MLOps

Keep your ML applications aligned with your goals​

Looking to incorporate AI/ML into your operations? You’re not alone. Until recently, machine learning systems that worked well were few and far between. Now, most, if not all, of the technical challenges involved in building and deploying ML-based applications now have proven solutions.

Incorporating MLOps (ML + DevOps) increases workflow efficiency, as well as the quality and consistency of Machine Learning solutions. SPAR provides all the necessary technical expertise to help you deploy ML-based processes and keep them productive through our MLOps solutions. Our Machine Learning team extends from data engineers to data scientists, from DevOps and ML Engineers.

More Deployments, done faster.

Getting a machine learning model up and running is a great start, but it needs to be managed to ensure it reflects changing trends and business objectives. This means continuous model training and monitoring the data and model for drift. This only happens when the entire lifecycle is managed. And that’s what SPAR does. Our MLOps expertise applies to the entire lifecycle – from integrating with model generation through model registry, versioning, orchestration, and deployment, to health, diagnostics, governance, and business metrics.

How SPAR’s MLOps helps your business

This can be as simple as reducing fraudulent transactions or as complex as building a medical diagnostic system that can detect cancer from images or scans.

We leverage our extensive data engineering
expertise to:

  • Search for relevant datasets
  • Verify the data and source and make it accessible
  • Validate compliances with requisite regulations (like GDPR)

From design to coding — that feeds clean and compatible data to the ongoing phases of model development

by selecting the correct cloud services and architecture that is both performant and cost-effective

This is an iterative phase where we select the best solution using qualitative and quantitative measures like precision, accuracy, and recall.

We build pipelines that factor in aspects like system requirements, cloud architecture, training and testing, data validation, etc.

This could be a static deployment (when the model is installed as part of application software), or dynamic deployment (using web frameworks that provide API endpoints to respond to users).

such as Automated Release Management, streamlines the entire CI/CD pipeline, enabling increased (and automated) deployments, shorter lead times and faster innovation.

We will help you set up monitoring to facilitate model performance and governance to ensure the models deliver on objectives of all users be it stakeholders, customers, or employees. This step includes:

  • Tracking performance to flag performance degradation
  • Setting up evaluation metrics and establishing logging strategies
  • Troubleshooting—in the event of system failures
  • Tuning model performance

Why SPAR Solutions?

Technical expertise

Our MLOps practice brings a full ecosystem of expertise to every project, that includes ML engineers, Data Scientists, DevOps engineers

Methodolog

We take a collaborative approach, listening to our clients and working collaboratively to define processes and execute implementations that bring tangible and sustainable results

Collaborative-work approach

Our systems and processes are designed to engender teamwork, and so are our people

What to expect from SPAR

Increased productivity

through systematic collaboration and standardized workflows

Better Monitoring

that creates better insights about performance to help continuous retraining, flag model drift, and ensure compliance

Automation

that reduces manual management, frees up resources, and allows reproducibility.

Coding automatiom

that makes data science work easier and saves time and money

Reduced deployment time and cost

Trusted Across Industries

Why SPAR Solutions?

  • Technical expertise: Our MLOps practice brings a full ecosystem of expertise to every project, that includes ML engineers, Data Scientists, DevOps engineers
  • Methodology: We take a collaborative approach, listening to our clients and working collaboratively to define processes and execute implementations that bring tangible and sustainable results
  • Collaborative-work approach: Our systems and processes are designed to engender teamwork, and so are our people

Trusted Across Industries