Conf42 Machine Learning 2021

Conf42 Machine Learning 2021 is going LIVE! Venture through 2 amazing keynotes and 21 mind-blowing talks! FREE RSVP to access all content: https://www.conf42.com/ml2021#register Meet and greet on Discord 🧑‍🤝‍🧑💬 https://discord.gg/DnyHgrC7jC — 0:00 Preamble Keynotes 🪐 0:44 Antje Barth - Amazon Web Services 1:17 Tempest van Schaik - Microsoft Getting Started 🐢 1:47 Milecia McGregor 2:06 Gajendra Deshpande Security 🦔 2:40 Madalina Burci 3:17 Suraj Muraleedharan Main 🐙 3:35 Hila Fox 3:56 Eduardo Dixo 4:25 Karl Weinmeister 4:47 Asif Mujawar 5:18 Nidal Albeiruti 5:59 Pawel Skrzypek & Anna Warno Tools 🐒 6:38 Ron Lyle Dagdag 7:06 Laura Ham 7:35 Aditya Lohia 8:12 Matteo Gabrielli 8:44 Nicola Pietroluongo 9:10 Mofizur Rahman 9:30 Dinesh Subramani Lessons Learned 🦉 10:24 Marianna Diachuk 10:57 Nicolas Metallo 11:24 Tim Spann 11:49 Joshua Arvin Lat 12:00 Thank you! — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Antje Barth Senior Developer Advocate - AI & ML @ Amazon Web Services Developing high-quality machine learning models involve many steps. We typically start with exploring and preparing our data. We experiment with different algorithms and parameters. We spend time training and tuning our model until the model meets our quality metrics, and is ready to be deployed into production. Orchestrating and automating workflows across each step of this model development process can take months of coding. In this session, I show you how to create, automate, and manage machine learning workflows using Amazon SageMaker Pipelines. We will create a reusable NLP model training pipeline to prepare data, store the features in a feature store, fine-tune a BERT model, and deploy the model into production if it passes our defined quality metrics. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Tempest van Schaik Biomedical Engineer @ Microsoft AI has made amazing technological advances possible; as the field matures, the question for AI practitioners has shifted from “can we do it?” to “should we do it?”. In this talk, Dr. Tempest van Schaik will share her Responsible AI (RAI) journey, from ethical concerns in AI projects, to turning high-level RAI principles into code, and the foundation of an RAI review board that oversees projects for the team. She will share some of the practical RAI tools and techniques that can be used throughout the AI lifecycle, special RAI considerations for healthcare, and the experts she looks to as she continues in this journey. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Milecia McGregor Developer Advocate @ Iterative Neural networks are great for complex data sets, but some sets have more features to figure out than others. Many times these features are initialized based on heuristics and they have to be tuned as the model returns predictions. With convolutional neural networks, the model tunes the features for itself. In this talk, you will learn some use cases for CNNs, how they work under the hood, and how you can create a CNN in Python. You’ll be able to see how convolutions and max-pooling help decrease the amount of pre-processing you have to do. By the end of the talk, you should have a good understanding of the basics of CNNs and how to implement them. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Gajendra Deshpande Assistant Professor @ KLS Gogte Institute of Technology Natural Language Processing(NLP) is an interesting and challenging field. It becomes even more interesting and challenging when we take into consideration more than one human language. when we perform an NLP on a single language there is a possibility that the interesting insights from another human language might be missed out. The interesting and valuable information may be available in other human languages such as Spanish, Chinese, French, Hindi, and other major languages of the world. Also, the information may be available in various formats such as text, images, audio, and video. In this talk, I will discuss techniques and methods that will help perform NLP tasks on multi-source and multilingual information. The talk begins with an introduction to natural language processing and its concepts. Then it addresses the challenges with respect to multilingual and multi-source NLP. Next, I will discuss various techniques and tools to extract information from audio, video, images, and other types of files using PyScreenshot, SpeechRecognition, Beautiful Soup, and PIL packages. Also, extracting the information from web pages and source code using pytessaract. Next, I will discuss concepts such as translation and transliteration that help to bring the information into a common language format. Once the language is in a common language format it becomes easy to perform NLP tasks. Next, I will explain with the help of a code walkthrough generating a summary from multi-source and multi-lingual information into a specific language using spacy and stanza packages. Outline 1. Introduction to NLP and concepts (05 Minutes) 2. Challenges in Multi source multilingual NLP (02 Minutes) 3. Tools for extracting information from various file formats (04 