Why is Machine Learning Deployment Hard? After several AI projects, I realized that deploying Machine Learning (ML) mode

Metrics, logs, service discovery, distributed tracing, configuration and secret management, CI/CD, local development experience, auto-scaling on custom metrics are all things to take care of and make a decision. These are only some of the things that we are calling out. There are definitely more decisions to make and more infrastructure to set up. An important area is how are your developers going to work with Kubernetes resources and manifests — more on this later in this blog post.

Out-of-the-box Kubernetes is never enough, for almost anyone. It’s a great playground to learn and explore. But you are most likely going to need more infrastructural components on top and tie them well together as a solution for applications to make it more meaningful for your developers. Often this bundle of Kubernetes with additional infrastructural components and policies is called Internal Kubernetes Platform. This is as an extremely useful paradigm and there are several ways to extend Kubernetes.

Tech executives strongly believe in the power of AI as a whole, but that doesn’t mean that they’re convinced by every idea out there. As Algorithmia reports, a third of all business executives blame the poor deployment statistics on a lack of senior buy in.

Of course, that doesn’t mean that every data scientist suddenly needs an MBA to excel at their job. However, some key learnings from classes or business experience might serve them a long way.

In addition, some pipelines might make use of containerization with Docker and Kubernetes, others might not. Some pipelines will deploy specific APIs, others not. And the list goes on.

For one, the hardware or cloud storage space to handle bigger datasets might not be available. In addition, modularity of machine learning models doesn’t always work the same at large scales as it does on small ones.

Not only is this a waste of time and resources. It can also lead to additional confusion when stakeholders don’t know which version of the code to use, and who to turn to if they encounter any bugs.

It seems as if data scientists are still viewed as somewhat nerdy and devoid of business sense. This makes it all the more important that data scientists amp up their business skills and seek the dialog with senior execs whenever possible.

We finalized on Prometheus. Prometheus is almost a defacto metrics infrastructure today. CNCF and Kubernetes love it very much. It works really well within the Grafana ecosystem. And we love Grafana! Our only problem was that we were using InfluxDB. We have decided to migrate away from InfluxDB and totally commit to Prometheus.

Some pipelines start in Python, continue in R, and end in Julia. Others go the other way around, or use other languages entirely. Since each language comes with unique sets of libraries and dependencies, projects quickly get hard to keep track of.

It’s all the more important that as soon as a project is started, a benchmark is established against which the model runs now and in the future. In combination with diligent version control, data scientists can get their models reproducible.

Connected with the above issue is that there is, as of now, no go-to way of versioning machine learning models. It’s quite obvious that data scientists need to keep track of any changes they make, but that’s quite cumbersome these days.

Finally, data sourcing may not be easy or even possible. This can be due to silo-structures in companies, as discussed earlier, or due to other challenges in obtaining more data.

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just after 8 p.m. It was one of those summer evenings where the warmth of the day still hung in the air. He brought beers and I brought the blanket. I teased him about his haircut (he needed one) and he told me I smelled good (I did). When the beer cans were empty and there was no longer a need to be upright, he suggested we stargaze. We peered up and after a single quiet moment, realized there were no stars to gaze at and laughed in unison. Then he went in for a kiss.

Although data scientists have an advantage if they’re able to implement their models, they should clearly communicate with the engineers about what needs to be done by whom. This way, they’ll save the company’s time and resources.