as anomaly detection, event correlation, predictive analytics, automated root cause analysis and natural language processing (NLP).

Big Data 106

Big Data Big Data ML ML 106

DagsHub

APRIL 21, 2024

MLOps helps these organizations to continuously monitor the systems for accuracy and fairness, with automated processes for model retraining and deployment as new data becomes available. You can consider this stage as the most code-intensive stage of the entire ML pipeline. It is designed to leverage hardware acceleration (e.g.,

Machine Learning 59

Machine Learning Machine Learning ML ML 59

phData

OCTOBER 11, 2024

To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud , Fivetran, and Azure to automate invoice processing. Implementation of metadata-driven data pipelines for governance and reporting. This is where AI truly shines.

AI 52

AI AI Data Pipeline ML 52

phData

JULY 5, 2024

Pinecone and Weaviate are popular managed vector database platforms that can efficiently scale to handle billions of documents and return relevant embeddings using an approximate nearest neighbor (ANN) algorithm. Chroma is a popular open-source vector database with an ANN algorithm; however, it currently does not support hybrid search.

AI 52

AI AI Database AWS 52

The MLOps Blog

MARCH 20, 2023

Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition.

ML 59

ML ML Machine Learning Machine Learning 59

ODSC - Open Data Science

JANUARY 7, 2025

Just as a writer needs to know core skills like sentence structure and grammar, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and soon. Data Engineering Data engineering remains integral to many data science roles, with workflow pipelines being a key focus.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

AWS Machine Learning Blog

SEPTEMBER 11, 2024

This is accomplished by breaking the problem into independent parts so that each processing element can complete its part of the workload algorithm simultaneously. Parallelism is suited for workloads that are repetitive, fixed tasks, involving little conditional branching and often large amounts of data.

AWS 102

AWS ML ML Clustering 102

Precisely

MAY 23, 2024

Whatever your approach may be, enterprise data integration has taken on strategic importance. Artificial intelligence (AI) algorithms are trained to detect anomalies. Today’s enterprises need real-time or near-real-time performance, depending on the specific application. Timing matters.

Data Governance 64

Data Governance ETL Data Pipeline Azure 64

Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

Mlearning.ai

JUNE 16, 2023

There is a VSCode Extension that enables its integration into traditional development pipelines. How to use the Codex models to work with code - Azure OpenAI Service Codex is the model powering Github Copilot. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI 52

AI AI Data Analysis Data Analysis 52

The MLOps Blog

FEBRUARY 27, 2025

Adhering to data protection laws is not as complex if we focus less on the internal structure of the algorithms and more on the practical contexts of use. To keep data secure throughout the models lifecycle, implement these practices: data anonymization, secure model serving and privacy penetration tests.

Machine Learning 64

Machine Learning Machine Learning AI AI 64

phData

JANUARY 31, 2024

Python has long been the favorite programming language of data scientists. Historically, Python was only supported via a connector, so making predictions on our energy data using an algorithm created in Python would require moving data out of our Snowflake environment.

Machine Learning 52

Machine Learning Machine Learning Python Data Scientist 52

Mlearning.ai

FEBRUARY 16, 2023

The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. ML models, in turn, require significant volumes of adequate data to ensure accuracy. What does Snowflake do?

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Precisely

MAY 23, 2024

Whatever your approach may be, enterprise data integration has taken on strategic importance. Artificial intelligence (AI) algorithms are trained to detect anomalies. Today’s enterprises need real-time or near-real-time performance, depending on the specific application. Timing matters.

Data Governance 52

Data Governance ETL Data Pipeline Azure 52

Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

Data Governance 59

Data Governance ML ML Cloud Data 59

Heartbeat

SEPTEMBER 18, 2023

Whether you rely on cloud-based services like Amazon SageMaker , Google Cloud AI Platform, or Azure Machine Learning or have developed your custom ML infrastructure, Comet integrates with your chosen solution. It goes beyond compatibility with open-source solutions and extends its support to managed services and in-house ML platforms.

ML 52

ML ML Data Scientist Machine Learning 52

DagsHub

AUGUST 29, 2024

The Complexity of ML Projects ML models usually have many iterations, each one with its unique data sets, preprocessing steps, hyperparameters, and algorithm adjustments. Regular backups can be done using the automated metrics that periodically copy the data and model files to remote storage such as cloud storage (eg.

ML 52

ML ML Python Machine Learning 52

Data Science Dojo

MARCH 9, 2023

Data Science Dojo is offering Memphis broker for FREE on Azure Marketplace preconfigured with Memphis, a platform that provides a P2P architecture, scalability, storage tiering, fault-tolerance, and security to provide real-time processing for modern applications suitable for large volumes of data. Try Memphis Now !

Apache Kafka 323

Apache Kafka Azure Data Science Data Pipeline 323

The MLOps Blog

MARCH 21, 2023

With language models and NLP , you’d likely need your data component to also cater for unstructured text and speech data and extract real-time insights and summaries from them. The most important requirement you need to incorporate into your platform for this vertical is the regulation of data and algorithms.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

phData

OCTOBER 28, 2024

However, if the tool supposes an option where we can write our custom programming code to implement features that cannot be achieved using the drag-and-drop components, it broadens the horizon of what we can do with our data pipelines. The default value is 360 seconds.

Python 52

Python ETL AWS Database 52

phData

SEPTEMBER 27, 2024

Let’s break down why this is so powerful for us marketers: Data Preservation : By keeping a copy of your raw customer data, you preserve the original context and granularity. Both persistent staging and data lakes involve storing large amounts of raw data. Your customer data game will never be the same.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

Flipboard

JUNE 16, 2025

SQL and MongoDB SQL remains critical for structured data management, while MongoDB caters to NoSQL database needs, which is essential for modern and flexible data applications. Data Structures and Algorithms (DSA): Why: Fundamental for clearing coding interviews across software development roles.

Python 50

Python AWS Machine Learning Machine Learning 50