Sparse and Variational Gaussian Process  What To Do When Data is Large

Big data is the cure for many machine learning problems. But one person’s cure can be another’s poison. Big data causes many Bayesian methods to be unpractically expensive. We need to do something or Bayesian methods are left behind the big data revolution.

In this article, I will explain why big data makes a very popular Bayesian machine learning method — Gaussian Process —unaffordably expensive. Then I will present Bayesian’s solution — the Sparse and Variational Gaussian Process model (SVGP model), that brings Gaussian Process back in the game.

Some notations

Medium supports Unicode in text. This allows me to write many math subscript notations such as X₁ and _Xₙ. _But I could not write down some other subscripts. For example:

So in the text, I will use an underscore “” to lead such subscripts, such as X* and X*1_.

If some math notations render as question marks on your phone, please try to read this article from a computer. This is a known issue with some Unicode rendering.

The Gaussian Process regression model

Suppose we have some training data _(X, Y). _Both X and Y are float vectors of length n. So n is the number of training data points. We want to find a regression function from X to Y. This is a typical regression task. We can use the Gaussian Process regression model (GPR) to find such a function.

#variational-inference #machine-leraning #inside-ai #bayesian-statistics #gaussian-process #ai

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Sparse and Variational Gaussian Process  What To Do When Data is Large
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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

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iOS App Dev


Making Sense of Unbounded Data & Real-Time Processing Systems

Unbounded data refers to continuous, never-ending data streams with no beginning or end. They are made available over time. Anyone who wishes to act upon them can do without downloading them first.

As Martin Kleppmann stated in his famous book, unbounded data will never “complete” in any meaningful way.

“In reality, a lot of data is unbounded because it arrives gradually over time: your users produced data yesterday and today, and they will continue to produce more data tomorrow. Unless you go out of business, this process never ends, and so the dataset is never “complete” in any meaningful way.”

— Martin Kleppmann, Designing Data-Intensive Applications

Processing unbounded data requires an entirely different approach than its counterpart, batch processing. This article summarises the value of unbounded data and how you can build systems to harness the power of real-time data.

#stream-processing #software-architecture #event-driven-architecture #data-processing #data-analysis #big-data-processing #real-time-processing #data-storage

Sid  Schuppe

Sid Schuppe


Benefits of Data Ingestion

In the last two decades, many businesses have had to change their models as business operations continue to complicate. The major challenge companies face today is that a large amount of data is generated from multiple data sources. So, data analytics have introduced filters to various data sources to detect this problem. They need analytics and business intelligence to access all their data sources to make better business decisions.

It is obvious that the company needs this data to make decisions based on predicted market trends, market forecasts, customer requirements, future needs, etc. But how do you get all your company data in one place to make a proper decision? Data ingestion consolidates your data and stores it in one place.

#big data #data access #data ingestion #data collection #batch processing #data access layer #data integration platform #automate data collection

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt