Each and every enterprise wants to harvest the data with the notion of simplifying and advancing our lifestyles. Data Scientists are employed to engineer data and discover best-fitted machine learning models for every scenario, be it detecting fraud system, diagnosing and predicting health issues or recommendation services. As per the recent survey, Artificial Intelligence will be adding $15.7 trillion to the global economy by 2030. But still, almost 90% of the models that are built are not operationalized. As enterprises are learning the significance of AI changing the way of doing business, there are hurdles along the way causing churn in AI adoption.
Managing Software Engineering projects are straight-forward with the goal and success metrics defined before-hand. The same approach does not fit for Machine Learning projects as the goal is a derivative measure of the iterative experimentation phase. For any enterprise to pilot artificial intelligence with much ease and success mandates the necessity of data and domain knowledge along with the understanding of the hurdles in the ML lifecycle phases.
What are the hurdles in operationalizing ML Projects?
A well-defined ML project kickstarts its lifecycle from project initiation and planning phase followed by the iterative phase of building, deploying and monitoring ML models. There are different actors either playing single or multiple roles based on the size of the enterprise during the entire lifecycle of ML projects.
Project Initiation phase involves the business stakeholders setting the project’s objective, scope, and purpose to derive value add by modeling the data with Machine Learning models. This initial phase sets the tone of the ML project thereby aligning all stakeholders to work towards the common goal.
Project Planning involves the project management team along with business stakeholders strategizing and planning to build a cost-effective model within the constraints of resources and budget. In ML Projects, the planning phase is part of the iterative lifecycle where the experimentation of the ML model becomes the derivative measure for re-scoping and adjusting the minimum viable product (MVP) of ML projects. The back-and-forth nature of ML projects adjusting the MVP based on model experimentation requires a highly collaborative environment.
Do we have a unified service offerings wherein Data Scientists and Data Engineers can work alongside the management team to scope out the key performance indicators for the ML projects?
The build phase propagates from data collection and labelling followed by exploring the model. The model is refined continuously throughout the build phase, either by fine-tuning the hyperparameters or retraining the model with more realistic training sets.
The collaborative nature of ML projects requires data scientists, MLOps and management to work as one team. The heterogeneous collection of ML libraries, frameworks, and tools provides the freedom to choose any ML stack, but it also perplexes Data Scientists with the burden of experimenting with a wide variety of ML stack. With each team siloed along with the absence of a unified platform to build, deploy and monitor models makes ML lifecycle management more rigid.
Can we seamlessly integrate with most of the ML stack with the capabilities to deploy and monitor models from a unified service dashboard?
Once the models are built and refined, it is ready to be productionalized. Enterprises piloting in Artificial Intelligence would like to have a cloud infrastructure to play around with their models, whereas enterprises who are restricted by regulatory data look forward to on-premise deployment with an added layer of data security. In both cases, enterprises want to deploy models prudently with minimum cost and maintenance.
Do we have the means to solve the varying demands of model deployment based on enterprise size and maturity within the allotted budget?
Monitoring should be an integral part of the entire Machine Learning lifecycle starting from the build phase monitoring the data volume to runtime model insights. Monitoring without actionable alerts become redundant soon. The alerting mechanism should be configurable to alert on model degradation with respect to its performance and quality. The monitoring capabilities need to be extended to resource constraints like CPU and memory with an automatic scaling feature.
Do we have an integrated MLOps monitor service to monitor machine learning models to align with the intended KPI set in the planning phase?
How to solve the hurdles in operationalizing ML projects
Predera’s AIQ offers an integrated MLOps services guiding the Data Scientists to seamlessly experiment models built in any ML stack, enabling MLOps team to deploy to any cloud or on-prem in a single-click, visualizing the performance indicators of ML models powering business team to monitor the purpose and success of ML projects.
Let’s demonstrate Predera AIQ’s unified experience by sampling a machine learning project lifecycle.
Project initiation phase kicks off by setting the objective and scope during the project initiation phase
Business: Banking Sector – IDBI Bank
Objective: Increase Customer Base
Scope: Customers in Portugal
Project planning phase sets the measurable goal and MVP with business stakeholders and project management starts managing the ml project lifecycle
Goal: Increase Customer Base
MVP: Increase customer traffic through marketing campaign and measure success by building machine learning models
Participants: Business Stakeholders, Project Management Team
Predera AIQ offers seamless integration to multiple ml stacks by adding just 2 lines of code to your model along with version control. It provides a competitive edge by automatically logging the model metadata, artifact, feature significance, performance metrics along with model insights without the need to write extra code and thereby not polluting your workspace with extra code for just logging model data.
Predera AIQ offers User Management to manage the access control of users based on their role.
Setup the project space in Predera and add all the stakeholders
Build the ML model and Integrate with Predera AIQ by adding just 2 lines of code to the model
The ML project, idbi-bank-ml-project, is automatically recorded in the Predera dashboard which is accessible by all the participants
Experiments are version controlled with model artifact, metadata, training/test datasets logged automatically.
Predera offers a deploy tool set with single-click deployment to any infrastructure either on cloud or on-premise along with configurable resource allocation like TPU, CPU, and GPU based on the model’s resource constraint. Predera’s deploy service is independent of the build service thereby providing the flexibility to not only deploy models experimented via Predera’s build toolset but also deploy models from any given GitHub repository with proper access control. The deployment can be simple or distributed wherein the models are tested across multiple worker nodes. It also supports TensorFlow serving deployment, A/B test deployment and even complex graph deployment. Predera not only offers one-click deployment strategy, but it also provides cron-based and periodic deployment schedules.
Each deployed model in Predera monitors the runtime model score, data drift and notifies team either via email or slack.
It also provides prediction and feedback API both in REST and gRPC format. While Data Scientists monitors for model performance degradation, ML engineers are alerted on resource consumption like CPU and memory. Predera AIQ offers an integrated and unified experience to all the business stakeholders, project managers, data engineers, data scientists and MLOps team to work as one team by visualizing the progress of ML projects.
Monitor Performance Metrics & Resource Metrics (CPU/Memory)
As machine learning models are built to power our lifestyles, we are also deemed with ethical responsibility to approach artificial intelligence with social awareness.
Predera AIQ empowers enterprises of any size to grow their business employing artificial intelligence at ease.