When it comes to machine learning models, there are many differences between creation environments (where the model was built), and production environments (where the model will be used, monitored and have it's life cycle managed). The creation environment is oriented towards a specific set of people working on the model, with specific system, data and outputs configurations. But the production environment may be quite different - with other people, systems and requirements applied to the model. Understanding these differences allows organizations to be efficient in both environments, and know how to best navigate for the full life cycle of their critical machine learning assets. Let’s take a deeper look at the differences between creation and production environment in order to increase the effectiveness of our deployment process.
Model assessment and model traceability are two crucial steps when deploying a machine learning model. Model assessment ensures that the model is running accurately and efficiently, while model traceability deals with the history of the machine learning model. These two components are critical for deployment, but how can we make sure we are successful in these areas? Today we are going to dive into model assessment and model traceability, and discuss the importance of these processes.
Deploying machine learning models is often a bottle neck in realizing the value from data science investments. Utilizing the cloud, combined with a microservices based infrastructure, to deploy machine learning models can make the process less complex, and make life easier for everyone involved. Let’s look into how analytic migration to the cloud can help the data science and IT teams specifically:
In one of our past blogs, we discussed the importance of the cloud and its many benefits, including better security, more storage, increased collaboration, cost effectiveness, and redundancy. Because the cloud has been a topic of much discussion in the past few years, most people understand these benefits of utilizing the cloud in their organization. Now we want to dive in a little deeper to determine why the cloud is so important specifically for deploying machine learning models and analytics.
In our previous blog we discussed how to deploy machine learning models into production successfully and efficiently. Getting models into production can be difficult, but that isn’t the only challenge you will face with machine learning models throughout their lifetime. Once the model has made it into production, it must be monitored in order to ensure that everything is working properly. There are many different roles involved in the getting the model into production. Similarly, the monitoring of each machine learning model requires the attention from many different perspectives to ensure that each aspect of the model is running accurately and efficiently. Let’s take a closer look into the different perspectives we must consider when monitoring machine learning models, and why each is so important:
Deploying analytic models into production can often prove to be a difficult and tedious process. In an ideal world, data scientists create a model, they hand it off to IT, and IT puts that model into the production environment. Seems simple enough, right? However, as many data science and IT teams know, there are many complications that can turn this process from a simple one, to a highly complex back and forth.
Machine learning has changed the way we leverage and apply analytic models, and it isn’t going away anytime soon. As more and more organizations bring machine learning into their analytic portfolio, benefits are becoming clearer. Machine learning increases efficiencies in many applications once it’s integrated into an organization’s infrastructure, but getting to that point comes with many challenges. Some challenges of incorporating machine learning into your company’s infrastructure can be technical, while others are strategic.
Today data scientists use more tools than ever when creating and deploying analytic models. According to Rexer Analytics, the typical data scientist uses an average of 5 tools in their daily job. This variety of tools can provide difficulties for IT later on when trying to deploy the model, especially if the organization uses a monolithic platform that locks the company into a set coding language. Today’s data science tooling was not built for large scale deployment enterprise systems, which is why many organizations are switching to a microservices based architecture.
Machine learning models have the ability to provide tremendous amounts of value to the companies that utilize them. Models that lead to business and market insights can be an important differentiator for organizations, and can end up being a strategic advantage for the entire business. Although the value added can be significant, model deployment is a process with many moving parts, including tracking large volumes of machine learning models, managing data science language packages and assets, monitoring the models’ data for training and production, and tracking organizational issues like permissions on each model. This complexity leads to a new trend: implementing a model management strategy. Model management systems are used to track each model and its assets. A well thought out approach to model management allows organizations to fully leverage their models and differentiate themselves from competitors. Let’s look into the specific ways that an approach to model management can help you keep your deployment process efficient and organized, while saving you valuable time.