Thu.Mar 11, 2021

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Choosing the right Machine Learning Framework

Domino Data Lab

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. Using these tools, businesses can scale their machine learning efforts while maintaining an efficient ML lifecycle.

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Step by Step process of Feature Engineering for Machine Learning Algorithms in Data Science

Analytics Vidhya

ArticleVideo Book Introduction Data Science is not a field where theoretical understanding helps you to start a carrier. It totally depends on the projects. The post Step by Step process of Feature Engineering for Machine Learning Algorithms in Data Science appeared first on Analytics Vidhya.

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Gartner – Top Trends in Data and Analytics for 2021: XOps

DataKitchen

Gartner identified XOps (DataOps, ModelOps, DevOps) as one of the top trends in data and analytics for 2021. Below we provide additional suggestions for further reading based on Gartner’s recommendations. What is XOps? . Gartner: “The multiplication of Ops disciplines stemming out of DevOps best practices has caused significant confusion in the marketplace.

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A Hands-On Introduction to Hugging Face’s AutoNLP 101

Analytics Vidhya

ArticleVideo Book Hugging Face, founded in 2016, has revolutionized the way people approach Natural Language Processing in this day and age. Based in New. The post A Hands-On Introduction to Hugging Face’s AutoNLP 101 appeared first on Analytics Vidhya.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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A Guide to Building Better Data Products

Juice Analytics

At Juice, we’ve helped our clients launch dozens of data products that generate new revenue streams, differentiate their solutions in the market and build stronger customer relationships. Along the way, we’ve learned a lot about what works and doesn’t. In this series I’ll take you through what you need to know to design, build, launch, sell and support a data product.

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Enterprise Data Operating Systems in the Cloud: Necessary, But Not Sufficient

Teradata

Getting your Cloud data architecture right starts with understanding which data products you need, the roles they perform, & the functional & non-functional characteristics that those roles demand.

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Data Virtualization: The Key to a Successful Data Lakes

Data Virtualization

If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lakes projects will actually fail. Obviously, you want to be in that 20% that succeed. The post Data Virtualization: The Key to a Successful Data Lakes appeared first on Data Virtualization blog.

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What’s New in the Dataiku Academy Since Launch?

Dataiku

Last May, we launched the Dataiku Academy , our free self-service learning and certification platform. Since then, we’ve been keeping busy trying to create and continuously improve on this interactive learning experience with video-rich content, self-paced courses, learning paths and certifications, to guide and support all of our users in their journey of learning and mastering Dataiku DSS.

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Data Virtualization: The Key to a Successful Data Lake

Data Virtualization

If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lake projects will actually fail. Obviously, you want to be in that 20% that succeed. The post Data Virtualization: The Key to a Successful Data Lake appeared first on Data Virtualization blog.

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How to Get Your Cloud Analytic Architecture Right

Teradata

Getting your Cloud data architecture right starts with understanding which data products you need, the roles they perform, & the functional & non-functional characteristics that those roles demand.

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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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Road to Automation – Partnering with Enterprise AI Companies

bridgei2i

Road to Automation – Partnering with Enterprise AI Companies. The commercial model of an enterprise AI company is simple – help technologically handicapped businesses make the most of AI technology. For centuries, enterprises have depended on new technology to stay a few steps ahead of their competitors. The need to invest in AI technology is just as severe as the need to invest in the Internet was back in the late 1990s.

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How Bliss Point Media Broke Free from Time-Consuming and Error-Prone NetSuite Reporting

Jet Global

Webinar Date: April 14, 2021 at 1 PM Local Time. Americas. Register Now. (EST). Europe, Middle East, Africa. Register Now. (GMT). Asia Pacific. Register Now. (AEDT). A finance team’s busiest time is during the month-end reporting process, due to a lack of control over the data produced from complicated consolidations, drawn-out entry validations, and the never-ending task of exporting and reformatting reports into spreadsheets to make them useful.

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How Can Small Businesses Benefit from an AI Data Company?

bridgei2i

How Can Small Businesses Benefit from an AI Data Company? We all know how Artificial Intelligence helps giants like Google and Amazon reshape their commercial techniques and business strategies. From eliminating the need for human assistance in repetitive tasks to reducing the risk of human errors in manual processes – AI can do a lot for large-scale businesses.

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Data Virtualization: The Key to a Successful Data Lake

Data Virtualization

If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lake projects will actually fail. Obviously, you want to be in that 20% that succeed.

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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How to Use Artificial Intelligence to Improve Customer Experiences?

bridgei2i

How to Use Artificial Intelligence to Improve Customer Experiences? The terms artificial intelligence and customer experience have been thrown around a lot in the last decade. The real question that needs answering is whether AI tools can provide businesses with real-time customer understanding. A top-quality AI-powered Customer Analytics system can help businesses recognize their customers’ present and future requirements.

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Top Three Requirements for Data Flows

Cloudera

Data flows are an integral part of every modern enterprise. No matter whether they move data from one operational system to another to power a business process or fuel central data warehouses with the latest data for near-real-time reporting, life without them would be full of manual, tedious and error-prone data modification and copying tasks. At Cloudera, we’re helping our customers implement data flows on-premises and in the public cloud using Apache NiFi , a core component of Cloudera DataFl

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What are data silos and why are they a problem?

Jedox

In this blog post, we’ll look at data silos, how they emerge and the problems they can cause within an organization. We’ll also discuss some approaches to resolving the silos, and most importantly, why it’s vital to success in the long run. A data silo typically consists of stored data that is not available to the entire organization, but only to some parts of it, such as departments, teams, or even individual employees, and is thus siloed within the organization.

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Data Saves Lives and Evolves Businesses: COVID 2020 Reflections

Sisense

Blog. As the COVID-19 pandemic drags on, we continue to find examples of organizations that use analytics to manage uncertainty. Data played an outsized role in the necessary changes we’ve seen in 2020 and will have an even greater impact in 2021 and beyond. A 2020 survey showed that almost half of businesses are now using analytics more often than they had prior to the pandemic.

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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.