How many jobs is this job description?
What are your opinions on machine learning platforms like AWS SageMaker and Azure Machine Learning?
[D] What do you guys think of MS Azure Machine Learning Studio? It's a browser-based ML enviroment.
It's fine for a particular type of need, aka: make a trained model and put it in production fast, keep it alive for a long time, and make ongoing predictions for a that long time easily (with API calls).
It's not great at: most of the parts of prototyping except wide model comparisons on a global view (but even then comes without automated tuning).
My overall feeling is: If you want to quickly test a ML driven product idea, Azure ML is a great way to get the product viability results you need fast. Prototype in whatever it is you prototype in, with focus on Azure ML models first. If these prototypes are good and meet your standards, productionalize in Azure ML and get that product into the hands of your customers fast.
More on reddit.com[D] Azure Machine Learning Model Management
I am a novice attempting to enter ML consulting and currently self learning. I haven't actively developed in 5+ years and got a simple model endpoint exposed to web on AWS in about 6 hours.
After training a model with AWS Sagemaker I deploy a Sagemaker endpoint. You could import your model to Sagemaker.
Then you use AWS Lambda Function to call the model prediction and output JSON and use the AWS API Gateway as a trigger for the Lambda function to expose it to web. I have gotten a very basic example of this working in practice.
Finally I hired an experienced web developer to make me a "starter" web app as a template for me to wrap web app interface around these ML model endpoints to take inputs and display predictions.
I am all-in on learning SageMaker right now but from initial docs it seems like I could bring in my own models.
My current planned full stack and consulting workflow is as follows and would welcome any feedback as well.
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Review - analyze - clean data using KNIME
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Upload clean data to AWS S3 bucket
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Use AWS Sagemaker API to train models and deploy endpoints
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Wrap model endpoint in Lambda function and expose to web via API Gateway
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Build a simple web app around the web API call or just provide the API documentation depending on the client and if they can integrate themselves or need a user interface.
What will I learn from this course?
When will I have access to the lectures and assignments?
How long will it take to complete this course?
Videos
“Please see below for the JD.
Infrastructure & Cloud Engineering
Direct the design, implementation, and optimization of hybrid infrastructure environments spanning on-premises systems and Azure cloud platforms.
Drive the adoption and integration of Azure AI services, including Azure Machine Learning, Cognitive Services, and AI-powered analytics solutions.
Ensure enterprise systems, networks, and data platforms meet high standards for availability, performance, and scalability.
Partner with software engineering teams to ensure infrastructure readiness, seamless CI/CD pipeline integration, and adherence to DevOps best practices.
Cybersecurity & Risk Management
Own and evolve the enterprise cybersecurity strategy in alignment with technology leadership.
Develop and maintain comprehensive security frameworks, incident response processes, and compliance programs (e.g., NIST, HIPAA, CIS, NYDFS).
Oversee proactive risk monitoring and mitigation efforts related to data protection, access control, and threat detection across all digital assets.
Help Desk & End-User Support
Lead Help Desk and desktop support functions to deliver exceptional service and technical assistance to all employees”
Just curious if you see 1 job here or many. I was offered this recently. Company is quite large, maybe over 1k employees. Seems like at least 2 jobs from my perspective.