Code repository for this post is here: you can see the MLFlow Recipes template in the main branch and the filled-in template on the fill-in-steps branch. The announcement of MLFlow 2.0 included a new framework called MLFlow Recipes. For a Data Scientists, using MLFlow Recipes means cloning a git repository, or "template", that comes with a ready-to-go folder structure for any regression or binary classification problem. This folder structure includes everything, from library requirements, configuration, notebooks and tests, that's needed to make a data science project reproducible and production-ready. It's easy to start a new project with MLFlow Recipes -- git clone a template from the MLFlow repository, and you are good to go.

When you look at a function program's source code, how do you know what it means--that is, what object or process is this function representing? Is the meaning found in the return values of the function, or is it located inside the function body? Answering these questions is important to understanding how to share domain knowledge among programmers using the source code as the medium. Whether debugging or adding new features to a program, programmers must read the code to understand what the program is doing. From this reading, the programmers must also know how the problem domain is represented in the code, so they can be certain that their changes to the source code won't make the program work in unexpected ways.

I hope you read and enjoyed my previous blog titled'Introduction to Rasa X' since it is a precursor to this one. In case you haven't, you can read it here. In this blog, I am going to lead you through the installation, folder structure, controls, and features of Rasa as well as Rasa X to develop an assistant. Let's first dive into installing Rasa. To install Rasa, you require Python 3.7 or Python 3.8.

Task 35: Using the trained mask detection model to predict face mask on images.

In this section we will train a simple digit recognition model using the MNIST dataset provided through the TensorFlow library². To load the dataset, in the first cell of you notebook type the following. This will display the shape of the training data inputs (tx) and target (ty), and validation data inputs (vx) and target (vy). Let's now display a set of 10 sample images for each digit to understand how the data looks like. This will generate the following grid of images.

Good quotes help make us stronger. What is truly inspiring about quotes is not their tone or contentedness but how those who share them reflect life experiences that really serve others. I didn't write the above quote about quotes (Quote-ception; bad pun?), but an AI model I trained did. And it says it better than I would have. Quotes are something that means different things to different people.

Computer vision has many uses. It can recognise faces, it can be used in quality control and security and it can also recognise very successfully different object on the image. Today we will look at the last example. We will build a supervised machine learning model to recognise cats and dogs on the image using Neural Networks. You will learn how to create and configure a Convolutional Neural Network (CNN).

As the word'pipeline' suggests, it is a series of steps chained together in the ML cycle that often involves obtaining the data, processing the data, training/testing on various ML algorithms and finally obtaining some output (in the form of a prediction, etc). Unlike a traditional'pipeline', new real-life inputs and its outputs often feed back to the pipeline which updates the model. This article by Microsoft Azure describes ML pipelines well. Simply put, ML has become so widespread so quickly that accuracy of models has become equally important as the ability to access, scale and store these models. A ML pipeline is essentially an automated ML workflow. While they represent a fast and efficient way for data teams to build and deploy, this article does not address these aforementioned services.)

As the word'pipeline' suggests, it is a series of steps chained together in the ML cycle that often involves obtaining the data, processing the data, training/testing on various ML algorithms and finally obtaining some output (in the form of a prediction, etc). Unlike a traditional'pipeline', new real-life inputs and its outputs often feed back to the pipeline which updates the model. This article by Microsoft Azure describes ML pipelines well. Simply put, ML has become so widespread so quickly that accuracy of models has become equally important as the ability to access, scale and store these models. A ML pipeline is essentially an automated ML workflow. While they represent a fast and efficient way for data teams to build and deploy, this article does not address these aforementioned services.)

The fields of knowledge management, information management, and content management have become critical to a modern workplace. Finding, documenting, and knowing things in an environment where data is dispersed, employees are always on the fly, and career paths change fast must be intuitive, simple, and seamless. Since the early 2000s, employing sound file management practices, mixed with optimized search, have been the rules of thumb when it comes to KM and ECM systems. But no longer will that be enough. From this nascent point in the era of AI and chatbots, you will be left behind if you're not putting a good bot to work.