DataPlatformGeeks successfully executed SEVEN webinars and TWO In-person events in the month of November, 2019.

  • Webinar on Understanding SQL Server Indexes – The Right Way by Satya Ramesh (Sr. Consultant, SQLMaestros)
  • Webinar on Develop zero-code Machine Learning models using Microsoft Technologies by Anupama Natarajan (AI MVP, MCT, New Zealand)
  • Webinar on Deep-Dive Session on SQL Server Column Store Indexes by Vijay Reddy (Regional Mentor, DPG)
  • Webinar on Machine Learning Basics using R & Python by Deepak Pradeep Kumar (Sr. Software Engineer, Microsoft)
  • Webinar on Implementing AI solutions using Azure Machine Learning Services by Vitor Fava (Data Platform MVP, Brazil)
  • Webinar on Developer Tools for Microsoft Azure by Radu Vunvulea (Data Platform MVP, Romania)
  • Webinar on Azure Machine Learning for MLOps by Mercy Ranjith (Solution Architect, Microsoft)
  • Understanding SQL Server Indexes – The Right Way

    Abstract: In this session, we are going to cover the basics of Clustered Indexes & Non-Clustered Indexes.
    1. How to create them and some best practices to follow.
    2. What is a covering index and how is it useful.
    3. What is fragmentation and how to defrag indexes
    4. What is fill factor and how is is useful.

    Develop zero-code Machine Learning models using Microsoft Technologies

    Abstract: Artificial Intelligence is undoubtedly booming in the business and every organisation wants to use AI to transform their business. Machine Learning, a category within AI has become far more effective and widely available enabling organisations to build systems that can learn how to perform tasks on their own. Whether you are a Developer or Data Scientist and new to Machine Learning, Microsoft provides tools to develop models without writing single line of code to accelerate your AI journey in your organisation.

    Deep Dive Session on SQL Server Column Store Indexes

    Abstract: In this webinar you will get to learn about SQL Server Column Store Indexes Overview and Use Cases.

    Machine Learning Basics using R & Python

    Abstract: Machine learning is essentially a form of applied statistics with lot of emphasis on the use of computers to statistically estimate complicated functions and find patterns on sample set / new set of data. A machine learning algorithms are set of algorithms that are able to learn from data about the experiences, trends, tasks, performance measures and improves the experience with new set of data.
    Some of the common machine learning tasks include Regression, Classification, Synthesis and Sampling, Imputation of missing values. We can look into few machine learning algorithms to train the machines and apply them to real world examples.
    We shall continue to have the series of Machine learning algorithms and go through Deep learning techniques to ensure you are industry level ramped up for implementing Deep learning techniques.

    Implementing AI solutions using Azure Machine Learning Services

    Abstract: Artificial Intelligence (AI) is bringing big changes in the way people and businesses relate to technology. As well as the arrival of the personal computer, the cloud computing, and smartphones, the AI is the artificial technology that takes you to where you’re going so much faster, intuitive and smart. In this session, we will discuss the basics of IA and how we can apply it in our business using the Azure Machine Learning Services.

    Developer Tools for Microsoft Azure

    Abstract: During this session, we’ll take a look at the proactivity tools that can be used to improve our development experience on Azure. We’ll talk about tools from multiple areas like storage, computation, automation, cleaning and many more. All of them are free to use, build by the Azure community or Microsoft to improve the cloud experience.

    Azure Machine Learning for MLOps

    Abstract: MLOps will enable you to build Continuous Integration and Continuous Delivery pipeline for a ML/AI project. We will be seeing how to use the Azure DevOps Project for build and release/deployment pipelines along with Azure ML services for model retraining, model management and operationalization.

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