Describe your project
Contact us directly to request our support in AI or HPC related projects.
Please prepare a brief description of your problems, challenges, plans, schedules and we will get back to you with our thoughts.
Let’s continue from there and explore how we can collaborate to help you reach your goals.
byteLAKE helps succeed with AI in many ways:
- 👩🏫👨🏫 Through AI Workshop we listen and do our best to understand the needs and goals of our clients and partners. Our experts help them ask proper questions, explain technologies that might be useful and even shadow their teams to better understand daily tasks. Then we assist them in preparing deployment plans and new technology roadmaps. We help understand which challenges could be addressed with existing technologies, which would potentially require additional research efforts, guide about best ways to transform early ideas into tangible results etc.
- 🤔👁 Proof of concept is a natural next step to demonstrate the first tangible benefits: process optimization, task automation, increased system reliability etc. This is also the stage where we help and guide our clients to prepare and collect the right data. Our experienced data scientists help process them and prepare for AI algorithms. Also, some of the key design decisions are taken here. All focused on delivering the most efficient solutions.
- 🧠💪 Solution delivery naturally follows and in our case this is done in Agile sprints, meaning you get results every 2 weeks. One thing worth mentioning here is that we have established a strong research practice for a reason. It happens that most of our clients come with requests where we hardly ever can build an AI system purely from ready-made components. Our projects very often are far more ambitious than what the off-the-shelf components have to offer. Besides, our experience in a very closely related area called HPC (High-Performance Computing) helps us deliver solutions that are truly scalable and produce results efficiently at every stage of the AI application lifecycle: training (=when we teach the algorithms to do certain things) and inferencing (=when the algorithms do their work).
As we progress, we help decide whether we shall run AI on the server, in the Cloud or closer to where the data is created or stored. Besides, we help deploy AI on embedded device as well as in large scale data center environments.