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Let’s Get
Started.
Let’s talk about how SPAR Solutions
can positively impact your business.
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.
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:
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:
Our MLOps practice brings a full ecosystem of expertise to every project, that includes ML engineers, Data Scientists, DevOps engineers
We take a collaborative approach, listening to our clients and working collaboratively to define processes and execute implementations that bring tangible and sustainable results
Our systems and processes are designed to engender teamwork, and so are our people
through systematic collaboration and standardized workflows
that creates better insights about performance to help continuous retraining, flag model drift, and ensure compliance
that reduces manual management, frees up resources, and allows reproducibility.
that makes data science work easier and saves time and money