Minutes) 4. Extract information from web pages and source code (04 Minutes) 5. Methods to convert information into common language format (05 Minutes) 6. code walkthrough for multi-source and multilingual summary generation (10 Minutes) 7. Conclusion and Questions (05 Minutes) — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Madalina Burci Developer Ambassador @ TypingDNA Did you know that you can recognize people by the way they type, powered by machine learning? Attend this session if you want to find out about typing biometrics and how they balance Security and User Experience, as well as to learn how to easily test the technology with the TypingDNA API and Postman. The session will have a theoretical part, covering some basics of Multi-Factor Authentication and deep-diving into Typing Biometrics. The second part will be practical, seeing a live demo of how any user could easily leverage one of the most advanced keystroke dynamics recognition algorithms, through the TypingDNA API and Postman. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Suraj Muraleedharan Senior DevOps Consultant @ AWS Regulatory customers have multiple guardrails when running workloads on managed compute provided by AWS. This talk will focus on the setting up guardrails, deployment and monitoring of the ML services using Service Catalog Tools. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Hila Fox Squad Leader @ Augury I am part of a squad that is responsible for taking the AI engine insights and distributing them to our customers and in-house analysts. Our insights are the core of our product and due to this we need good visibility to be able to identify patterns and also when we are not performing as expected, to take action. In this talk I will share how we improved our visibility in our products and also our quality by monitoring the output of our ML pipelines. This was an iterative process which was performed by me and the Algo team in which we added metrics, dashboards and alerts. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Eduardo Dixo Senior Data Scientist @ Continental Drones with mounted cameras provide significant advantages when compared to fixed cameras for object detection and visual tracking scenarios. Given their recent adoption in the wild and late advances in computer vision models, many aerial datasets have been introduced. In this talk, we’ll explore recent advances in object detection, comparing the challenges of natural images with those recorded by drones. Given the successes achieved by pretraining image classifiers on large datasets, and transferring the learned representations, a set of object detectors fine-tuned on publicly available aerial datasets will be presented and explained. We’ll highlight existing libraries that mitigate the cost of training large models from scratch, by including pretrained model weights and model variants found in the literature. Both Convolutional Neural Networks and the newly developed Transformers applied to vision will be covered and compared, outlining the main features of each architecture. The presentation will be accompanied by code snippets for aiding understanding and delivering practical examples. This is aimed at a general audience familiar with Python. Knowledge of Computer Vision is a plus but not a requirement as we’ll introduce the necessary concepts. We’ll ground the presented model architectures and libraries on the task of object detection applied to aerial datasets and demonstrate that state-of-the-art methods are within everyone’s reach. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Karl Weinmeister Engineering Manager - Cloud/AI/ML @ Google Machine learning practitioners are solving important problems every day. They’re also experiencing a new set of challenges that are unique to ML projects. This session will cover what to watch out for in terms of building a model; model accuracy; transparency and fairness; and MLOps. The good news is that there are solutions. Attendees will hear about best practices and tools that will help address these issues. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Asif Mujawar Database Specialist SA @ AWS The solution describes the best practices and available services in the AWS Cloud to implement a seamless omni-channel experience for a customer interacting with a call center. The interaction can happen via all the popular channels: phone call, mobile or web chat and even smart home devices like Amazon Alexa. The seamless aspect of the solution refers to the effortless and trouble-free transition between the different means of support: chat with an Artificial Intelligence (AI) powered bot, live chat with an agent or phone support. From this unified solution access Customer Relationship Management (CRM) data and knowledge base information and route the call / chat based on ML Churn / Sentiment prediction within Amazon Connect. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Nidal Albeiruti Solutions Architect @ AWS Binary and simple sensors are widely used in IoT and IIoT worlds. These sensors can provide more features other than their state that can help different machine learning workloads. This session will focus on data preparation and feature engineering techniques to extract additional features from binary sensors specifically. