We used to all want to get access to as wide a range of information, things, and experiences as possible. For a while now, this has been changing — just as there is a shift in viewing the Internet as a place with all possible knowledge, to viewing it a place with all of the world’s garbage and some knowledge — in the same manner, a change in what we want to focus on in general, can be noticed.
Once you know what goals you want to achieve in life, a more specific path can be mapped out for you. In that sense, your interests and focus should be directed into that specific goal. You don’t need to know everything about everything (does anyone?); you only need to know your preferred specialties. Here is why minimalism in all shapes and forms can be applied.

And all in an effort to make life easier for yourself.

Source de l’article sur DZone (Agile)

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Since February of 2018, scientists from Google’s health-tech subsidiary have pioneered innovative ways of creating revolutionary healthcare insights through artificial intelligence prediction algorithms. Based on the back of a patient’s eye scan, their system can make predictions against the patient’s risk of experiencing a severe cardiac incident.


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One of the most common questions I am asked in my Professional Scrum Master (PSM) courses and in coaching engagements is:

"How do we build trust?"

Source de l’article sur DZone (Agile)

Conversation drives sales and this is a well-known fact. For customers, it is important to have someone to ask questions and clarify doubts, someone who could guide them and recommend them the best option. Today, conversations can be automated, and today there is no need to have a physical person attached to each customer. Nowadays, conversational commerce became a fast-growing buzzword and chatbots play a key role in this field. Today, I would like to discuss why chatbots became so popular and why e-commerce and m-commerce companies heavily invest in it.

What Is a Chatbot?

First off, let’s make sure we are on the same page. What is a chatbot? 
A chatbot is a computer program or an artificial intelligence, which conducts a conversation via auditory or textual methods. It simulates how a human would behave in an automatic way, improving the efficiency of the process.


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Venky (Enterprise Architect): “Hey dude, Agile isn’t working for us and we are wasting so much time in ceremonies with no fruitful outcomes.”

Ramesh (Agile Practitioner): “Well, let us start with the objective. What are you trying to do?”

Source de l’article sur DZone (Agile)


Why Enterprise Application Companies Should Take a Cue From Apple’s Siri and Google Assistant

Enterprise applications are the next frontier in the adoption of natural language interfaces. Unlike consumer tech, e-commerce, and various chatbots where NLP/U is more of a technical novelty, the world of enterprise is a killer ground for natural language interfaces.

A Need for a Unified Interface

One of the key unique properties of natural language is the fact that it provides a unified interface to any data source or sources. It’s the one interface that everyone already knows, and at the same time, it’s the same interface to any supporting system. Think about it…you can easily ask a lawyer, salesman, or marketing professional about any specific topic as long as you can formulate a question in a minimally understandable way.


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Is a no-code platform a means to an end? Simply to build custom applications easily?

The answer is both yes, and no.

Source de l’article sur DZone (Agile)

In Part 1 of this series, we discussed the need for automation of data science and the need for speed and scale in data transformation and building models. In this part, we will discuss other critical areas of ML-based solutions like:

  • Model Explainability
  • Model Governance (Traceability, Deployment, and Monitoring)

Model Explainability

Simpler Machine Learning models like linear and logistic regression have high interpretability, but may have limited accuracy. On the other hand, Deep Learning models have time and again produced high accuracy results, but are considered black boxes because of the machine’s inability to explain their decisions and actions to human users. With regulations like GDPR, model explainability is quickly becoming one of the biggest challenges for data scientists, legal teams, and enterprises. Explainable AI, commonly referred to as XAI, is becoming one of the most sought-after research areas in Machine Learning. Predictive accuracy and explainability are frequently subject to a trade-off; higher levels of accuracy may be achieved but at the cost of decreased levels of explainability. Unlike Kaggle, competitions where complex ensemble models are created to win competitions, for enterprises, model interpretability is very important. Loan Default Prediction model cannot be used to reject loan to a customer until the model is able to explain why a loan is being rejected. Also, it is often required at the model level as well as individual test instance level. At Model level, there is need to explain key features which are important and how variation in these features affect the model decision. Variable Importance and Partial Dependence plots are popularly used for this. For an individual test instance level, there are packages like “lime,” which help in explaining how black box models make a decision.


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Part of the job of a Product Owner is to pay attention to the list of issues in the issue tracker. Not just to get a feeling for the cadence of the project, but to have an impact on its direction.

Paying attention to the issues doesn’t mean just tracking down what bugs are still opened, mind you. Consider the case of a Product Owner with the release due date looming over the horizon: he or she needs to start looking at the list of remaining issues and take active steps to make sure that they are going to get done more or less on time.

Source de l’article sur DZone (Agile)

If you are planning to experiment with deep learning models, Keras might be a good place to start. It’s a high-level API written in Python with backend support for Tensorflow, CNTK, and Theano.

For those of you who are new to Keras, you can read more at keras.io or a simple google search will take you to the basics and more on Keras.


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