The Deployment Gap in Machine Learning Solutions is Addressed with MLOps
It’s well known that 80% of ML models trained by data scientists are not deployed in production. MLOps solves this problem, with a proper Enterprise Data Science platform.
Enterprise organizations that are AI-naive often start their journey to build data science capacity by hiring a platoon of data scientists. They focus on the organizations’ mission, use cases, domain data and domain experts to carry out their data science research and build multiple AI prototypes to get buy-in for AI/ML solutions to their managers and executives.
But when you are an AI-naive organization, your data science stack on prem is usually not a stack at all- you might be able to get R and Python on prem, and may also be able to get cloud sandboxes to do your experimentation and exploration.
So you have all of these data scientists in your organization performing AI research, and training AI/ML models. But when you’re an AI-naive organization, you don’t have the environment, processes, governance, and infrastructure in place to deploy your AI/ML models in production. In other words, your AI/ML models never make it out of your laptop or desktop- your local environment. At best, you have a local AI application that does not add business value, as you can’t scale it to the Enterprise. Some people decide the answer is to buy more tools and utilize SaaS to build the AI application to deploy. But try as much as you want from your silo, your only contribution to the Enterprise is more technology sprawl. Exasperated as a data scientist, you leave your organization for greener pastures, and your AI/ML models and artifacts remain on your laptop, collecting dust instead of ROI.
This gulf between ML model development and ML model deployment remains a significant challenge at many enterprises. The solution to this machine learning deployment gap is MLOps.
This dilemma of ML models constrained to local environments has prompted the evolution of MLOps, a set of practices and technologies aimed at bridging this deployment gap. However, for MLOps to succeed, organizations require a robust Enterprise Data Science platform to serve as the infrastructure to roll out these practices effectively.
Let’s look at some of the factors of this deployment gap. Data scientists are skilled at designing, training, and fine-tuning machine learning models that can extract valuable insights from data. Yet, their expertise often falls short when it comes to transitioning these models into real-world applications. Several factors contribute to this deployment gap:
1. Lack of Integration: Data scientists and IT operations teams often work in silos, with little collaboration between them. This separation leads to models that aren’t seamlessly integrated into existing systems.
2. Scalability Issues: Models developed in isolated environments may not scale effectively when deployed in production. This can lead to performance bottlenecks and inefficiencies.
3. Maintenance Challenges: Ongoing model maintenance, monitoring, and updates are critical for successful deployment. Many organizations struggle with this aspect of ML model management.
The challenges of deploying machine learning models can be effectively addressed with MLOps. It combines the principles of DevOps with specialized practices tailored to machine learning workflows. MLOps addresses the deployment gap by:
1. Streamlining Collaboration: MLOps encourages collaboration between data scientists, data engineers, ML engineers, and IT operations. It fosters a culture of teamwork to ensure that models are production-ready.
2. Automating Processes: Automation is at the heart of MLOps. It automates model deployment, scaling, and monitoring, reducing the manual effort required to transition models to production.
3. Continuous Improvement: MLOps incorporates continuous integration, continuous deployment, continuous monitoring, and continuous training (CI/CD/CM/CT) practices to ensure that models remain updated, perform optimally, adapt to changing data, and adapt to changing concepts.
In addition to MLOps, addressing the deployment gap requires an Enterprise Data Science platform. MLOps, while promising, requires a solid foundation to be truly effective. An Enterprise Data Science platform serves as this foundation, offering the following key benefits:
1. Centralized Infrastructure: It provides a centralized environment where data scientists can develop and test their models in a production-like setting. This streamlines the transition to deployment.
2. Scalability and Resource Management: An Enterprise Data Science platform ensures that resources are allocated efficiently, enabling the scaling of models when needed.
3. Version Control and Collaboration: It offers version control and collaboration features, facilitating teamwork and tracking changes made to ML models.
4. ML Model Lifecycle Management: These platforms assist in the end-to-end management of the ML model lifecycle, from development to deployment, maintenance, and eventual retirement.
5. Security and Compliance: Ensuring data security, ML model security, and compliance is crucial. Enterprise Data Science platforms often have built-in security measures to protect sensitive data, prevent malware in the ML models, and ensure regulatory compliance.
In conclusion, the deployment gap in machine learning is a persistent issue that organizations face, leading to a considerable waste of resources and untapped potential. MLOps, with its focus on collaboration, automation, and continuous improvement, holds the key to closing this gap. However, for MLOps to be successful, an Enterprise Data Science platform is essential. This infrastructure empowers organizations to streamline the transition from model development to production, ultimately unleashing the full potential of their data science initiatives. By investing in an Enterprise Data Science platform, organizations can significantly improve their ability to deploy machine learning models and unlock the value hidden within their data.
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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