IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

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AWS Machine Learning Blog

MAY 15, 2023

Solution overview Six people from Getir’s data science team and infrastructure team worked together on this project. Deep/neural network algorithms also perform very well on sparse data set and in cold-start (new item introduction) scenarios. The following diagram shows the solution’s architecture.

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ODSC - Open Data Science

JUNE 7, 2023

Data lakes are able to handle a diverse range of data types. From images, videos, text, and even sensor data. Then, there’s data integration. A data lake can also act as a central hub for integrating data from various sources and systems within an organization.

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AWS Machine Learning Blog

OCTOBER 2, 2024

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. Tanvi Singhal is a Data Scientist within AWS Professional Services.

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ODSC - Open Data Science

FEBRUARY 21, 2024

These files contain metadata, current state details, and other information useful in planning and applying changes to infrastructure. It helps to observe data science principles in working with these files. They can provide encryption for passwords and other secrets to ensure state data integrity and confidentiality.

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Pickl AI

JULY 19, 2024

They enhance data integrity, security, and accessibility while providing tools for efficient data management and retrieval. A Database Management System (DBMS) is specialised software designed to efficiently manage and organise data within a computer system. Indices are data structures optimised for rapid data retrieval.

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Pickl AI

NOVEMBER 15, 2023

From financial services to e-commerce and telecommunications, organizations leverage Data Warehouses to unlock the full potential of their structured data for strategic advantage. What Is Data Lake Architecture? Delta Lake vs. Data Lake Delta Lake is an open-source storage layer that brings ACID transactions to Data Lakes.

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Pickl AI

AUGUST 31, 2023

Types of Data Profiling: Data profiling can be broadly categorized into three main types, each focusing on different aspects of the data: Structural Profiling: Structural profiling involves analyzing the structure and metadata of the data. It supports metadata analysis, data lineage, and data quality assessment.

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Pickl AI

OCTOBER 11, 2023

This process involves real-time monitoring and documentation to provide visibility on the data quality, thereby helping the organization detect and address data-related issues. It is backed by sophisticated algorithms that empower the identification of budding data irregularities Simplify the amalgamation of data from diverse origins.

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Pickl AI

OCTOBER 11, 2024

These include the database engine for executing queries, the query processor for interpreting SQL commands, the storage manager for handling physical data storage, and the transaction manager for ensuring data integrity through ACID properties. Data Independence: Changes in database structure do not affect application programs.

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Pickl AI

JUNE 28, 2023

With the use of these tools, one can streamline the data modelling process. Moreover, these tools are designed to automate tasks like generating SQL scripts, documenting metadata and others. Improved Visualization Data modelling tools offer intuitive graphical representations of data models.

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Pickl AI

JANUARY 22, 2025

Introduction In today’s data-driven world, organizations generate approximately 2.5 quintillion bytes of data daily, highlighting the critical need for efficient data management. Database Management Systems (DBMS) serve as the backbone of data handling.

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Pickl AI

OCTOBER 21, 2024

The primary purpose of a DBMS is to provide a systematic way to manage large amounts of data, ensuring that it is organised, accessible, and secure. By employing a DBMS, organisations can maintain data integrity, reduce redundancy, and streamline data operations, enabling more informed decision-making.

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Pickl AI

JULY 23, 2023

It requires sophisticated tools and algorithms to derive meaningful patterns and trends from the sheer magnitude of data. Meta Data Metadata, often dubbed “data about data,” provides essential context and descriptions for other datasets. To know more about Pickl.AI

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Pickl AI

OCTOBER 10, 2023

Data Transparency Data Transparency is the pillar that ensures data is accessible and understandable to all stakeholders within an organization. This involves creating data dictionaries, documentation, and metadata. It provides clear insights into the data’s structure, meaning, and usage.

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Pickl AI

MAY 15, 2023

Online Processing: this type of data processing involves managing transactional data in real time and focuses on handling individual transaction. The systems are designed to ensure data integrity, concurrency and quick response times for enabling interactive user transactions. The Data Science courses provided by Pickl.AI

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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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Pickl AI

OCTOBER 19, 2023

This includes removing duplicates, correcting typos, and standardizing data formats. It forms the bedrock of data quality improvement. Implement Data Validation Rules To maintain data integrity, establish strict validation rules. This ensures that the data entered meets predefined criteria.

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Artificial Corner

JUNE 6, 2023

The benefits of Databricks over Spark is Highly reliable and performant data pipelines and Productive data science at scale — source: [link] Databricks also introduced Delta Lake, an open-source storage layer that brings reliability to data lakes. MapReduce: simplified data processing on large clusters.

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Flipboard

DECEMBER 3, 2024

As a global leader in agriculture, Syngenta has led the charge in using data science and machine learning (ML) to elevate customer experiences with an unwavering commitment to innovation. It facilitates real-time data synchronization and updates by using GraphQL APIs, providing seamless and responsive user experiences.

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. By analyzing millions of metadata elements and data flows, Iris could make intelligent suggestions to users, democratizing data integration and allowing even those without a deep technical background to create complex workflows.

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Pickl AI

JUNE 11, 2024

These are subject-specific subsets of the data warehouse, catering to the specific needs of departments like marketing or sales. They offer a focused selection of data, allowing for faster analysis tailored to departmental goals. Metadata This acts like the data dictionary, providing crucial information about the data itself.

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