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We want to understand your business needs.
When it comes to bringing useful AI solutions to life, Lemay.ai has the experience to ensure that systems are aligned with project goals and adhere to agreed performance standards.
We stay in close communication with you throughout the project, so if business conditions change, or new situations arise, you know as soon as we do. It’s this philosophy, combined with our expertise, that has turned so many of our clients into repeat clients.
While AI itself doesn’t have scaling issues, the way the AI is implemented can. These issues have to be planned for and addressed in advance in order to ensure a usable system.
For example, image files can be large, and when thousands are sent in from remote cameras, it takes too long to send them to the server, digest the image, to put them into the machine learning model, and wait for it to analyze and return the results.
To prevent this situation, we designed an AI system for one client that ran continuously, and all the client needed to do is retrieve the latest results. That change alone (even without the others that we made) led to a 3X improvement in performance. Additional improvements, like using better libraries, resulted in a 10X improvement in performance.
Cloud infrastructure offers a variety of benefits with AI deployments: no physical equipment to manage, easy scaling, and almost no concerns about downtime. There are a number of excellent providers like Amazon Web Services (AWS), IBM Cloud, Google Cloud, and Microsoft Azure.
But costs can add up without the right cloud management approach. There can also be load balancing (how processing is distributed across computing resources) and latency (delay between sending and receiving information) issues.
Data governance or internet access in rural areas may be considerations that rule out cloud deployments. We provide on-premise deployments when needed.
Parameters for usage can change over time, and with exposure to new sources of information. If required, we can make adjustments to how the system interacts with new data created by users and other data providers.
We build to ensure future growth in many dimensions, making sure it’s not only expandable in terms of abilities, but it can also scale to serve millions of users.
In a way, deployment is just the beginning.
Machine learning DevOps (development + operations) is a cycle of ongoing monitoring and improvement aimed at providing security, upgrades based on user feedback, and ultimately value. Ongoing scaling is a typical consideration, as is retraining as requirements grow.
While our training sessions help your teams understand, maintain, and grow your system, we can also provide ongoing support and upgrades. Many of our clients enjoy the continuous improvements we can deliver with ongoing contracts.
The costs of capturing, processing, and storing large volumes of data can grow as the dataset grows. Initial discussions about budgetary considerations will help us plan the right system for your needs over time.
Lemay.ai delivers solutions that not only work, but that can also be expanded. Part of this process involves a detailed knowledge transfer session with every client, to ensure that they have the tools at their disposal to own their own solution.
Following our solution‘s initial handover, and depending on your organization‘s needs, we perform periodic informal check-ins with your stakeholders and technical team to verify the ongoing success the solution provided, and that the team is working effectively with the solution.
Lemay.ai will deliver a solution that not only works, but can be further developed. Not only do we follow up with you once your system is deployed, we work with you on an ongoing basis to ensure good performance.
Detailed knowledge transfer ensures your team has the tools they need to continue working with your system.
After the deployment of the solution, we complete a final report containing the solution architecture, design rationale, and performance results as measured by the success metrics.
