In our list of articles, where we discuss the infrastructure of UnifyAI and dig down into the necessity and significance of every component of it, we got to know about UnifyAI’s data aggregator, feature store and model integration and development toolkit. which are aligned with unifyAI infrastructure to play a vital role in ensuring smooth data integration, seamless data supply and accurate and easy model-building procedures to further saving and orchestrate multiple models for multiple AI and ML use cases.
Here UnifyAI’s data aggregator and feature store ensures a streamlined data flow throughout the entire machine-learning pipeline, After fixing the data flow model integration and development toolkit is used to build models that can understand the pattern of the data and make predictions based on the data understanding. With the combination of these three components, data can be converted into useful information. But making this information useful in the real world requires more external effects. Here a centralized place is required to save the trained models and as a result, a model repository comes into the picture. Let’s understand what is a model repository.
What is a Model Repository?
In real-life scenarios, it has become increasingly common to employ multiple models to address a single AI and ML use case. This practice is often referred to as the experimentation phase. During this phase, various models are developed and tested to identify the most effective one for the specific use case. This is how multiple models for a single use case come into the picture and managing these models becomes more challenging as saving the learning of every crucial trained and tested model is difficult as well.
A model repository addresses the difficulty of saving the knowledge gained by trained and validated models. In AI and ML workflows a model repository stores the artifacts of models and allows users to manage these artifacts. However, when tasked with managing multiple models for one or more applications, several challenges arise. Here are a couple of these challenges related to model management.
Challenges of Model Management
Upon completing the model development and evaluation using testing or validation data, we often encounter multiple metrics to assess the model’s performance. It is not uncommon to observe that different models perform well with different subsets of data for a similar task. In such scenarios, we end up with multiple models, and preserving and reusing their capabilities becomes essential. There are multiple such challenges which generally occur with AI and ML workflows especially when the number of developed models for a use case is high, let’s take a look at them one by one.
- Difficult Model Management: In the process of addressing various use cases using machine learning and AI models, data scientists develop a multitude of models to thoroughly evaluate their performance, aiming to attain the best possible outcomes. This often leads to the selection of numerous models for a single use case. Consequently, effective management of these models becomes essential, so that models can be used appropriately when needed.
As mentioned earlier, a single use case might involve multiple models. In traditional approaches, storing these models often demands the creation of specific model directories containing multiple files. This can lead to the necessity of building multiple directories for managing multiple models, creating a significant obstacle in the path of transitioning machine learning models to production. - Difficult Model Integration: In conventional AI and ML workflows, incorporating pre-built models can pose challenges due to the varying internal structures of models. These structures are shaped by the unique model-building processes, which can differ across different models.
- Difficult Model Reproducibility and Version Control: When a practitioner creates an AI or ML model, he goes through various steps such as data processing, model hyperparameter tunning, cross-validation, model training and testing. During the experimentation phase, it becomes difficult to Reproduce results or track changes.
- Limited collaboration: Collaborative ML projects require team members to share code, data, and model weights manually. This approach is prone to errors, and version mismatches, and hinders real-time collaboration.
- Repetitive development procedure: Building machine learning models from scratch takes a lot of time and computer power. So, it’s really important to store and organize these models properly after they’re developed. This way, we can avoid making the same models again in the future. If we don’t do this well, it can slow down projects and make it harder to come up with new ideas.
These are the major points which establish model repository as a need in AI and ML workflows, as it efficiently stores and manages the artifacts of developed models. The repository acts as a centralised storage solution, allowing users to save their models systematically and seamlessly. It reduces the efforts of implementing ad-hoc solutions, and manual processes, that rely heavily on communication between sparse components to ensure that models and other components workflows are properly managed and maintained.
A model repository in AI and ML workflows not only holds the model artifacts but also lets the user manage, maintain and use them when needed.
Our AI platform UnifyAI is built with a powerful model repository to save, reuse, and efficiently manage model artifacts. This user-friendly model repository empowers UnifyAI infrastructure to provide a seamless transition of AI and ML models from experimentation phases to deployment in production phases. Let’s take a look at the advantages of the UnifyAI Model repository in the next section and understand how this model repository fulfils the above-discussed needs and challenges while providing numerous benefits to enhance the AI and ML workflow efficiency.
