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Empower your career – Discover the 10 essential skills to excel as a data scientist in 2023

Data Science Dojo

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. This includes sourcing, gathering, arranging, processing, and modeling data, as well as being able to analyze large volumes of structured or unstructured data.

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LLMOps demystified: Why it’s crucial and best practices for 2023

Data Science Dojo

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM. Why is LLMOps Essential?

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On the implementation of digital tools

Dataconomy

Forbes reports that global data production increased from 2 zettabytes in 2010 to 44 ZB in 2020, with projections exceeding 180 ZB by 2025 – a staggering 9,000% growth in just 15 years, partly driven by artificial intelligence. However, raw data alone doesn’t equate to actionable insights.

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Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Smart Data Collective

These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data. These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial intelligence and machine learning.

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Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning Blog

ML development – This phase of the ML lifecycle should be hosted in an isolated environment for model experimentation and building the candidate model. Several activities are performed in this phase, such as creating the model, data preparation, model training, evaluation, and model registration.

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5 Hardware Accelerators Every Data Scientist Should Leverage

Smart Data Collective

It is highly popular among companies developing artificial intelligence tools. This feature helps automate many parts of the data preparation and data model development process. Companies working on AI technology can use it to improve scalability and optimize the decision-making process.

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Integrating AI into Asset Performance Management: It’s all about the data

IBM Journey to AI blog

Imagine a future where artificial intelligence (AI) seamlessly collaborates with existing supply chain solutions, redefining how organizations manage their assets. If you’re currently using traditional AI, advanced analytics, and intelligent automation, aren’t you already getting deep insights into asset performance?

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