Why: AI Infrastructure Review
Hardware and Cloud Infrastructure
Assess the hardware and cloud infrastructure supporting the MLOps processes, including compute instances, storage solutions, and networking components. Assess the scalability of the infrastructure to handle varying workloads and data sizes.
DevOps and CI/CD Practices
Examine the CI/CD pipeline used for model development, testing, and deployment. Review version control practices, automated testing, and integrations with deployment tools.
Data Preprocessing and Feature Engineering
Review the data preprocessing and feature engineering pipelines used for data preparation before model training. Evaluating the data cleansing, normalization, and feature extraction techniques employed.
Monitoring and Logging
Evaluate the monitoring and logging mechanisms in place to track the health and performance of MLOps infrastructure components. Identify any potential bottlenecks or issues affecting system performance.
Data Ingestion and Integrations
Analyze the data ingestion process, including data sources, data connectors, data transformation steps. Evaluate the data integrations and sychronization capabilities with external systems.
Data Storage and Security
Assess the data storage solutions used for storing both raw and processed data. Evaluate the data security measures in place to protect sensitive information.
Testimonials: What Our Clients Are Saying
Without their help, we may have gone out of business. We saw a 1,000% increase over the maximum capacity of our legacy systems, over 48,000 orders per day, and an average of 75 million page views and 2.3 million unique users each month in 2017.
Asif is a brilliant computer scientist, a physicist, an original thinker, an erudite scholar, and an artist who can sculpt new algorithms, and paint new solutions, veritably a Leonardo da Vinci of our times, and a passionate educator with deep insight into AI/ML/DL/Data-Science/Big-Data topics.
When it comes to Big-data and machine learning, I would simply say he (Asif) knows A-Z in that. No matter what the domain is, he has machine-learning-based solutions for the problems involved. Working with him for years, I have realized how his predictions on the machine learning revolution in big data have come true.
We’ve done development in the past..the SolutionMap is a higher level way of defining a comprehensive approach to getting the success that you want.
It was not just about software…it was all about what is this going to do in your company, how are the people in your organization with your partners and with your other stakeholders going to interact with this piece of software and what are their expectations of it…it also helped us on the backside thinking about our business processes…it was really illuminating to me.
From the design through to the development detail, they listened, understood, and carefully tested before release, often pushing our initial ideas to a better outcome.
