Wed.Jul 28, 2021

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DeepDive into the Emerging concpet of Machine Learning Operations or MLOPs

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon ML + DevOps + Data Engineer = MLOPs Origins MLOps originated. The post DeepDive into the Emerging concpet of Machine Learning Operations or MLOPs appeared first on Analytics Vidhya.

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Using Event Data in Manufacturing to Improve Business Processes

David Menninger's Analyst Perspectives

Event data can be used to enhance existing processes, but it can also be used to dramatically impact operations, revenue models and the bottom line for manufacturers. Our Benchmark Research shows 95% of manufacturers consider it important to speed the flow of information and improve responsiveness within business processes. In this perspective I’ll share how manufacturers are working with event data to transform their organizations.

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Using Seaborn’s FacetGrid Based Methods for Exploratory Data Analysis

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Exploratory Data Analysis or EDA is a vital step in. The post Using Seaborn’s FacetGrid Based Methods for Exploratory Data Analysis appeared first on Analytics Vidhya.

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DataKitchen Wins Data & Analytics Vendor of the Year Award – OnConferences

DataKitchen

The post DataKitchen Wins Data & Analytics Vendor of the Year Award – OnConferences first appeared on DataKitchen.

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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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A Simple Guide to Hypothesis Testing for Dummies!

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Statistics is the science of analyzing huge amounts of data. The post A Simple Guide to Hypothesis Testing for Dummies! appeared first on Analytics Vidhya.

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4 Ways Data-Driven Automation Enhances Merchandise Distribution

Smart Data Collective

The supply-chain analytics market is projected to be worth over $16.8 billion by 2027. This is largely due to the benefits of using data analytics to improve automation in merchandise distribution. As a retailer or manufacturer selling via e-commerce platforms, you already know the importance of using big data to improve automation. While some jobs must be performed by actual humans, many can be performed just as well through algorithms, machines, and other technologies.

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3 Key Components of the Interdisciplinary Field of Data Science

Domino Data Lab

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication.

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Advanced OpenCV and NumPy Operations: Cropping, Copying, And Pasting

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Computer Vision is a real-world application of Machine Learning, that. The post Advanced OpenCV and NumPy Operations: Cropping, Copying, And Pasting appeared first on Analytics Vidhya.

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A Spotlight on Different Ways of Working at Dataiku

Dataiku

Are you more of a remote worker who likes to focus at home or someone who thrives in the office chatting with your colleagues? Maybe you are both, exclusively one, or maybe it depends on the day! There is no right or wrong answer to this, just personal preferences that you and your employer need to be aware of. Right?

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Business Intelligence vs Data Science vs Data Analytics

FineReport

Data is knowledge, new oil, powerful weapon. Data is highly valued nowadays. Good data can give you keen insights, convincing evidence to make informed decisions. By observing and analyzing data, we can develop more accurate theories and formulate more effective solutions. For this reason, data science and/vs. business intelligence has become two buzzwords that represent some new trends in the scientific and business area. .

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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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Data Science Governance – Don’t Reinvent The Wheel

Alation

As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. While governance has been a major focus for many years when it comes to managing data, governance focused on data science processes is still far less mature. That needs to change. This blog will discuss a couple of distinct areas of governance that organizations should consider.

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An In-Depth View of Data Science

Domino Data Lab

Data science is a field at the convergence of statistics, computer science and business. It is highly valued by organizations as they strive to remain competitive, increase revenues and delight customers because data scientists are able to coax insight on how to improve decision making by the business out of the vast stores of data created by the business.

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How Enterprise MLOps Supports Scaling Data Science

Domino Data Lab

For companies investing in data science, the stakes have never been so high. According to a recent survey from New Vantage Partners (NVP), 62 percent of firms have invested over $50 million in big data and AI, with 17 percent investing more than $500 million. Expectations are just as high as investment levels, with a survey from Data IQ revealing that a quarter of companies expect data science to increase revenue by 11 percent or more.

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How Enterprise MLOps Works Throughout the Data Science Lifecycle

Domino Data Lab

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. For companies creating models to scale, an enterprise Machine Learning Operation (MLOps) platform not only needs to support enterprise-grade development and production, it needs to follow the same standard process that data scientists use.

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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.