Overview on Azure Machine Learning

This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.

Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.

The document provides information about an experienced machine learning solutions architect. It includes details about their experience and qualifications, including 12 AWS certifications and over 6 years of AWS experience. It also discusses their vision for MLOps and experience producing machine learning models at scale. Their role at Inawisdom as a principal solutions architect and head of practice is mentioned.

Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.

Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. In this session we will learn how to create data integration solutions using the Data Factory service and ingest data from various data stores, transform/process the data, and publish the result data to the data stores.

The document provides an overview of Vertex AI, Google Cloud's managed machine learning platform. It discusses topics such as managing datasets, building and training machine learning models using both automated and custom approaches, implementing explainable AI, and deploying models. The document also includes references to the Vertex AI documentation and contact information for further information.

Overview of the Azure Internet of Things platform as delivered as part of the Microsoft IoT Cloud Workshops in Dec 2018.

The document discusses Vertex AI pipelines for MLOps workflows. It begins with an introduction of the speaker and their background. It then discusses what MLOps is, defining three levels of automation maturity. Vertex AI is introduced as Google Cloud's managed ML platform. Pipelines are described as orchestrating the entire ML workflow through components. Custom components and conditionals allow flexibility. Pipelines improve reproducibility and sharing. Changes can trigger pipelines through services like Cloud Build, Eventarc, and Cloud Scheduler to continuously adapt models to new data.

This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.

Azure Synapse is Microsoft's new cloud analytics service offering that combines enterprise data warehouse and Big Data analytics capabilities. It offers a powerful and streamlined platform to facilitate the process of consolidating, storing, curating and analysing your data to generate reliable and actionable business insights.

Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.

This document discusses MLOps, which is applying DevOps practices and principles to machine learning to enable continuous delivery of ML models. It explains that ML models need continuous improvement through retraining but data scientists currently lack tools for quick iteration, versioning, and deployment. MLOps addresses this by providing ML pipelines, model management, monitoring, and retraining in a reusable workflow similar to how software is developed. Implementing even a basic CI/CD pipeline for ML can help iterate models more quickly than having no pipeline at all. The document encourages building responsible AI through practices like ensuring model performance and addressing bias.

An introduction to the concepts and requirements to get started developing data pipelines in Azure Data Factory version 1.

Adoption of ML at scale in the Enterprise, Machine Learning Platforms & AutoML [1] Definitions & Context • Machine Learning Platforms, Definitions • ML models & apps as first class assets in the Enterprise • Workflow of an ML application • ML Algorithms, overview • Architecture of a ML platform • Update on the Hype cycle for ML & predictive apps [2] Adopting ML at Scale • The Problem with Machine Learning - Scaling ML in the Enterprise • Technical Debt in ML systems • How many models are too many models • The need for ML platforms [3] The Market for ML Platforms • ML platform Market References - from early adopters to mainstream • Custom Build vs Buy: ROI & Technical Debt • ML Platforms - Vendor Landscape [4] Custom Built ML Platforms • ML platform Market References - a closer look Facebook - FBlearner Uber - Michelangelo AirBnB - BigHead • ML Platformization Going Mainstream: The Great Enterprise Pivot [5] From DevOps to MLOps • DevOps <> ModelOps • The ML platform driven Organization • Leadership & Accountability (labour division) [6] Automated ML - AutoML • Scaling ML - Rapid Prototyping & AutoML: • Definition, Rationale • Vendor Comparison • AutoML - OptiML: Use Cases [7] Future Evolution for ML Platforms Appendix I: Practical Recommendations for ML onboarding in the Enterprise Appendix II: List of References & Additional Resources

This document discusses architecting a data lake. It begins by introducing the speaker and topic. It then defines a data lake as a repository that stores enterprise data in its raw format including structured, semi-structured, and unstructured data. The document outlines some key aspects to consider when architecting a data lake such as design, security, data movement, processing, and discovery. It provides an example design and discusses solutions from vendors like AWS, Azure, and GCP. Finally, it includes an example implementation using Azure services for an IoT project that predicts parts failures in trucks.

This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.

Amazon SageMaker is an end-to-end machine learning platform that allows users to build, train, and deploy machine learning models at scale. It provides pre-built machine learning algorithms, notebook instances to build models, one-click training for ML/DL models and custom algorithms, and deployment of trained models without additional engineering effort. SageMaker also manages and scales model inference clusters and APIs for production.

So you got a handle on what Big Data is and how you can use it to find business value in your data.  Now you need an understanding of the Microsoft products that can be used to create a Big Data solution.  Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together.  How does Microsoft enhance and add value to Big Data?  From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way