The beauty of open source software is that it allows you to create, experiment and transform code, and even give it a higher purpose. After discovering and deep diving into a new and exciting security scanning tool, with the help of our engineering team, we began making this tool into something more. What initially could have been used for red-teaming, bug bounty hunting or hacking in general was transformed into a tool that can help blue teams defend against the bad guys better.

Mentorship is the partnership between two parties called mentor - the actor that shares knowledge and offers guidance and mentee - actor that receives the knowledge and is being guided. Together, they will embark on a journey of self-discovery and development in both of their professional and personal lives.

To manage a product with success doesn’t have a clear recipe. It can easily be a magic formula each product manager finds out and applies to their own solution. What’s important is sharing the success stories and innovate the product management domain by network with like-minded people. Remember each product is unique and the approach we take to manage doesn’t guarantee the success for all products – in this context we should rather seek the method that best fits a product.

To conclude what we have covered so far, it's clear that when building a model, the trainer selection is not the most difficult part. AutoML is able to suggest a list with the best models, due to the evaluation metrics which accompany every model. What is much more complex (and time-consuming) is the data preparation which, along with the training pipeline, builds a model ready to make predictions.

Usually the longest phase, where the majority of people are involved in a project is the execution phase. Ensuring success in this phase poses the greatest challenges, as the four dimension of project management state that success depends on the project manager’s competencies, the structure of the organization, the process of progress monitoring, and the practices that are part of the organization culture (Kendra & Taplin, 2004). While competencies and progress monitoring depend solely on the project manager and are, therefore, considered controllable, the organization structure and culture is much greater, thus hard to manage. Therefore, in this essay we’ll consider these as fixed, and try to argue in favor of the working solutions to overcome these limitations.

Ovidiu Mățan: You have been leading Bosch Engineering Center Cluj for a few months now. How was the transition into your new role, and how do you see the integration of the technologies developed in Cluj into the Bosch global ecosystem?

Frank Wolf: I took over the role of Head of Bosch Engineering Center Cluj this year in July, and since then I am responsible for leading this great organization with more than 800 employees and about 200 interns.

At the end of the first part of the series we had a preprocessing pipeline ready to load data and concatenate the features selected for the training into one special feature called Features, and a target feature named Label serving as a category where the selected feature classifies.

Data being the most valuable commodity nowadays, it lies at the core of almost every business, irrespective of its domain. Whether we’re talking about search engines, entertainment platforms, ecommerce stores or fintech solutions, they are all about data and represent it in the most meaningful way to their users. In the process of gathering these vast amounts of data, many organizations are leveraging web scraping to extract information from 3rd party sources, such as websites. In most cases, the existing information on these websites is designed for human end-users.

Six years ago, I started to work on the first microservices projects. It was a migration from a monolithic solution to something different, to containers and microservices. Fast forward, we are in 2020, and in ALL engagements where I am involved, we have containers and something more – serverless.

The purpose of these series of articles is to provide a complete guide (from data to predictions) to machine learning, for .NET developers in a .NET ecosystem, and that is possible now using Microsoft ML.NET and Jupyter Notebooks. Moreover, you don't have to be a data scientist to do machine learning.