Our Client’s Top 5 Questions On MLOps

AllCloud Blog:
Cloud Insights and Innovation

If you are among the 50% of companies planning to increase their machine learning & AI budgets, then you’ve probably got some questions about how to do it right.

You may also be among the 33% of companies who say that their data scientists spend more than half their time on ML deployment.

If so, then MLOps may be the solution you are looking for.

At AllCloud, we offer an MLOps solution that helps companies automate many of the daily activities of machine learning models, so that data scientists can focus on innovation. Businesses often turn to us to better understand what MLOps provides, whether it is cost-effective, and how to launch MLOps quickly and efficiently.

We’ve gathered the 5 most common questions our clients ask us about MLOps so you can launch into MLOps and reap the benefits too.

1. Is an MLOps solution costly?

To the contrary! When designed correctly, MLOps will in fact save you money. 

We’ve discussed before how MLOps can make your data scientists more efficient and productive. But what does that mean in practice? It means they won’t have to repeat the same operations numerous times, when one execution is enough. They won’t need to rerun a possibly costly training job because no model lineage was put in place. In addition to saving expensive manpower time, you are also saving on machine time!

Moreover, if you build your solution on AWS, you will benefit from the pay-as-you-go approach. No upfront investment and no unwelcome surprises when you need to scale your architecture.

2. What does MLOps demand from me?

Every organization has its own set of ML practices and methods. So of course, we will need to work closely with your data scientists and engineers to understand which workflows and infrastructure work for you.

But you won’t need any in-house MLOps knowledge. A key goal of AllCloud’s MLOps engagement is enabling you to get up and running and gain the skills to get the most from your Machine Learning activities.

3. Can I bring in my own models and technology? Or do I have to use your models?

You can bring your own code, your own libraries, and your own models. The solutions we build for you will be cloud-based, built on AWS, but can be fully customized to suit your own models. If you so choose, you can indeed leverage code and algorithms that AWS provides their users. But this is optional and completely up to you.

4. What are the benefits of Amazon SageMaker? Do I have to be using it already to leverage MLOps?

SageMaker is AWS’s managed service for machine learning. It offers a suite of tools to quickly and easily build, train and deploy machine learning models. It also offers all the capabilities needed to build MLOps pipelines. Moreover, SageMaker’s native integration with other AWS services allows you to build robust and complex systems that really take ML from the lab all the way to serving your applications and business.

Having said that, you don’t need any existing workload on SageMaker to get started with MLOps at AllCloud. We can seamlessly migrate your ML systems for you to SageMaker. 

To sum up: Is it possible to build everything you need without SageMaker? It’s possible. 

Will it be easier, faster and more efficient with SageMaker? Absolutely yes!

5. Do I have to be an AllCloud Engage customer to access MLOps? How can I get started?

No, you don’t need to be a member of AllCloud Engage to start benefiting from MLOps. 

If you’re interested in working with us, let’s talk! We can adapt MLOps to your specific requirement and to the scope of your project. Contact us to get started.

If you are an AllCloud Engage customer, the MLOps solution is available to you via AllCloud Solutions Factory. Your team can now start deploying it in your environments, or we can help you get it implemented.

Want to start unlocking the power of MLOps for your business?

Jonathan Chemama

AI Tech Lead

Read more posts by Jonathan Chemama