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Pawel Skrzypek CTO @ 7bulls.com & Anna Warno Data Scientist @ 7bulls.com The presentation prepared by AI Investments and 7bulls.com team. We are working on time series forecasting for over 4 years and want to make a review of the latest and most advanced time series forecasting methods like ES-Hybrid, N-Beats, Tsetlin machine, and more. We will provide also tips and tricks for forecasting difficult, noisy, and nonstationary time series, which can significantly improve the accuracy and performance of the methods. The complete time series forecasting methodology will be presented as well, along with the most efficient supporting tools. Also, a brief introduction to the ensembling of predictions will be done. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Ron Lyle Dagdag Lead Software Engineer @ Spacee Have you ever wanted to make your apps “smarter”? This session will cover what every ML/AI developer should know about Open Neural Network Exchange (ONNX) . Why it’s important and how it can reduce friction in incorporating machine learning models to your apps. We will show how to train models using the framework of your choice, save or convert models into ONNX, and deploy to cloud and edge using a high-performance runtime. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Laura Ham Community Solution Engineer @ SeMI Technologies This talk is an introduction to the vector search engine Weaviate. You will learn how storing data using vectors enables semantic search and automatic data classification. Topics like the underlying vector storage mechanism and how the pre-trained language vectorization model enables this are touched. In addition, this presentation consists of live demos to show the power of Weaviate and how you can get started with your own datasets. No prior technical knowledge is required; all concepts are illustrated with real use case examples and live demos. Most of all data is unstructured. Additionally, data is often stored without context, meaning and relation to concepts in the real world. This means that all this data is difficult to index, classify and search through. While this is traditionally solved by manual effort or expensive machine learning models, Weaviate takes another approach to this problem. Weaviate is a vector search engine, which stores data as vectors and automatically adds context and meaning to new data. This enables to search through the data without using exact matching keywords. Moreover, data can be automatically classified. Weaviate is completely open source, has a built-in machine learning model, has a graph-like data model, completely API-based and is cloud-native. Weaviate uses a GraphQL API next to RESTful endpoints to interact with the data in an intuitive manner. Additionally, Python, Go, Java and JavaScript clients are available to facilitate interaction between Weaviate and your applications. GraphQL and client examples will be shown in the presentation. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Aditya Lohia Machine Learning Engineer @ Tod'Aers When we aim for better accuracies, sometimes we forget that the algorithms become more massive and slower. This fact renders the algorithms unusable in real-time scenarios. How do you deploy your solution? Which framework to use? Can you use Python for deploying my solution? Can you use Jetson Nano for multi-stream inferencing? If you are curious to solve these questions, join me in this talk to discover TensorRT and DeepStream and how they reduce your algorithm’s latency and memory footprint. NVIDIA TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. DeepStream offers a multi-platform scalable framework with TLS security to deploy on edge and connect to any cloud. If you are using a GPU and CUDA/Tensor cores, you can leverage the SDK framework to deploy bigger and better algorithms for your real-time scenarios. The main focus of this talk will be to demonstrate why, where, and how to use TensorRT and DeepStream. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Matteo Gabrielli Solutions Architect @ AWS AWS AI services are putting Machine Learning in the hands of every builder. In this talk we will explore how services like Amazon Comprehend could speed-up customers in getting insights from text and deliver value to their business. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Nicola Pietroluongo Senior Solutions Architect @ AWS Would you like to run inference in the cloud using automatic scaling, built-in high availability, and a pay-for-value billing model? In this talk you are going to see how to bundle your ML model to run serverless inference in response to events and where it’s suitable to do so. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Mofizur Rahman Developer Advocate @ IBM Kubeflow is a machine learning toolkit for Kubernetes where users can develop, deploy, and manage ML workflows in a scalable and portable manner. Deploying and maintaining it can be a bit tricky since Kubeflow is composed of many components such as notebooks and pipelines and their potential configurations. This makes the barrier to entry to Kubeflow very high and make it difficult for teams to adopt Kubeflow. To help alleviate some of these deployment woes the Kubernetes Kubeflow Operator was created. It automates the deployment, monitoring, and management of Kubeflow as a whole. In this session, users will learn how they can best leverage the Kubeflow Operator to quickly get Kubeflow up and running on their Kubernetes clusters. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Dinesh Subramani Solutions Architect @ AWS In this session, we showcase the power of machine learning in taking a large corpus of a foreign language text (we use the entire Wikipedia) and automatically learning word embeddings for that language. This is typically the first key step in building natural language processing (NLP) solutions, such as text classification or topic modelling. You see how easy it is to apply the BlazingText algorithm built into Amazon SageMaker in order to process the entire contents of Wikipedia in this language and visualize the results. You then can apply these learnings to any language of your choice. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Marianna Diachuk Data Scientist @ Restream It’s no secret that the deployment of the Machine Learning models conceptually is far from training those models and requires a different mindset. Some deal with it by having people dedicated to work on deployment since there’s actually a lot to do even when the model is still in the development phase and some just expect data scientists to do everything from modeling and analysis to deployment and monitoring. In this talk I’d like to share my experience with deployment starting from 2017 as well as the lessons I’ve learned. Wait for a couple of wild and sometimes embarrassing stories but at least (oops) I didn’t do it again. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Nicolas Metallo Senior Data Scientist @ AWS The University of Oxford houses 21 millions of objects in the collections of its Gardens, Libraries & Museums. Preserving these assets requires great care. In this talk, we will review how AWS helped them build a sector leading ML solution that increased access to its collections for students, researchers, and public visitors while saving its staff and volunteers a massive amount of work. This talk will show recent work related to the new AWS Case Study titled “University of Oxford introduces a sector leading Machine Learning prototype to augment Digitisation in Numismatics”. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Tim Spann Developer Advocate @ StreamNative A cloud data lake that is empty is not useful to anyone. How can you quickly, scalably and reliably fill your cloud data lake with diverse sources of data you already have and new ones you never imagined you needed. Utilizing open source tools from Apache, the FLaNK stack enables any data engineer, programmer or analyst to build reusable modules with low or no code. In this talk we will utilize Apache NiFi, Apache Pulsar, Apache Flink and MiNiFi agents to load CDC, Logs, REST, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before. I will teach you how to fish in the deep end of the lake and return a data engineering hero. Let’s hope everyone is ready to go from 0 to Petabyte hero. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast

Joshua Arvin Lat CTO @ NuWorks Interactive Labs It is not an easy task to design and build systems in the cloud that involve Machine Learning and Data Science requirements. It also requires careful planning and execution to get different teams and professionals such as data scientists and members of MLOps teams to follow certain processes in order to have a sustainable and effective ML workflow. In this talk, I will share the different strategies and solutions on how to design, build, deploy, and maintain complex intelligent systems in AWS using Amazon SageMaker. Amazon SageMaker is a fully managed machine learning service that aims to help developers, data scientists, machine learning practitioners, and MLOps teams manage machine learning experiments and workflows. We will start by discussing some of the important concepts and patterns used in production environments and systems. As we discuss these concepts and patterns, we will provide a couple of practical solutions and examples on using the different features and capabilities of Amazon SageMaker to solve the different needs of data science and MLOps teams. — 0:00 Intro 0:20 Talk — 🥇 Gold Sponsor AWS 🥈 Silver Sponsors ChaosNative Microsoft Restream SeMI Technologies Stream Native TypingDNA 🤝 Media Partners Bpb Infosec Conferences [ Inside Dev ] Manning O'Reilly Packt — Website 🚀🪐 https://www.conf42.com​ Reach Out 📧📭 mark@conf42.com Discord Server 🧑‍🤝‍🧑💬 https://discord.com/invite/dT6ZsFJ5ZM​ LinkedIn 👨‍💼💼 https://www.linkedin.com/company/4911...​ Twitter 🎵🐦https://twitter.com/conf42com​ Conf42Cast 🎧 http://www.conf42.com/podcast