UnifyAI’s Model Repository
In the preceding sections, we gained insights into the challenges with model management in ML workflows which need to be mitigated. Here model repositories are acknowledged as essential components, addressing challenges and enhancing the efficiency of AI endeavors.
UnifyAI’s Model Repository stands out by not only effectively resolving prevalent issues but also incorporating a range of additional features that contribute to a smoother and more dependable ML workflow. This repository is integral to the UnifyAI framework, serving not only as a storage space for models but also elevating the overall reliability and model management capabilities of the UnifyAI ecosystem by facilitating its users with proper model management and maintenance.
Here is a glimpse of the UnifyAI Model Repository which provides a proper framework for model management and is an essential part of the UnifyAI Platform:
- Centralised Model Repository: UnifyAI incorporates an integrated Model Repository, which allows users to conveniently save and store their constructed model artifacts for future use. This repository serves as a centralized place, which also offers users the capability to view and explore stored models, facilitating efficient and easy model management.
- Seamless Model Registration: UnifyAI serves as an all-encompassing platform, offering not just a repository for storing model artifacts but also providing a comprehensive IDE for model development and the UnifyAI library using which users can register the model into the UnifyAI model repository very easily. This avoids the pain of building varied model Registration Approaches, and users can simply focus on model building and register models into the model repository.
- Easy Model Integration: UnifyAI’s model repository provides a space not just for ongoing experiments, but also takes care of preserving past experiments. It offers an incredibly simple method to store previously developed models so that in case transitioning into UnifyAI infrastructure doesn’t make the user restart the whole procedure. This is how it lets the users avoid the Repetitive development procedures and focus more on the new innovation and speedy progress of projects.
- Continuous experimentation: UnifyAI has its own IDE and library which can be used to build models and register them to its model repository. This IDE is similar to other Python and R IDE, which makes it easy for data scientists to create models by following general procedures and using the UnifyAI library and a few lines of code build models can easily registered in the UnifyAI repository. Since data scientists rigorously iterate between multiple models and methods, they can easily log their models, code and data knowledge into the model repository and perform continuous experimentation.
- Model, Code and Data Versioning: As we know that the models are built using different codes and data and the unifyAI model repository comes with the facility of storing the articfacts of models, versions of different codes and data used to build that specific model. Using this facility of this repository users can easily track the changes they performed during model development and use or share this knowledge to maintain appropriate collaboration while having control over the different versioning and reproducibility of models.
- Single Click Model Deployment: Models stored in UnifyAI’s model repository can be seamlessly deployed to production using the integrated components of the UnifyAI infrastructure. A key player in this process is the UnifyAI core engine that streamlines model deployment and simplifies it to a single-click action.
The mentioned benefits and characteristics are crucial for a model repository to be efficient, reliable, and successful in the context of the complete machine-learning lifecycle. Each of these aspects plays a significant role in ensuring that the process of developing, deploying, and managing machine learning models is smooth and effective.
UnifyAI’s Model repository serves as a pivotal component within the larger context of UnifyAI, transforming it into an end-to-end AI platform. This comprehensive platform offers a seamless, effective, efficient, and scalable solution to guide AI and ML use cases from experimentation to production. Let’s understand what is UnifyAI?
What is UnifyAI?
DSW’s UnifyAI is an end-to-end MLOps platform that combines all the necessary components for seamless AI/ML implementation. Eliminating disjointed tools and manual processes is one of the key features of UnifyAI. By combining data engineering, feature engineering, MLOps, model monitoring, and many other processes, it provides a unified and cohesive environment for end-to-end AI/ML development, right from experimentation to production.
Automation is a core feature of UnifyAI, reducing the time, cost, and effort required to experiment, build, and deploy AI models. UnifyAI reduces the time and effort required to build and deploy AI models. There are various other factors about UnifyAI that enhance the scalability of AI/ML use cases and allow enterprises and organisations to scale their AI initiatives across the organisation, from small-scale projects to large-scale deployments. UnifyAI provides the necessary infrastructure and computational power to handle diverse data sets and complex AI algorithms, ensuring that enterprises can effectively leverage the potential of AI at any scale.
About Data Science Wizards
DSW, specialising in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.
Our mission is to democratise AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.
Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.
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