The Spark + AI Summit Europe is just around the corner, and it's a great opportunity for data scientists and Machine Learning (ML) practitioners to get up to speed on the latest tools and innovations in the field!
Below is a selection of talks on ML best practices for productionizing ML at scale, real-life use cases, and popular tools like TensorFlow and MLflow, across the AI use cases, data science, machine learning, and deep learning tracks that will help you sharpen some of these skills.
AI Use Cases Track
Quby is a leading company offering data driven home services technology across European markets, known for creating the in-home display and smart thermostat Toon. Via their IoT devices, data teams at Quby have access to Europe’s largest energy dataset, at petabytes scale and growing exponentially. In Making Homes Efficient and Comfortable Using AI and IoT Data, Ellissa Verseput of Quby will describe how machine learning is implemented on the Quby platform and will show multiple use cases backed by high-resolution IoT data.
SK Telecom is the largest wireless telecommunications provider in South Korea with 300,000 cells and 27 million subscribers. These 300,000 cells generate data every 10 seconds, the total size of which is 60TB, 120 billion records per day. In Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction with Geospatial Visualization, Hongchan Roh and Dooyoung Hwang of SK Telecom will present how to analyze, predict, and visualize network quality data, as a spark AI use case in a telecommunications company.
Anomaly detection has numerous applications in a wide variety of fields. In banking, with ever growing heterogeneity and complexity, the difficulty of discovering deviating cases using conventional techniques and scenario definitions is on the rise. In Deep Anomaly Detection from Research to Production Leveraging Spark and Tensorflow, Davit Bzhalava and Shaheer Mansoor will present an outline of Swedbank’s ways of constructing and leveraging scalable pipelines based on Spark and Tensorflow in combination with an in-house tailor-made platform to develop, deploy and monitor deep anomaly detection models.
RTL Netherlands exists for 30 years in 2019. Video has been at the core of its business. AI gives us them opportunity to deeply understand what their consumers love. In How Data is Transforming the Dutch Media Industry, Maurits van der Goes of RTL Netherlands will cover several AI and ML methods currently used to extract and analyze features used for different data science products: new show and episode creation, talkshow subject selection, interpret viewing ratings among others.
Data Science, Machine Learning, and Deep Learning Track
In Koalas: Making an Easy Transition from Pandas to Apache Spark, Tim Hunter and Takuya Ueshin of Databricks will present Koalas, a new open-source project that aims at bridging the gap between the big data and small data for data scientists and at simplifying Apache Spark for people who are already familiar with the pandas library in Python.
In Migrating Apache Spark ML Jobs to Spark + TensorFlow on Kubeflow, Holden Karau of Google will take on two existings Spark ML pipelines and explore the steps involved in migrating them into a combination of Spark and TensorFlow using the open source Kubeflow project.
The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.x version, all with a focus on ease of usability and a better user experience. In Introduction to TensorFlow 2.0, Brad Miro of Google will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras.
Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In Seamless End-to-End Production Machine Learning with Seldon and MLflow, Alejandro Saucedo of Seldon will walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks.
Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. In Continuous Evaluation of Deployed Models in Production, Deepak Pai and Vijay Srivastava of Adobe will describe their experience from building such a core machine-learning services: Continuous Model Evaluation.
Societe Generale is one of the major banks in France and has many data science teams across the globe. After years of exploration and prototyping, it is time for the company to really deploy machine learning projects at scale to the production environment. In Machine Learning at Scale with MLflow and Apache Spark, Chongguang Liu and Mohamed Farhat will cover challenges and lessons learned throughout this journey.
Data Science and Machine Learning Classes
If you learn best by doing, don't miss our tutorial on Managing the Complete Machine Learning Lifecycle with MLflow, a 80-minutes session with an expert-led talk designed to introduce you to MLflow, a new open source framework for managing the ML lifecycle, followed by hands-on exercises allowing attendees to learn by doing.
Next, check out the Data Science with Apache Spark™ as well as the Hands on Deep Learning with Keras, TensorFlow, and Apache Spark™ and Machine Learning in Production: MLflow and Model Deployment training courses.
You can also browse through our sessions from the schedule, too.