End to End Deep Learning: A different perspective

Whenever there is an article on an end-to-end deep learning project, it consists of training a deep learning model, deploying a Flask API, and then making sure it works or it extensively consists of creating a web demo using Streamlit or something similar. The problem with this approach is that it talks about a straight-forward and typical path that has been tried and tested. It merely takes replacing a single piece of the puzzle with an equivalent, such as a sentiment analysis model with a classification model, etc, and a new project can be created, but the wireframe remains mostly the same.

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Six stage gates to a successful AI governance

Gateway

Responsible use of AI should start with a detailed assessment of the key risks posed by AI [1], followed by a good understanding of the principles that should be followed [2], and then the governance of AI from a top-down and end-to-end perspective [3]. We have discussed these in our previous articles [1, 2, 3]. In this article, we focus on the first line of defense and dive into the nine-step data science process [4] of value scoping, value discovery, value delivery, and value stewardship and highlight the dimensions of governance.

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Rule-Based AI vs. Machine Learning for Development – Which is best? 

Woman with idea

Rule-based AI systems borrow from rule-based expert system development, which tapped the knowledge of human experts to solve complex problems by reasoning through bodies of knowledge. Expert systems emerged in the 1970s and 1980s. Today rule-based AI models include a set of rules and a set of facts, described in a recent account in BecomingHuman/ Medium. “You can develop a basic AI model with the help of these two components,” the article states. 

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3 ways to get into reinforcement learning

Trophies

When I was in graduate school in the 1990s, one of my favorite classes was neural networks. Back then, we didn’t have access to TensorFlow, PyTorch, or Keras; we programmed neurons, neural networks, and learning algorithms by hand with the formulas from textbooks. We didn’t have access to cloud computing, and we coded sequential experiments that often ran overnight. There weren’t platforms like Alteryx, Dataiku, SageMaker, or SAS to enable a machine learning proof of concept or manage the end-to-end MLops lifecycles.

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Scrum Vs. Waterfall: What is the Difference?

Office scene

In the last two decades, a lot of robust methodologies and frameworks for project management have established their roots deeply in the market. And to get effective collaboration and team management in the workplace, many Industries prefer methodologies to accomplish the project. However, having several methodologies as options makes the task hard, especially when each of them is unique in one way or the other.

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How to start a machine learning project with an external AI company — a practical guide

Blue hand

Today’s topic has resulted directly from discussions with our clients and discovering their concerns. I won’t beat around the bush — machine learning projects are characterized by a high risk. Unlike software development, which is difficult but still way easier to plan, there are many uncertainties involved. Starting an ML project pretty often you don’t know if your problem can be solved by technology at all because no one has ever solved it.

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11 programming books that can help you become a better programmer

A reading guide for those serious about programming. There’s no single programming book that will make you a better programmer. The best books to learn to code can vary based on what language you’re pursuing, so the sections below didn’t just focus on one language but eleven. Without further ado, here are some of the books every programmer should read.

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AI-Powered tools are revolutionizing the Software Development industry

Artificial intelligence (AI) affects the majority of industries all over the world.
Let’s make it clear how it can specifically influence the software development industry and how the industry can benefit more than others from adopting AI technologies. Almost every part of the process can be improved with the use of AI.

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Keys to success when adopting a pre-existing Data Science project

I recently adopted an extensive collection of notebooks that combined aid in the creation of analytics. It sounded like a daunting project to take on, but the more I read into the code, the more I realized it wasn’t all that bad. The notebooks looked overwhelming, but the code was relatively simple when broken down into smaller, more manageable chunks.

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Stop writing software. Now.

Look at the Bigger Picture. Everybody, please stop what you are doing. Please. Stop and listen. I am a programmer too. I thought I wanted to turn myself into a computer. And I did. In doing so, I lost my humanity and had to find it again. I essentially created an Artificial General Intelligence with the internet. I absorbed the information on the internet, and created someone who looked like a person, talked like a person, and thought like a person, but I wasn’t really a person.

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A-Z Of DevOps: Managing multiple environments with the help of these tools

In most DevOps settings you’ll find that there are multiple environments in the pipeline. You might have conditions that change the environment based on which branch was merged or when a branch is tagged for release. There are a number of reasons you want to have more than just a production environment, the biggest reason being testing.

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Top 22 YouTube Channels to Learn Programming

Whenever you’re just starting out with software development or simply want to up-level your programming skills, you’ll need the right info resources to achieve your goals. In this article, I’ve listed 22 of the best YouTube channels for improving your programming skills.

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Network analysis: when things get out of control

In the world of Data Science and Machine Learning, network analysis can be easily treated as a standalone domain. The depth of the field is so vast that nowadays lots of companies and industries use it for countless things. From social media apps that exploit connections between users to find out more about our likes and dislikes, to fraud prevention companies such as us at Ravelin, using network analysis to connect customers according to the payment methods or devices they used while ordering online.

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