Empower Your Data Science Teams
with Our MLOps Architecture

Qbiz Machine Learning Enablement

The Problem

The efficient deployment, management, and monitoring of data science models present a challenging task for many data science teams. Without an optimized framework, the gap between the value that data science can unlock and the ability to deliver tangible results can hinder business progress.

The Impact

The absence of a streamlined process for developing, deploying, and maintaining machine learning models ultimately impacts the company’s competitive advantage:  

Less value drawn relative to what is possible. 
Data teams produce fewer actionable models as these take longer to deploy
Ultimately this impacts the company’s competitive advantage

Key points include:

Lower data science model production and reach:

Yearly data science model throughput is low due to lengthier deployment cycles. Fewer business problems are tackled.

Models have a shorter lifespan:

Businesses reap benefits from data science models for a shorter span of time, as these become stale or irrelevant at a faster rate due to the absence of model monitoring practices and proactive refactoring.

Misleading model results:

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COMMON STRUGGLES

Data Science teams often face a recurring motif of struggles from the lack of robust MLOps infrastructure and development practices being in place.

Diverse data
sources:

Companies collect and manage data from a variety of software and mobile applications. This can result in data scientists spending time rationalizing and transforming data on-the-fly, which can lead to data reconciliation headaches.

Inadequate Feature
Management:

Scripts and workflows that don’t follow standardized practices often include overlapping and/or skewed feature engineering principles. This aects the reusability of codebases and draws out development cycles.

Lack of Standardized
Model Development:

Data scientists will often use varying methods for experimentation design as well as their own coding practices and style guides. These unregulated practices create a lot of unwanted friction in a production environment.

Lack of Automation in
Model Deployment:

Without a streamlined process that promotes models to production, this task can take far longer to perform and introduces a large amount of entropy.

Lack of Model
Monitoring:

The relationship between model features and target variables, as well as the distributions of features are likely to shift over time (model and data drifts), worsening model performance and in some cases, making them entirely unusable. Without model monitoring in place, companies can’t remediate these issues in a timely manner.

SOLUTION DETAILS

Qbiz is committed to helping organizations overcome these challenges by deploying the Qbiz Machine Learning Enablement solution - the perfect blend of machine learning, software engineering, and operations principles. Our solution is comprised of:

Feature store:

We help you establish a centralized feature repository. We aid in designing feature entities, defining relationships between them, aligning them to business objectives and ensuring their compliance and real-time availability.

Develop and Deploy:

We streamline everything from model selection, training, and evaluation to model deployment, implementing robust automation processes that allow you to scale.

Model Monitoring:

We help monitor productionized machine learning models, to ensure the timely resolution of drifts, performance drops and other issues.

Utilities and Code standards:

We build reusable utilities that scientist teams leverage to increase delivery velocity and mitigate duplicative and redundant work. Our best practices on codebase standards and organization improve the eciency of teams.

Model success criteria:

We help your team define success criteria thresholds for data science models, allowing teams to confidently recommend these for productionization purposes.

Model feasibility assessment:

We use various techniques to front-load the vetting of a model use case with o-the-shelf and automated machine learning models.

Business use case:

We help you operationalize predictive model outcomes, integrating them to critical business processes and decision making.

Roles and responsibilities:

We define standards around roles, responsibilities and artifact ownership which demarcates boundaries and reduces ambiguity.

BESPOKE QBIZ APPROACH

At Qbiz, we adopt a comprehensive approach towards Data Science Enablement:

Understanding Requirements: After understanding your needs, existing infrastructure, budget, and team expertise, we help you choose the right tooling, platform and project plan.

Data Integration: We ensure a seamless integration of your existing data sources with the MLOps platform. This involves connecting data repositories and implementing a foundational bedrock that ensures high quality training data

Feature Store: We help you establish a centralized repository for the features used in your machine learning models. This includes pipelines that transform raw data into feature values, track feature versions and keep training data and production data aligned.

Model Prototyping and Model Selection: Our balanced model prototyping approach leverages a mix of classical and cutting-edge model libraries to quickly short-list the best model candidates, weighing in various performance metrics as well as the expected LOE to productionize.

Model Deployment: Our team’s Machine Learning Engineers (MLEs) build and deploy robust ML pipelines given their deep exposure in both data science and platform engineering. We document all end-to-end processes.

Model monitoring: Our team sets up a rigorous model monitoring framework to track changes in model behavior and alert relevant team members when necessary. We balance threshold sensitivities to detect sudden changes (e.g. a feature enhancement in production breaks a model) or gradual changes (model / data drift). We also set up default resolution pathways.

Training and Onboarding: We provide detailed training and onboarding sessions, making sure your team is set up for success to fully own the MLOps platform moving forward.

PROVEN EXPERTISE

Qbiz has a track record of success, evidenced by the significant milestones we helped one of the world’s largest apparel companies achieve.

Problem

One of the key challenges the apparel company faced was ensuring the accuracy and consistency of their data across multiple data science teams.

Solution

The Qbiz MLE solution was to engage in rigorous data validation processes, working closely with their teams to maintain the quality of their data, including:

Transitioning cloud services from AWS to GCP to optimize costs.
Providing visibility into feature lineage to facilitate better decision making.
Leveraged Containerization and deployment strategies across multiple regions for High Availability.
Refactored code to make use of highly-scalable platforms.
Designed coding templates for code standardization and unit testing.
Helped build a standardized feature store.
Leveraged workflow management tools to scale-out repetitive (serial) operations.
Designed libraries for code reuse.
Leveraged Open Source data profilers to bootstrap data quality rules for monitoring predictions against thresholds.
Trained data scientists on highly scalable solutions to maximize velocity and reduce costs.
Added streaming data for near real-time forecasting of holiday sales.

The Qbiz Machine Learning Enablement solution resulted in significant improvements in model performance, cost-effectiveness, and data reliability at the apparel company.

Results and Future Direction

WANT TO EMPOWER YOUR DATA
SCIENCE TEAMS?