Most modern Integrated Development Environments (IDEs) and code editors have a feature to search across available functionality and items in an open project. In JetBrains IDEs, this feature is called Search Everywhere: it allows users to search for files, actions, classes, symbols, settings, and anything from VCS history from a single entry point. However, it works with the candidates obtained by algorithms that don't account for semantics, e.g., synonyms, complex word permutations, part of the speech modifications, and typos. In this work, we describe the machine learning approach we implemented to improve the discoverability of search items. We also share the obstacles encountered during this process and how we overcame them.

Robot Framework is an open-source Test Automation framework. It was initially developed by Nokia Networks, however, it is now maintained by the Robot Framework Foundation. You will learn about the features, pros, and cons of the Framework along with instructions to install the needed software. Robot Framework is a Test Automation tool in which the test cases are written using keywords that makes it easy to learn and use. These keywords are written in a tabular form. With Robot Framework, the Test Scripts are replaced by a few keywords thereby replacing the need for large pieces of code.

This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don't need to have any technical knowledge to learn this skill. You'll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base Theory of Probability.

In early 2018, Google launched their highly anticipated IDE: Google Colaboratory. Its fairly recent launch makes it the newest major IDE on the market. It does not require any installation, and running from the google drive on a web browser, it comes pre-installed with many popular libraries such as PyTorch, Tensorflow, and Keras. Importing unique libraries is also simple, as!pip is already installed, making the complicated activation of a terminal obsolete. Google Colab is also linked to the Google Drive, meaning it is saved onto the cloud.

Today, we're extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML). We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling. Many of the tools which developers take for granted when building traditional software (debuggers, project management, collaboration, monitoring, and so forth) have yet been invented for ML. For example, when trying a new algorithm or tweaking hyper parameters, developers and data scientists typically run hundreds and thousands of experiments on Amazon SageMaker, and they need to manage all this manually.

In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain modeling in recent years. However, current knowledge engineering tools with visual modeling, like itSIMPLE (Vaquero et al. 2012) and VIZ (Vodr\'a\v{z}ka and Chrpa 2010), are less efficient than the traditional method of hand-coding by a PDDL expert using a text editor, and rarely involved in finetuning planning domains depending on the plan validation. Aim at this, we present an integrated development environment KAVI for planning domain modeling inspired by itSIMPLE and VIZ. KAVI using an abstract domain knowledge base to improve the efficiency of planning domain visual modeling. By integrating planners and a plan validator, KAVI proposes a method to fine-tune planning domains based on the plan validation.