Submitted by Jatinder on Fri, 03/06/2020 - 07:14

Enterprises today are adopting machine learning and artificial intelligence (AI) at a rapid pace to stay ahead of their competition, deliver innovation, improve customer experiences, and grow revenue. However, the challenges with such integrations is that the development, deployment and monitoring of these models differ from the traditional software development lifecycle that many enterprises are already accustomed to.

Leveraging AI and machine learning applications, SNP helps bridge the gap between the existing state and the ideal state of how things should function in a machine learning lifecycle to achieve scalability, operational efficiency, and governance.

SNP has put together a list of the top 5 challenges enterprises face in the machine learning lifecycle and how SNP leverages Azure Machine Learning to help your business overcome them.

Q1. How much investment is needed on hardware for data scientists to run complex deep learning algorithms?

By leveraging Azure Machine Learning workspace, data scientists can use the same hardware virtually at a fraction of the price. The best part about these virtual compute resources is that businesses are billed based on the amount of resources consumed during active hours thereby reducing the chances of unnecessary billing.

Q2: How can data scientists manage redundancy when it comes to training segments and rewriting existing or new training scripts that involves collaboration of multiple data scientists?  

With Azure data pipelines, data scientists can create their model training pipeline consisting of multiple loosely coupled segments which are reusable in other training pipelines. Data pipelines also allows multiple data scientists to collaborate on different segments of the training pipeline simultaneously, and later combine their segments to form a consolidated pipeline.

Q3. A successful machine learning life cycle involves a data scientist finding the best performing model by using multiple iterative processes. Each process involves manual versioning which results to inaccuracies during deployments and auditing. So how best can data scientists manage version controlling?

Azure Machine Learning workspace for model development can prove to be a very useful tool in such cases. It tracks performance metrics and functional metrics of each run to provide the user with a visual interface on model performance during training. It can also be leveraged to register models developed on Azure Machine Learning workspace or models developed on your local machines for versioning. Versioning done using Azure Machine Learning workspace makes the deployment process simpler and faster.

Q4. One of the biggest challenges while integrating the machine learning model with an existing application is the tedious deployment process which involves extensive manual effort. So how can data scientists simplify the packaging and model deployment process?

Using Azure Machine Learning, data scientists and app developers can easily deploy Machine Learning models almost anywhere. Machine Learning models can be deployed as a standalone endpoint or embedded into an existing app or service or to Azure IoT Edge devices.

Q5. How can data scientists automate the machine learning process?

A data scientist’s job is not complete once the Machine Learning model is integrated into the app or service and deployed successfully. It has to be closely monitored in a production environment to check its performance and must be re-trained and re-deployed once there is sufficient quantity of new training data or when there are data discrepancies (when actual data is very different from the data on which your model is trained on and is affecting your model performance).

Azure Machine Learning can be used to trigger a re-deployment when your Git repository has a new code check-in. Azure Machine Learning can also be used to create a re-training pipeline to take new training data as input to make an updated model. Additionally, Azure Machine Learning provides alerts and log analytics to monitor and govern the containers used for deployment with a drag-drop graphical user interface to simplify the model development phase.

Start building today!

SNP is excited to bring you machine learning and AI capabilities to help you accelerate your machine learning lifecycle, from new productivity experiences that make machine learning accessible to all skill levels, to robust MLOps and enterprise-grade security, built on an open and trusted platform helping you drive business transformation with AI. Contact SNP here.

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Operationalizing ML Workflow using Azure Machine Learning

Top 5 FAQs on Operationalizing ML Workflow using Azure Machine Learning

March 06, 2020 | By Makarand P Batchu

Operationalizing ML Workflow using Azure Machine Learning

Enterprises today are adopting machine learning and artificial intelligence (AI) at a rapid pace to stay ahead of their competition, deliver innovation, improve customer experiences, and grow revenue. However, the challenges with such integrations is that the development, deployment and monitoring of these models differ from the traditional software development lifecycle that many enterprises are already accustomed to.

Leveraging AI and machine learning applications, SNP helps bridge the gap between the existing state and the ideal state of how things should function in a machine learning lifecycle to achieve scalability, operational efficiency, and governance.

SNP has put together a list of the top 5 challenges enterprises face in the machine learning lifecycle and how SNP leverages Azure Machine Learning to help your business overcome them.

Q1. How much investment is needed on hardware for data scientists to run complex deep learning algorithms?

By leveraging Azure Machine Learning workspace, data scientists can use the same hardware virtually at a fraction of the price. The best part about these virtual compute resources is that businesses are billed based on the amount of resources consumed during active hours thereby reducing the chances of unnecessary billing.

Q2: How can data scientists manage redundancy when it comes to training segments and rewriting existing or new training scripts that involves collaboration of multiple data scientists?  

With Azure data pipelines, data scientists can create their model training pipeline consisting of multiple loosely coupled segments which are reusable in other training pipelines. Data pipelines also allows multiple data scientists to collaborate on different segments of the training pipeline simultaneously, and later combine their segments to form a consolidated pipeline.

Q3. A successful machine learning life cycle involves a data scientist finding the best performing model by using multiple iterative processes. Each process involves manual versioning which results to inaccuracies during deployments and auditing. So how best can data scientists manage version controlling?

Azure Machine Learning workspace for model development can prove to be a very useful tool in such cases. It tracks performance metrics and functional metrics of each run to provide the user with a visual interface on model performance during training. It can also be leveraged to register models developed on Azure Machine Learning workspace or models developed on your local machines for versioning. Versioning done using Azure Machine Learning workspace makes the deployment process simpler and faster.

Q4. One of the biggest challenges while integrating the machine learning model with an existing application is the tedious deployment process which involves extensive manual effort. So how can data scientists simplify the packaging and model deployment process?

Using Azure Machine Learning, data scientists and app developers can easily deploy Machine Learning models almost anywhere. Machine Learning models can be deployed as a standalone endpoint or embedded into an existing app or service or to Azure IoT Edge devices.

Q5. How can data scientists automate the machine learning process?

A data scientist’s job is not complete once the Machine Learning model is integrated into the app or service and deployed successfully. It has to be closely monitored in a production environment to check its performance and must be re-trained and re-deployed once there is sufficient quantity of new training data or when there are data discrepancies (when actual data is very different from the data on which your model is trained on and is affecting your model performance).

Azure Machine Learning can be used to trigger a re-deployment when your Git repository has a new code check-in. Azure Machine Learning can also be used to create a re-training pipeline to take new training data as input to make an updated model. Additionally, Azure Machine Learning provides alerts and log analytics to monitor and govern the containers used for deployment with a drag-drop graphical user interface to simplify the model development phase.

Start building today!

SNP is excited to bring you machine learning and AI capabilities to help you accelerate your machine learning lifecycle, from new productivity experiences that make machine learning accessible to all skill levels, to robust MLOps and enterprise-grade security, built on an open and trusted platform helping you drive business transformation with AI. Contact SNP here.

Makarand P Batchu
Author:
Makarand P Batchu

Makarand Batchu is a Consultant in Advanced Analytics at SNP Technologies. His role involves gathering and analysing data, using various types of analytics and data visualization tools to help customers achieve effective operational goals.