We’ll begin by taking the time to develop a deep understanding of your unique business model, strategic goals, and day-to-day challenges. We’ll then assess your related business processes to discover potential opportunities and identify problems that could be best solved by AI, and work to figure out their corresponding feasibility.

At this stage in the process, our Data & Solution Architects will do data due diligence to map use cases to available data. A careful audit of your data assets, data infrastructure, and data governance policies is done to identify any gaps that need to be filled before you can process and model data at scale.

An AI-ready dataset is one that is diverse and balanced. For instance, an image-based dataset needs to come with different backgrounds, lighting conditions, and a range of angles if you want to form a rich training dataset. We will collect data via existing data stores, pre-packaged sources, web scraping, or synthetic data creation.

Blackstraw has proven expertise in architecting data systems and pipelines that are scalable, secure, and guarantee high data availability. Data is ingested from hybrid data sources, then cleaned, processed, and stored centrally in a data lake. Our industry-leading data engineering capabilities will set up your AI project for unmatched success.

The success of an AI solution is contingent on building an accurate training data set, and the Blackstraw team has the necessary experience and proven tools to drive efficiencies in this time-consuming process.

Select the model framework and architecture and train the models through Machine Learning and Deep Learning techniques. This process involves providing a training algorithm with training data to learn from. Here training data along with labels/target values, learning algorithm finds patterns in data based on ground truth and these patterns will be saved in trained models.

Hyperparameters of the chosen models are iteratively tuned for optimal success. Model hyperparameters such as training steps, learning rate, initialization values and activation function, etc. are tuned to continuously establish a balance between overfitting and underfitting the model.

Simulations are run to evaluate the performance of the models to ensure they’re set up to meet your goals. Data Scientists select trained models and evaluate performance on unseen test data. We pull different metrics like Precision,Recall, F1-Score, Accuracy, Confusion matrix, etc to select the best model.

The prototype model is typically written using a statistical scripting language, and will now be ported into a production language for scaling, latency and efficiency. Our full-stack and mobile development teams leverage the latest technology stack to integrate the Inference model with your enterprise systems or develop applications ground up.

We adopt application security strategies such as “Shift-Left” at the very early stage of the software development life cycle.

We employ sophisticated strategies such as Infrastructure as a Code, Containers and Serverless technologies to ensure seamless deployment across various hosting platforms be it Cloud, On-prem or Hybrid.

CI/CD deployment integrates code frequently into a shared repository, whereupon automated tests verifies the security and quality of the build and release the code to production. The AI solution is now ready for real-time data!

The final ongoing step in the lifecycle of an AI solution is proactively tracking and resolving errors and exceptions in the production systems to meet business SLAs. This process, called “inaccuracy management,” is essential for success and requires experience to do correctly—which the Blackstraw team has in abundance.

Now that the AI implementation is live, the model is iteratively tuned and improved based on the inaccuracies that are being corrected. This feedback is used to enrich the Model training data set for continuous improvement of Inferencing. Based on a defined cadence, the improved models are pushed back into production via CI/CD pipeline.