Watch how machines get smarter by connecting language Hit like, comment, and subscribe for more AI insights! 🔔 AI, word embeddings, machine learning, natural language processing, AI learning, word context, language understanding, artificial intelligence, AI tutorial, how AI works, word mapping, AI insights, AI in language, smart AI, AI explanation. #AI #WordEmbeddings #ArtificialIntelligence #MachineLearning #NLP #AIExplained #TechExplained #SmartAI #AIInnovation #ContextClues #AIEducation

#datascience #tensorflow #automl In this live event we will look at new AutoML framework for building TensorFlow models by name Auto Tensorflow. This is a exclusive live event with creator of Aut Tensorflow, Hasan Rafiq Topics covered include Introduction to Auto Tensorflow Auto Tensorflow current capability and future roadmap Live Demo of Auto Tensorflow Overview and demo of Auto EDA and Auto What-IF Analysis Live Q&A

#datascience #machinelearning #deeplearning In this workshop we will identify melanoma in images of skin lesions and we will use images within the same patient and determine which are likely to represent a melanoma. Using patient-level contextual information may help the development of image analysis tools which can better support clinical dermatologists

#datascience #databricks #deltalake In this live session we will talk about databricks, newly launched features of databricks and deep dive into databricks delta lake features with a hands on demo Databricks Overview Databricks delta lake features and demo New features of databricks (SQL Analytics, delta share etc) Lakehouse Overview Q&A

#datascience #dataengineering #apacheairflow In this session Srinidhi will take us through Apache airflow with a quick demo on how to get started with it Topics Covered Airflow Overview Creating Airflow DAGs Demo – ETL on Postgres/Redshift using Airflow Dynamic DAGs/Tasks Airflow on Docker

#datascience #machinelearning #mlops In this webinar I will be demonstrating end to end processes from model development and model deployment. CI/CD aspect will focus on continuous model deployment. Topic Agenda includes Building an Image Classifier Creating a Streamlit web application Deploying the web application in K8s Continuous Integration pipeline (GitHub) Continuous Deployment pipeline

#datascience #machinelearning #featurestore MLOps (Machine Learning Operations) is a recent term that is concerned with how to automate model training, model validation, and model deployment. MLOps can be thought of as an extension of DevOps (Software Development Operations) with the goal to unify both ML applications development and operation from ML applications, making it easier for teams to deploy better models more frequently and more efficiently. A feature store is a feature computation and storage service that enables features to be registered, discovered, and used both as part of ML pipelines as well as by online applications for model inferencing. It enables engineers to apply software engineering development principles to ML, more precisely creating a hub for feature data where people and users can discover and share features and with other users within the organization. During this talk, Jim Dowling, CEO at Logical Clocks, will discuss and demo how the Hopsworks Feature Store enables MLOps workflows for training models using features from the Feature Store, analyzing and validating models, deploying them into online model serving infrastructure, and monitoring model performance in production.

#datascience #deeplearning #machinelearning Link to detailed video on DeepAutoViML - https://youtu.be/IcpwNNNXsWE In this video we will see how we can build a deep learning model in a single line of code. We will use DeepAutoViML to train our model and also see how we can easily inference on the trained model

#datascience #deeplarning #machinelearning In this video we will see how Deep AutoViML can accelerate deep learning model development for varied ML task This session will cover how Deep AutioViML allows you to Build custom pipeline for your dataset Analyze data Build custom transformation (preprocessing) pipeline Auto Build and Tune model Flexible with bring your own model

#datascience #machinelearning #ml In this video, Saishruthi Swaminathan will walk us through the lifecycle of a machine learning workflow This session will cover • Business discussion: What is the problem being solved? • What are the questions derived from the business problem? • Can the business problem be solved using the data? • Did you go through the design thinking to understand the unintended consequence of the approach? • Identify data source and data extraction process • Is the data collected representative of the problem to be solved? • Is there any sensitive data? Did we handle data as per the policy? • Explore data for insights • Develop the prediction model (Cover imbalance data handling) • Evaluate the model • Show deployment ways • How can you tell a overall story to non-data professionals? Data: https://www.kaggle.com/radmirzosimov/telecom-users-dataset

#kubernetes #docker #microservices Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available Kubernetes operates at the container level rather than at the hardware level, it provides some generally applicable features common to PaaS offerings, such as deployment, scaling, load balancing, and lets users integrate their logging, monitoring, and alerting solutions

#aws #devops #mlops In this live session we will understand what devops is and as well see a demo of DevOps on AWS. Session Coverage include Introduction to DevOps DevOps on AWS overview CI/CD pipeline with AWS CodePipeline + CodeCommit + CodeBuild Demo using AWS ECS Q&A

#datascience #machinelearning #courses Link to my YouTube channel - https://www.youtube.com/c/AIEngineeringLife (Subscribe to get notified on new courses) Link to GitHub repo - https://github.com/srivatsan88 This video is a quick introduction to courses, content and code in my channel AIEngineering. This provides a guide to navigate through my channel content

#machinelearning #streamlit #mlops Link to part 1 of the video - https://youtu.be/wfgcuwFAQ38 Link to model training video - https://www.youtube.com/watch?v=84J1fMklQWE Cloud Build is a managed service on Google Cloud Platform that allows you to continuously build, test and deploy containers on various google services like GKE, Cloud Run, App Engine and others Cloud Build can import source code from a variety of repositories or cloud storage spaces, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives. In this video we will see how we can deploy model on github triggers

#machinelearning #streamlit #mlops Link to model training video - https://www.youtube.com/watch?v=84J1fMklQWE Cloud Build is a managed service on Google Cloud Platform that allows you to continuously build, test and deploy containers on various google services like GKE, Cloud Run, App Engine and others Cloud Build can import source code from a variety of repositories or cloud storage spaces, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives.