Customer-centric Professional services providers improve the client’s condition by providing skills, behaviors, content, advice, experiences, and other factors unique to that niche over a designated time.
A system providing a standard and consistent set of definitions of metrics on top of the data warehouse.
Know your audience, Adapt your vocabulary to match your audience, Make client care, Structure your story - Minto’s Pyramid Principle, Demonstrate competence & Establish your credibility Be Specific with a flexible framework in place, Appeal to the head, heart, and hand
How to understand what you should do in life? Every minute spent doing something other than what you love the most today is a minute you will likely regret when you’re old.
Do you want to excel in the engineering career? Surprisingly, this has very little to do with technical skills. To be a high-leveraged engineer, learn meta-skills like better communication, relationship management, and business understanding once you perfect the basics of your technical skills.
I will give you a framework on how to engage clients with minimal effort, it's quite normal that you have to send the 1st proposal to a client with very limited information. The proposal needs to be directionally right and open-ended enough to incorporate new details at a later stage.
IT services growth does not only mean getting more business. There are other profitability levers that should also be considered like not just to grow in volumes but also think about the rate of profitability per resource.
The adaptation of the AI mindset and an iterative AI journey will ensure that AI investments are properly executed and more resources are put into the bets that are proven to work in the MVP phase.
Data scientists are getting automated faster than the rest of the engineers
IDC predicts that up to 88 percent of all AI and ML projects will fail during the test phase. A major reason is that AI solutions are difficult to maintain. In this post, I will highlight how the maintenance of AI solutions is different and why MLOps is important.
Imagine that you are either * The Data Scientist, who is going to implement the solution, will require technical feasibility and complexity assessment like is the right data available? how inference will be integrated with the business process? and is the acceptance criteria achievable with the given data set and constraints?
For business-facing (B2B) AI products, it’s often difficult to get the data necessary to build a prototype because a lot of highly specialized data is locked up within the companies that produce it. There are a couple of general ways in which AI teams can get around this problem:
A list of key questions that will help asses business and technical feasibility of a machine learning solution Lets take an example of a machine learning and deep learning based forecasting solution. Imagine that you are the Machine learning engineer and want to asses a forecasting use case from two