How Enterprise Organizations Get Value from Data Science/ Machine Learning
So you deployed your first ML model in production. You might feel underwhelmed by such a basic initiative. So what’s next?
Congratulations! You are an Enterprise organization that has taken the crucial first step in the implementation of Data Science and Machine Learning (DS/ML). You’ve successfully deployed a basic ML project in your production environment, and that in itself is no small feat. You’ve dedicated time and resources to build a Proof of Concept, secure an Authority to Operate, allocate budget, onboard ML engineers, procure hardware, set up environments, and connect your DS/ML cluster/services to data sources. Now, it’s time to look forward to your next steps.
Perhaps you’re contemplating the impact of your initial ML project, thinking that there must be more significant strides to be made in your organization’s journey into the realm of data science. The good news is, you’re on the right track, and there are several key steps you can take to continue growing and enhancing your organization’s data science capabilities:
Step 1- Build Reusable Pipelines: Your first project’s success provides you with a strong foundation to build upon. One of the next logical steps is to create reusable pipelines. These pipelines consist of a feature pipeline, an ML training pipeline, and an inference pipeline. Developing these pipelines allows you to streamline the process for future ML projects. Instead of reinventing the wheel each time, you can leverage these pipelines to expedite the development and deployment of new models.
Step 2- Assemble an MLOps Team: Scaling up your DS/ML initiatives requires more than just pipelines. You need a dedicated team to manage and maintain your machine learning operations (MLOps). This team should consist of experts who understand the intricacies of deploying and managing ML models in production. Their role will be to ensure the reliability, scalability, and performance of your DS/ML solutions.
Step 3- Scaling for Multiple Enterprise ML Solutions: With your MLOps team in place, you can start thinking about deploying multiple Enterprise ML solutions in production. This is where your organization’s data science capabilities truly begin to mature. Each new project builds on the experience and knowledge gained from the previous ones, allowing you to tackle increasingly complex challenges.
Step 4- Vision and Leadership: It’s essential to have a clear vision and strategy for your organization’s data science journey. This vision should be communicated to directors and executives, as their support and understanding are critical to the success of your DS/ML initiatives. A technical AI Strategist plays a vital role in advising and aligning the organization’s leadership with the technical aspects of DS/ML. Usually, an AI Strategist is brought in after an organization passes the phase of just exploring DS/ML (prototyping phase) to the phase of wanting to mature DS/ML for the Enterprise (operationalization phase).
Step 5- Continuous Learning and Innovation: Keep in mind that the field of data science and machine learning is rapidly evolving. To stay competitive and extract maximum value, your organization should foster a culture of continuous learning and innovation. Encourage your team to stay updated with the latest advancements, and be open to experimenting with new techniques and technologies.
In conclusion, your successful foray into the world of Data Science and Machine Learning is a significant milestone. You’ve laid the groundwork for a more mature and impactful data science capacity in your organization. Building reusable pipelines, establishing an MLOps team, and scaling for multiple enterprise ML solutions are all key steps in this journey. With the right vision, leadership, and commitment to ongoing learning and innovation, your organization can continue to extract value from DS/ML, drive innovation, and remain competitive in a data-driven world.
If you're looking for support from me, here are a few options:
Enterprise Data Science Consultancy: With my consult team comprised of a Senior Data Scientist, Senior ML Engineer, Senior Data Engineer, and Senior Cloud Engineer, we will help you architect and build your Enterprise Data Science platform, and transfer knowledge to your IT team to maintain and optimize it. We will also overlay an MLOps framework to manage the AI solutions you build on this platform. If you don’t have an MLOps team, we will help you build one. Please get in touch about this consultancy here
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