Data Science
MLOps is like process engineering for Data Science
The goal of MLOps is to streamline the development, deployment, and operation of machine learning models, by supporting their building, testing, releasing, monitoring, performance tracking, reusing, maintenance and governance, joining the efforts of Data Science and IT teams under a shared focus.
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Highlights of 2020 - as read by you
What better way to round off 2020 than with a review of our most popular content as read by you. We've selected just 6 of your most enjoyed articles this year in case you fancy diving in again. From AI and data fundamentals, to the use of AI in different sectors, and even a quick win AI project, here's what made the cut.
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What is Python?
Python is a popular programming language with a wide variety of applications including data science, web development, scientific computing, and software development.
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What is Jupyter?
Jupyter is a suite of products that support data science and scientific computing, including Jupyter Notebook, JupyterHub and JupyterLab. It is 100% open-source software, free for all to use and released under the liberal terms of the modified BSD license.
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What is AI?
Artificial intelligence is a technology that can ‘think’ in a human-like way. It is a diverse area of computer science that enlists tools such as machine learning and image processing to recreate and build upon the complex capabilities of the human brain.
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What is data science strategy?
Data science strategy refers to a company’s vision for how data will be used to help achieve the company’s business goals, how to build a thriving data culture, and how to address the skills and knowledge required to execute this vision.
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What is data strategy?
Data strategy refers to a company’s vision for how data will be used to help achieve the company’s business goals. This enables companies to analyse progress and make better business decisions, but it also helps to create a thriving data environment where it can be easily shared and accessed, safely stored and standardised, and viewed as a central resource to all departments.
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What is AI strategy?
AI strategy refers to a company’s vision for how AI will be deployed to help achieve the company’s business goals. It should be closely linked to your data strategy and hence to your business objectives.
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What is data science consulting?
Data science consulting is a service that will help your organisation develop a data science strategy and implement data science solutions. You may need a consultant to help you start integrating data science into your business strategy, or to help keep your projects on track.
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What is data science auditing?
Data science auditing is a review of your data science solutions to assess their quality, security and compliance. This helps businesses produce good quality data science solutions.
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What is data platform development?
A data platform is a service that includes data collection, storage and analysis. Developing a data platform creates a central service that encompasses a data warehouse, business intelligence tools, and a suite of data science and analytical environments.
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What is data science development?
Data science development is the development of custom data science solutions (algorithms, models) from the data source to the deployment of the models.
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What is data science?
Data science applies scientific methods to data to reveal insights and predictions that can help you to meet your business goals. This helps businesses build better products and make better decisions.
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What is business intelligence?
Business intelligence refers to the systematic collection, integration, analysis and reporting of data from all areas of your business - from finance and sales to worker productivity - to drive better decision making.
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What is remote support?
Remote support tools are IT tools and software that enable an IT technician or a support representative to connect to a remote computer from their consoles via the Internet and work directly on the remote system.
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What does it mean to be AI-Ready?
Artificial Intelligence (AI) and Machine Learning (ML) have burst into the spotlight in recent years getting attention from businesses and all levels of society. Awareness of AI has drastically increased as people become more familiar with how the tech giants are using data to enhance their products and create better solutions. The market is opening up to more AI-infused products, and the public are regularly interacting with AI as it slips into their day-to-day lives through smartphones and virtual assistants.
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The AI Hierarchy of Needs meets the Minimum Viable Product
Two of my favourite pyramids are the Data Science Hierarchy of Needs and the Minimum Viable Product. Combining them helps us build effective artificial intelligence (AI) proof of concepts in businesses. It also supports building AI competency at the same time as demonstrating Return on Investment (ROI).
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Expert Perspectives: Enhancing business with data and AI
This week we spoke to Dr. Leila Etaati, co-founder, data scientist, consultant and mentor at RADACAD, about what she thought was the key to success with AI for businesses, and how her business was implementing these beliefs. The RADACAD team work with other companies to deliver expert training and consulting around all things data, with a passion for helping businesses improve by listening to their data.
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How to get AI to work for your business and enhance operations
Enterprise cognitive computing is the application of AI to enhance business operations. It has a wide range of applications including call handling, fraud detection and maintenance scheduling. ECC systems automate repetitive tasks and improve efficiency through fast search and information processing.
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