Ciprian is the CEO of Genisoft, one of the most innovative Microsoft partners in Romania. Ciprian is also the Chief Data Scientist of Solliance, one of the top worldwide Microsoft AI partners. He is recognized internationally as a Microsoft Regional Director and a Microsoft Most Valuable Professional for Artificial Intelligence. Cloud Computing, Artificial Intelligence, and Machine Learning are some of the key areas of his expertise spanning 20+ years of IT. Ciprian is also very passionate about quantum physics and consequently, about quantum computing.
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In the past 10 years we’ve gone through a spectacular evolution of classical computing capacity. In addition to the classic CPU, we’ve seen the rise of the GPU and, lately, the rise of the FPGA with huge increases in computing power available for both general workloads and specialized workloads (like deep learning for example). The datacenters of the global public cloud providers are becoming ever larger, denser, and more powerful. In the era of AI we’re now able to harness all this tremendous power to deliver ground breaking solutions like face and emotion recognition, conversational intelligence, emotion analysis, outlier detection, and many more. Yet from a certain point of view, despite all the huge advancements in classical computing, it becomes obvious that we’re getting closer and closer to the hard limit imposed by mother nature. It’s one of those cases when, to move forward, one needs to completely change the context. Enters quantum computing, an approach based on the very spectacular and strange behavior of matter and energy governed by the laws of quantum physics. Join me in this session in a journey of quantum computing discovery that will amaze you and most probably will make you question everything you know about modern IT. We’ll talk about the why, the how, and the when and we’ll delve into the spectacular implications of this ground breaking new technology.
DevOps has become ubiquitous in the world of classical development. Almost all projects that exceed a certain level of complexity become inevitably DevOps projects. Yet there is one category of projects that are stepping out of the line. You’ve guessed it right, it’s the category of Data Science projects. When it comes to DevOps, Data Science projects pose a range of special challenges, whether it’s about the technical side of things, the philosophy of the people involved, or the actors involved. Think about one simple example: versioning. While in a “classical” development project versioning refers almost exclusively to source code, in the world of data science it gets another important aspect: data versioning. It’s not enough to know the version of the code for your model, it’s equally important to know the “version” of the data it was trained on. Another interesting question is, for examples, what does a “build” mean in the world of data science? Or a “release”? Join me in this session for an applied discussion about DevOps principles and approaches for AI and Machine learning projects. In addition to the principles, we’re also going to analyze an end-to-end example of a DevOps pipeline used in a real-world project.