With the extensive usage of cloud-based technologies to perform machine learning and data science related experiments, choosing the right toolset/ platform to perform the operations is a key part for the project success. Since selecting the perfect toolset for our ML workloads maybe bit tricky, I thought of sharing my thoughts on that by getting…

In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data science workloads in the cloud. This is the fourth course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurec ertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam.

U.S. Eligibility Requirements • Interested candidates must submit an application and resume/CV online to be considered • Must be 18 years of age or older • Must be willing to submit to a background investigation; any offer of employment is conditioned upon the successful completion of a background investigation • Must have unrestricted work authorization to work in the United States. For U.S. employment opportunities, Gallagher hires U.S. citizens, permanent residents, asylees, refugees, and temporary residents. Temporary residence does not include those with non-immigrant work authorization (F, J, H or L visas), such as students in practical training status. Exceptions to these requirements will be determined based on shortage of qualified candidates with a particular skill. Gallagher will require proof of work authorization • Must be willing to execute Gallagher's Employee Agreement or Confidentiality and Non-Disclosure Agreement which requires, among other things, post-employment obligations relating to non-solicitation, confidentiality and non-disclosure Gallagher offers competitive salaries and benefits, including: medical/dental/vision plans, life and accident insurance, 401(K), employee stock purchase plan, educational expense reimbursement, employee assistance program, flexible work hours (availability varies by office and job function) training programs, matching gift program, and more. Gallagher believes that all persons are entitled to equal employment opportunity and does not discriminate against nor favor any applicant because of race, sex, color, disability, national origin, religion, creed, age, marital status, citizenship, veteran status, gender, gender identity / expression, actual or perceived sexual orientation, or any other protected characteristic. Equal employment opportunity will be extended in all aspects of the employer-employee relationship, including, but not limited to, recruitment, hiring, training, promotion, transfer, demotion, compensation, benefits, layoff, and termination. In addition, Gallagher will make reasonable accommodations to known physical or mental limitations of an otherwise qualified applicant with a disability, unless the accommodation would impose an undue hardship on the operation of our business.

Azure Machine Learning, Azure Synapse Analytics and Spark using Azure Databricks are cloud services that you can use to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides. Azure Cognitive Services are APIs/SDKs/services available to help developers build intelligent applications without the need for AI or data science skills/knowledge. Azure Cognitive Services enable developers to easily add cognitive features such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The event will be held in English.

This course is designed to help you develop the skill necessary to perform ETL operations in Databricks, build unsupervised anomaly detection models, learn MLOPS, perform CI/CD operations in databricks and Deploy machine learning models into production.

Azure Synapse Analytics is an unlimited information analysis service aimed at large companies that was presented as the evolution of Azure SQL Data Warehouse (SQL DW), bringing together business data storage and macro or Big Data analysis. Synapse provides a single service for all workloads when processing, managing and serving data for immediate business intelligence and data prediction needs. The latter is made possible by its integration with Power BI and Azure Machine Learning, due to Synapse's ability to integrate mathematical machine learning models using the ONNX format. It provides the freedom to handle and query huge amounts of information either on demand serverless (a type of deployment that automatically scales power on demand when large amounts of data are available) for data exploration and ad hoc analysis, or with provisioned resources, at scale. As one of the few Microsoft's Power BI partners in Spain, at Bismart we have a large experience working with both Power BI and Azure Synapse.

Trey Research Inc. delivers innovative solutions for manufacturers. They specialize in identifying and solving problems for manufacturers that can run the range from automating away mundane but time-intensive processes to delivering cutting edge approaches that provide new opportunities for their manufacturing clients. Trey Research is looking to provide the next generation experience for connected car manufacturers by enabling them to utilize AI to decide when to pro-actively reach out to the customer thru alerts delivered directly to the car's in-dash information and entertainment head unit. For their PoC, they would like to focus on two maintenance related scenarios. In the first scenario, Trey Research recently instituted new regulations defining what parts are compliant or out of compliance.

To make sure that your experiment logs are reliably stored, Azure Databricks recommends writing logs to DBFS (that is, a log directory under /dbfs/) rather than on the ephemeral cluster file system. For each experiment, start TensorBoard in a unique directory. For each run of your machine learning code in the experiment that generates logs, set the TensorBoard callback or filewriter to write to a subdirectory of the experiment directory. That way, the data in the TensorBoard UI will be separated into runs.

PyTorch is a popular open source machine learning framework. PyTorch is ideal for deep learning applications such as computer vision and natural language processing. MLflow is an open source platform for the end-to-end machine learning lifecycle. Delta Lake is an open source storage layer that brings reliability to data lakes. Azure Databricks is the first-party Databricks service on Azure that provides massive scale data engineering and collaborative data science.

Azure Databricks provides an environment that makes it easy to build, train, and deploy deep learning models at scale. Many deep learning libraries are available in Databricks Runtime ML, a machine learning runtime that provides a ready-to-go environment for machine learning and data science. For deep learning libraries not included in Databricks Runtime ML, you can either install libraries as a Databricks library or use init scripts to install libraries on clusters upon creation.