Mon.Oct 18, 2021

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Humans and AI: Bargaining Power

DataRobot

I have a confession to make—I’m a back-seat driver! When sitting in a taxi, I can’t help but grumble when the ride isn’t smooth, or the driver chooses the slowest lane of traffic. I have to fight the urge to take control. When it comes to shopping, I passively accept what is offered for sale. But my wife, who grew up in Asia where haggling is part of the culture, is different.

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A Guide to Machine Learning Pipelines and Orchest

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Learn how machine learning pipelines are used in productions and design your first pipeline using simple steps on disaster tweets classification datasets. You will also learn how to ingest the data, preprocess, train, and eventually evaluate the results. Image 1 Introduction In this […].

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5 hot new IT jobs — and why they just might stick

DataKitchen

The post 5 hot new IT jobs — and why they just might stick first appeared on DataKitchen.

IT 356
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Text Analysis in Natural Language Processing using Julia

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview of Text Analysis in Julia The article majorly focuses on how to make you comfortable with the outline of Julia text processing tools with a brief explanation of their use in projects. Here we see the TextAnalysis package of Julia with the functionalities […]. The post Text Analysis in Natural Language Processing using Julia appeared first on Analytics Vidhya.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Our 2021 Data Impact Awards Finalists

Cloudera

It’s that time of year again… Award season! We are thrilled to announce the finalists of the 2021 Data Impact Awards. This year’s entrants have excelled at demonstrating how innovative data solutions can help solve real-time challenges and positively impact people around the world. . The entries are some of the most remarkable we’ve seen, giving our judges the tough task of selecting an award worthy shortlist.

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What Is Mature MLOps? A Perspective

Dataiku

On a recent webinar with Jim Kobelius of TDWI (that you can watch below), we talked about mature MLOps. You can watch our presentations, but I thought it would be helpful to summarize some of the concepts based on over ten years working with machine learning and, over the last three and a half years, specifically focusing on MLOps.

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What Is Active Metadata Management and How Does It Work?

Octopai

First, what active metadata management isn’t : “Okay, you metadata! Quit lounging around! We’re gonna move those bits and bytes! Everyone, get into position and… one – and zero! And one – and zero!”. Now, what active metadata management is (well, kind of): “Okay, you metadata! Quit lounging around! You’re going to start pulling your weight here and deliver actionable insights that we can use to improve our business immediately!”.

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From Back-Office to Competitive Advantage - Transforming Risk in Banking

Teradata

Risk management in banks is undergoing a rapid transformation, accelerated by COVID, but with causes and potential impacts that go deeper. Find out more.

Risk 52
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Sankey Diagrams, Parallel Sets & Alluvial Diagrams… What’s the Difference?

The Data Visualisation Catalogue

For ages, the naming between Sankey Diagrams , Parallel Sets , and Alluvial Diagrams have been used interchangeably. Are these visualisations that different from one another and is it a bad thing that some misnaming is taking place? From my research into the various visualisation types, it’s common to see a particular visualisation type being identified under multiple names.

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Using MATLAB for Data Science and Machine Learning

Domino Data Lab

The opportunities to solve problems with the use of data are greater than ever, and as different industries embrace them, the available data has been steadily increasing and the number of tools expanded. A typical question that new data scientists ask is related to the best programming language to learn, either to get a good understanding of coding or to future-proof their skills.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Demand Planning and Forecasting: The Key to Supply Chain Challenges

Jet Global

For years, business leaders have struggled with the challenges of getting the right product to the right place at the right time. When a company ends up with too much inventory on hand, its capital is tied up in goods stored in the warehouse, so it can’t be put to use elsewhere. Instead of paying down debt, saving on interest expense, and preserving liquidity; its cash is committed to maintaining bloated levels of inventory.