ODSC - Open Data Science

FEBRUARY 8, 2024

How can we build up toward our vision in terms of solvable data problems and specific data products? data sources or simpler data models) of the data products we want to build? A data product may aim to automate a decision, or it may aim to assist a human decision-maker. What are we working towards?

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ODSC - Open Data Science

OCTOBER 21, 2024

This resource focuses on fairness, transparency, and accountability within TensorFlow models. From model fairness to explainability, TensorFlow provides extensive tools and documentation to ensure AI systems are ethically sound and free from bias.

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Pickl AI

OCTOBER 17, 2023

This blog discusses best practices, real-world use cases, security and privacy considerations, and how Data Scientists can use ChatGPT to their full potential. Machine Learning Models: How Data Scientists Use ChatGPT Data Scientists use ChatGPT as a powerful ally in the ever-evolving field of Data Science.

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Snorkel AI

OCTOBER 18, 2023

Claims adjusters pour hours into reviewing claims documents, verifying information, coordinating with customers, and making decisions about payments. AI can expedite tasks like data entry , document review , trend forecasting, and fraud detection. Claims data is often noisy, unstructured, and multi-modal.

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Snorkel AI

OCTOBER 18, 2023

Claims adjusters pour hours into reviewing claims documents, verifying information, coordinating with customers, and making decisions about payments. AI can expedite tasks like data entry , document review , trend forecasting, and fraud detection. Claims data is often noisy, unstructured, and multi-modal.

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Pickl AI

JANUARY 30, 2023

Since the rise of Data Science, it has found several applications across different industrial domains. However, the programming languages that work at the core of Data Science play a significant role in it. Hence for an individual who wants to excel as a data scientist, learning Python is a must.

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ODSC - Open Data Science

OCTOBER 15, 2023

Institute of Analytics The Institute of Analytics is a non-profit organization that provides data science and analytics courses, workshops, certifications, research, and development. The courses and workshops cover a wide range of topics, from basic data science concepts to advanced machine learning techniques.

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Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

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Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Smart Data Collective

JANUARY 26, 2021

Big data architecture lays out the technical specifics of processing and analyzing larger amounts of data than traditional database systems can handle. According to the Microsoft documentation page, big data usually helps business intelligence with many objectives. How to Find a Quality Translation Company.

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Pickl AI

AUGUST 9, 2024

Hierarchies align data modelling with business processes, making it easier to analyse data in a context that reflects real-world operations. Designing Hierarchies Designing effective hierarchies requires careful consideration of the business requirements and the data model.

Data Warehouse 52

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Mlearning.ai

FEBRUARY 13, 2023

Document Databases Document databases organize data in the form of documents instead of rows and columns. These databases are intended to accommodate unstructured data like texts, images, and videos. with each document representing a file and each folder symbolizing a group of files. Document DBs 3.

Database 52

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DagsHub

AUGUST 29, 2024

Version control systems (VCS) play a key role in this area by offering a structured method to track changes made to models and handle versions of data and code used in these ML projects. With weak version control, teams could face problems like inconsistent data, model drift , and clashes in their code.

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Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. This graph is an example of one analysis, documented in our internal catalog.

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Pickl AI

NOVEMBER 28, 2024

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. Model evaluation and tuning involve several techniques to assess and optimise model accuracy and reliability.

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The MLOps Blog

JUNE 27, 2023

User support arrangements Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Qwak Qwak is a fully-managed, accessible, and reliable ML platform to develop and deploy models and monitor the entire machine learning pipeline.

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Pickl AI

MAY 7, 2024

This newfound proficiency not only empowers them to become true data storytellers but also elevates their value within their organizations, placing them at the forefront of data-driven success. Here it is important to mention that Tableau for Data Science is eaully significant. This course prepares you for the future.

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phData

SEPTEMBER 26, 2023

A common problem solved by phData is the migration from an existing data platform to the Snowflake Data Cloud , in the best possible manner. Data flows from the current data platform to the destination. Another point to consider is the end data model and if it will differ from the current structure.

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Dataconomy

MAY 2, 2023

Machine learning engineers are professionals who possess a blend of skills in software engineering and data science. Their primary role is to leverage their programming and coding abilities to gather, process, and analyze large volumes of data. Does a machine learning engineer do coding? percentage points.

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DataRobot

JUNE 23, 2021

As they strive to improve models, data scientists continually try new approaches to refine their predictions. To help data scientists experiment faster, DataRobot has added Composable ML to automated machine learning. Step 1: Use Automation to Perform the Initial Feature Engineering and Modeling. So let’s dig in!

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DagsHub

DECEMBER 11, 2023

The following points illustrates some of the main reasons why data versioning is crucial to the success of any data science and machine learning project: Storage space One of the reasons of versioning data is to be able to keep track of multiple versions of the same data which obviously need to be stored as well.

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phData

OCTOBER 17, 2024

dbt offers a SQL-first transformation workflow that lets teams build data transformation pipelines while following software engineering best practices like CI/CD, modularity, and documentation. Snowflake has integrated the Streamlit library to create apps from your data without your data ever leaving Snowflake’s security.

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ODSC - Open Data Science

APRIL 26, 2023

As you can imagine, data science is a pretty loose term or big tent idea overall. Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. What makes this job title unique is the “Swiss army knife” approach to data.

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Data Analyst Machine Learning Machine Learning Power BI 52

phData

JULY 17, 2023

We live in a world where vast amounts of data are being collected, and unprecedented compute power is available to extract value. The advancement of technology in large language models (LLMs), machine learning (ML), and data science can truly transform industries through insights and predictions.

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IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance toolkit lets you direct, manage and monitor AI activities without the expense of switching your data science platform, even for models developed using third-party tools.

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Hacker News

SEPTEMBER 26, 2024

It's common to see documentation for dev tools structured like a computer program. It starts with defines a core data model and the relations and the atoms. that defaults to some base image with “most” data science libraries pre-installed, but that they user can override. that always has to be provided.

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Iguazio

JANUARY 22, 2024

In addition, MLOps practices like building data, experting tracking, versioning, artifacts and others, also need to be part of the GenAI productization process. For example, when indexing a new version of a document, it’s important to take care of versioning in the ML pipeline. This also makes indexing more efficient.

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Flipboard

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Text, images, audio, and videos are common examples of unstructured data. Amazon Textract – You can use this ML service to extract metadata from scanned documents and images.

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Pickl AI

AUGUST 27, 2024

Python’s flexibility extends to its ability to handle a wide range of tasks, from quick scripting to complex data modelling. This versatility makes Python perfect for developers who want to script applications, websites, or perform data-intensive tasks.

Data Analysis 52

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The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Data science practitioners experiment with algorithms, data, and hyperparameters to develop a model that generates business insights.

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DataRobot

DECEMBER 16, 2021

and train models with a single click of a button. Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Explanations around data, models , and blueprints are extensive throughout the platform so you’ll always understand your results.

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DagsHub

DECEMBER 14, 2023

Why Version Control is Essential in ML Version control is an indispensable practice in machine learning (ML) for several crucial reasons: Reproducibility: ML projects are often iterative and involve numerous experiments with different data, models, and hyperparameters.

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Pickl AI

JUNE 7, 2024

This blog will delve into ETL Tools, exploring the top contenders and their roles in modern data integration. Let’s unlock the power of ETL Tools for seamless data handling. Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse.

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ODSC - Open Data Science

FEBRUARY 14, 2023

The database would need to be highly available and resilient, with features like automatic failover and data replication to ensure that the system remains up and running even in the face of hardware or software failures. This could be achieved through the use of a NoSQL data model, such as document or key-value stores.

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Pickl AI

AUGUST 11, 2024

Consider factors such as data volume, query patterns, and hardware constraints. Document and Communicate Maintain thorough documentation of fact table designs, including definitions, calculations, and relationships. Use indexing and partitioning strategies to improve query performance.

Data Quality 52

Data Quality Data Warehouse Data Governance Business Intelligence 52

AWS Machine Learning Blog

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks. Also, you can update the model’s deploy status.

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The MLOps Blog

OCTOBER 3, 2023

So I was able to get from growth hacking to data analytics, then data analytics to data science, and then data science to MLOps. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. How do I get this model in production?

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