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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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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AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. Matúš Chládek is a Senior Engineering Manager for ML Ops at Zeta Global.

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ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication.

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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

JULY 17, 2023

In order to fully leverage this vast quantity of collected data, companies need a robust and scalable data infrastructure to manage it. This is where Fivetran and the Modern Data Stack come in. Data modeling, data cleanup, etc. Fivetran is a critical member of the Modern Data Stack. What is Fivetran?

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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?

Data Science 52

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ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

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phData

JULY 22, 2024

I’ve moved from building user interfaces and backend systems to designing data models, creating data pipelines, and gaining valuable insights from complex datasets. This journey has reshaped my career from a software generalist to a data specialist. Outside of work, what's your life like?

Data Engineering 52

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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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Iguazio

JANUARY 22, 2024

Production App - Build resilient and modular production pipelines with automation, scale, testing, observability, versioning, security, risk handling, etc. Monitoring - Monitor all resources, data, model and application metrics to ensure performance. This helps cleanse the data.

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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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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. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

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DagsHub

SEPTEMBER 10, 2023

DagsHub is a centralized platform to host and manage machine learning projects, including code, data, models, experiments, annotations, model registry, and more! Intermediate Data Pipeline : Build data pipelines using DVC for automation and versioning of Open Source Machine Learning projects.

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DataRobot Blog

SEPTEMBER 13, 2022

At this level, the data science team will be small or nonexistent. Businesses will then require more information-literate staff, but they’ll need to contend with an ongoing shortage of data scientists. By now, data scientists have witnessed success optimizing internal operations and external offerings through AI.

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Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

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The MLOps Blog

DECEMBER 28, 2022

Union of business and data teams The success of ML projects lies in the strong collaboration between the data team and the business team. Such continuous alliance of the business team helps the data science team to create ML models that have the potential to add significant business value.

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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. What does a modern data architecture do for your business?

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phData

OCTOBER 17, 2024

By leveraging version control, testing, and documentation features, dbt Core enables teams to ensure data quality and consistency across their pipelines while integrating seamlessly with modern data warehouses. Aside from migrations, Data Source is also great for data quality checks and can generate data pipelines.

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Iguazio

DECEMBER 18, 2023

For data science practitioners, productization is key, just like any other AI or ML technology. This will require investing resources in the entire AI and ML lifecycle, including building the data pipeline, scaling, automation, integrations, addressing risk and data privacy, and more.

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

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Iguazio

DECEMBER 18, 2023

For data science practitioners, productization is key, just like any other AI or ML technology. This will require investing resources in the entire AI and ML lifecycle, including building the data pipeline, scaling, automation, integrations, addressing risk and data privacy, and more.

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Pickl AI

JULY 8, 2024

Security is Paramount Implement robust security measures to protect sensitive time series data. Integration with Data Pipelines and Analytics TSDBs often work in tandem with other data tools to create a comprehensive data ecosystem for analysis and insights generation.

Database 52

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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Therefore, you’ll be empowered to truncate and reprocess data if bugs are detected and provide an excellent raw data source for data scientists.

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DagsHub

DECEMBER 5, 2023

With collaborative coding tools, DagsHub provides a central location for data science teams to visualize, compare, and review their experiments, eliminating the need to set up any infrastructure. DagsHub detects and supports DVC's metrics and params file formats, and it also sets up a DVC remote where we can version our data.

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Alation

FEBRUARY 17, 2023

What frameworks and operating models have you seen work well? The firms that get data governance and management “right” bring people together and leverage a set of capabilities: (1) Agile; (2) Six sigma; (3) data science; and (4) project management tools. Establishing a solid vision and mission is key.

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phData

JULY 18, 2023

Advanced Analytics: Snowflake’s platform is purposefully engineered to cater to the demands of machine learning and AI-driven data science applications in a cost-effective manner. No Hardware Provisioning: No hardware to provision, just a t-shirt-sized warehouse available as needed within seconds.

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DagsHub

AUGUST 29, 2024

Data can change a lot, models may also quickly evolve and dependencies become old-fashioned which makes it hard to maintain consistency or reproducibility. With weak version control, teams could face problems like inconsistent data, model drift , and clashes in their code.

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The MLOps Blog

MAY 9, 2023

A typical machine learning pipeline with various stages highlighted | Source: Author Common types of machine learning pipelines In line with the stages of the ML workflow (data, model, and production), an ML pipeline comprises three different pipelines that solve different workflow stages. Happy pipelining!

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Data Science Dojo

APRIL 3, 2025

You must ensure continuous governance and security of your AI models and systems to prevent bias, data leaks, or any unauthorized AI interactions. The partnership between Databricks and Gencore AI enables enterprises to develop AI applications with robust security measures, optimized data pipelines, and comprehensive governance.

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Towards AI

JANUARY 22, 2025

Simply put, focusing solely on data analysis, coding or modeling will no longer cuts it for most corporate jobs. In this regard, I believe the future of data science belongs to those: who can connect the dots and deliver results across the entire data lifecycle.

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