What is NDR?

NDR is an artificial intelligence conference. Well, technically, it’s an artificial intelligence, and machine learning, and deep learning, and data science conference. We don’t discriminate :).

NDR-113 is also the main character in Isaac Asimov’s beautiful sci-fi novel, The Positronic Man. The book tells of a robot that begins to display human characteristics, such as creativity, emotion, and self-awareness. We felt that naming our conference after him was an appropriate homage to the story.

NDR is something we think you’re going to love.


Chopin Hall, Palas Ensemble
Iasi, Romania

June 7th, 2018


The good people at Strongbytes and Codecamp, naturally.

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Bringing Great Minds Together

Your chance to meet international experts and learn from their experience

We aim to bring together practitioners of data science, machine learning and deep learning. Filled with selected technical talks, our conference is designed to educate and inspire the audience, with experienced speakers discussing their own experiences, showcasing best practices, and business use cases.

An Energising Experience

1 day, 1 track, 11 sessions

At the end of the day you’ll come out excited and exhausted, wanting more. You will get a better understanding of how to build intelligent applications, and see how companies are using intelligent techniques in production. You will find out about new tools and techniques, how you can improve your workflow, or how you can start your data science career.

See you there!

NDR is coming up in

Our Speakers

Conference Schedule

  • NDR June 8 2021

  • NDR June 9 2021

  • 14:00 - 14:40
    Tools and methods to track, analyze, and operate ML models have gotten mature in the past years. This talk is an introduction to MLflow, an open-source platform for managing the end-to-end machine learning (ML) lifecycle. In this talk, you will see a hands-on presentation on how to track and serve the ML lifecycle with the help of MLflow. Attendees will get all code, notebooks, and datasets used in the talk. YOU WILL LEARN ABOUT: - MLOps in general - The MLflow technology - Working with ML experiments - Tracking and analyzing ML model metrics - Using MLflow's model registry to manage a model's lifecycle

  • 14:45 - 15:25
    Deep learning achieves great performance in many areas, and it’s especially useful for computer vision tasks. However, using deep learning in production is challenging: it requires a lot of effort for developing the infrastructure to serve deep learning models at scale. In this talk, we present the system for image classification we built at OLX. The main requirement for this system is to classify tens of millions of images daily and be able to serve reliably even during peak hours. It took a year and lots of trial and error to arrive at the system we currently use. We’ll present the details of this journey and tell our story: how we approached it initially, what worked and what didn’t, how it evolved and how it’s working right now. Of course, we’ll walk you through the technical details and show how to implement a similar system using Python, AWS, Kubernetes, MXNet, and TensorFlow.

  • 15:40 - 16:20
    A goal of commercial insurers is to accurately classify small businesses according to the risks they face and thereby determine the correct insurance premiums. Setting the proper compensation is essential because charging too much or too little can harm insurers and their customers. 50% of the business applications required the industry classification code correction. This mainly was a manual exercise of looking up business details on the internet or purchasing business classification codes from 3rd party providers. The recent advancements in computational power and machine learning have led to vast improvements. However, the accuracy level was still less than the industry norm until the Cognizant team experimented with business ontology. The Cognizant team coded the ontology model for restaurant business using the NAICS and ISO industry manual. With details around the business name, address, and URL Cognizant team extracted the data about the company from its corresponding website and 3rd party data providers. It was able to classify the restaurant business with >90% accuracy. This session describes ontologies and their use in computational reasoning to support the precise classification of small businesses for insurance applications.

  • 16:25 - 17:05
    Like the name suggests Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure to achieve more efficiency in IoT operations, improve human-machine interactions and enhance data management and analytics. Current IoT systems can only react to an event while AIoT systems can proactively detect failures and events. The infusion of AI in IoT systems delivers the promise of predictive maintenance which will help organizations save millions of dollars in support and maintenance of equipment. Moreover, the future of industrial automation lies in the convergence of AI and IoT. The importance of AIoT to Bosch can be gauged by the fact that the Artificial Intelligence of Things will impact almost every industry vertical including automotive, aviation, finance, healthcare, manufacturing and supply chain.

  • 17:20 - 18:00

  • 18:05 - 18:45
    The main challenge in the knowledge industry is to identify relevant data hidden within layers of noise and to build meaningful inferences – assessments, ratings, opinions – based on this data. In the ESG (Environmental, Social and Governance) research and ratings industry, this challenge is especially salient these days, as companies worldwide want to show to their stakeholders that company ESG performance is concordant with increasing stakeholder expectations. We will show how a leader in the ESG industry Sustainalytics, a Morningstar company, has built a unique combination of document embedding and fast similarity detection to identify relevant corporate disclosure and to build automated inferential procedures to texts to the reality these texts describe.

  • 14:00 - 14:40
    "My Pandas is slow!" - I hear that a lot. We'll look at ways of making Pandas calculate faster, help you express your problem to fit Pandas more efficiently and look at process changes that'll make you waste less time debugging your Pandas. By attending this talk you'll get answers faster and more reliably with Pandas so your analytics and data science work will be more rewarding.

  • 14:45 - 15:25
    The autonomous driving dream is closer and closer to becoming a reality. We at Continental use machine learning to help the vehicles of tomorrow perceive their environment with great agility and precision, in order to assist the driver, and eventually to enable superhuman driving performance. This session will detail some of the ways in which deep learning models are used to detect, localize, and classify traffic participants, and to build a comprehensive environment model of the surroundings of the vehicle.

  • 15:40 - 16:20
    Ciprian and Patrick will discuss where things stand in the Quantum Computing world with an eye toward how close we might be to results that impact the lives of people in the real world. This 40 minute session will start with an overview in the first half and respond to questions for the second half of the session.

  • 17:20 - 18:00
    Machine learning operations (MLOps) is the use of machine learning models by development/operations (DevOps) teams. MLOps provides the tools and best practices to deploy, monitor, manage, and govern machine learning models in production. In this session you will learn MLOps best practices, with the goal of: - Faster experimentation and development of models - Faster deployment of models into production - Quality assurance


Community Partners



Chopin Hall - Palas Congress Hall, Palas Street no. 7A, Iași, Romania 700259