#Microsoft #MicrosoftAI #microsoftopenai #openai #azureai
GPT family of models is taking over the world by storm. And using them has never been easier – Azure is offering OpenAI as a generally available managed service. But how can we start using it and infuse our solutions with generative intelligence? This session will answer this question along with addressing: what exactly is available on Azure from the OpenAI suite? What models should we use and when? And how can we work with, and integrate, this service in our development process?

#AuditIT, #Machinelearning, #ML, #AI, #datanalytics
A real-life example of implementation of machine learning technique to automatically analyze large amount of audited data in order to arrive with high-value IT audit observations and making entire audit process more efficient. What are the results of audit observations based on machine learning. The presentation could help you to understand how implementation of new audit analytics might improve auditing process and make the entire audit function modern and effective.

#machine_leaning #NLP #DSP #music_industry

The revolution of machine learning is reaching every aspect of our lives - including art and music.
In this talk, we will dive into the world of song analysis and the extraction of lyrical and musical features. We will discuss existing approaches, both in machine learning - Natural Language Processing, Digital Signal Processing - and in music theory & linguistics.

We will see how we can use these features in different kinds of machine learning models, and how these models can be used to solve problems in the music industry, such as song tags and song similarity.
Attend this talk to learn how your technical skills can be useful also to your hobbies.

#recommenderSystems #lean #machineLearning #development

Exploring Lean Data Science for Recommendation Systems

Showcasing the Solution Evolution - from heuristics to complex ML models

How different industries and organizations shape the approach of designing a recommendation systems

#activelearning #machinelearning #deeplearning #wisesampling

Active Learning - a way for collecting labeled data wisely, thus achieving equivalent or better performance levels with fewer data samples and saving time and money.

Background - what type of methods there is? What is the motivation behind using active learning?

Methods - two different methods will be describe, be demonstrating them on a specific real world use case

Results - what are the results of those methods compared to regular random sampling?

Validations - how can we validate the results? How can we be sure that all the components behaves as we want?

#timeseries, #datascience, #forecasting, #preprocessing, #python

Time series data is fundamental for most model businesses — however its handling involves a variety of common issues that can lead to serious consequences when not treated with care.

In this talk, I will introduce the audience to several such common issues, their causes and effective solutions.

This will include: (1) stabilising complex time series dynamics; (2) imputation of time-clustered missing values; (3) reducing impact of noise on forecasting models.

#ML, #Analytics, #ETL, #ModernData, #Kubernetes
Data Ops – become a trendy topic right now… easy and efficient ingestion, transformation and complex analytics of the data in various formats is becoming a must today. Simply to understand the data insights and gain competitive advantage.
Today's data pipelines assume combining efficient solutions for ingestions (i.e Kafka), transformations (i.e DBT) and Database Layer for ultrafast analytics workloads. All of them must meet following expectations:

• Simplified but yet Highly Scalable Architecture
• Fault-Tolerant Architecture
• Process High Volumes of Data (in Various Formats – structured or semi structured)
• Streamlined Data Pipeline Development

#feedback #loops #recommender #systems

degenerative feedback loops are present not only in recommender systems anymore

when data distribution shifts, model retraining will usually make the situation worse

talk summarizes how to detect and prevent degenerative feedback loops in production environment when applied to a mobile game

#AI #AnomalyDetection #RCA #Network #PredictiveNetworkMaintenance #AI-EmpoweredNetwork

Have you ever wondered how AI is used in the telco network area?

Orange, as data-driven and AI-powered telco company, place Data and AI at the heart of our innovations for Smarter Networks. In that case, network management is supported by Machine Learning models for detecting anomalies based on different types of network data. During this presentation we will zoom on Predictive Network Maintenance based on system logs from network nodes. Our aim is to shorten root cause analysis with Explainable AI to improve network experts daily job.

#marketing measurement, #machine learning, #bayesian statistics, #experiments
Not too long ago, digital marketers were enjoying the golden age of user-level tracking, which allowed for quick and efficient decision-making and testing. Today, privacy changes and regulations are fundamentally changing the industry landscape. In this talk, we will follow Twigeo, a growth-focused marketing agency, on its journey toward building a modern marketing measurement stack. We will go over the problem, business and data constraints, and solution highlights, such as combining Bayesian modeling with location-based experiments in a self-improving feedback loop. Finally, we will discuss how it's all brought together using a mix of cloud and open-source technologies such as Snowflake, Airflow, MLflow, and Streamlit.

Chronon is Airbnb's data management platform specifically designed for ML use cases. Previously, ML practitioners at Airbnb spent roughly 60% of their time on collecting and writing transformations for machine learning tasks. Chronon reduces this task from months to days - by making the process declarative. It allows data scientists to easily define features in a simple configuration language. The framework then provides access to point-in-time correct features - for both - offline model training and online inference. In this talk we will explore the problems that arise in industrial feature engineering context and explain how you can use Chronon to solve them.