What is Machine Learning?

We hear a lot about Machine Learning & Deep Learning these days – that the next wave of jobs are going to be in AI and Machine learning – we attempt to answer some questions here and give some insight on component selection.

What is it?

Machine learning is focused on using computer algorithms which draw predictive insight from static or dynamic data sources – this is done using analytic and probabilistic models – refined using training and feedback.

It uses pattern recognition, artificial intelligence learning methods, and statistical data modeling. “Learning” happens in two ways – Supervised Learning ; when the desired outcome is known and an annotated data set used to train / fit a model to accurately predict values outside the training set. Unsupervised learning ; uses raw data to seek out inherent partitions or a group of characteristics present in the data itself.

Machine learning combines varied disciplines of computer science, mathematics, graph and network theory, statistics and probability – practically endless areas of application

A lot more computing can be accomplished with a workstation and a few graphic cards than what the whole planet’s computing power could do in 1990 ! Let that sink in. Estimated worldwide data storage is in zettabyte ranges , 1 zettabyte is a trillion gigabytes. We’re probably staring down the golden age of Machine Learning right now!

Right time ?

Humongous amounts of data from scientific instruments, business records, mature and well annotated databases – things look to be falling in place for ML. Inexpensive computing resources and established programming methods for utilizing GPU acceleration AND there is the money. Money in research, startups and big business budget allocations – Data Scientist is the hottest job title.

Quite understandably, the single biggest driving force for the rise of Machine Learning is to try and sell you stuff! High paying jobs in places like Google, Baidu, Microsoft & Facebook – some really amazing work is being done to improve marketing and advertising.

The applications ?

ML has huge potential for scientific discovery – terabytes of data generated by research instruments open up possibilities for discovery in chemistry, bio-medicine, economics etc. Great potential for robotics, autonomous vehicles, translation and voice recognition is a no-brainer.

To realize how far computing power has come along , here’s a short note for perspective. In 2012 Google set out analyzing YouTube data using a neural network with unsupervised learning – the feature extraction classifier discovered (on its own) that Cat videos are the most popular on YouTube. This work was accomplished using a conventional computing cluster for training – consisted of 1000 nodes with 1600 cores! Today you could run a model with the exact same complexity using 2 workstations with 4 GPU’s in each. This makes for a great use case for GPU acceleration in ML.

What configuration should i choose?

Short answer is – it depends. On your data set and your work load
Part selection guideline would be some thing like the below –

  • One or Two Nvidia Pascal GPU’s
  • Intel i7 or i9 CPU
  • 32GB to 62GB of system memory (RAM)

Recommended configuration –

  • GPUS – 2 nos. (GTX 1070, GTX 1080ti or Titan Xp) (Cuda Cores are your friends!)
  • Core i9 7900X (10 Core)
  • 64GB of Memory
  • 1 TB SSD/HDD (SSD is preferred for faster data transfer)

A more basic configuration can be arrived at, optimizing part selection based on budget and use case – not every application will need a i9 7900x – an i5 8400 can do the job for smaller data sets (although not as fast)

Do comment if you want to see a more detailed reasoning on component selection or shoot us an email at support@themvp.in for any questions or queries.

Until next time.
theMVP.in – High Performance Systems




What RYZEN means for gamers ?

The hype around RYZEN is finally dying out a bit, and we’re seeing the benchmark wars begin. Specifically – most of these benchmarks focus on how much of a performance improvement RYZEN has over Intels X99 chipset processors.

What does this mean for the gamer who is looking for a serious upgrade –

Let the benchmarks do the talking – 10 games tested i7 6800k Vs Ryzen 1700X

Pretty evenly matched – but the price point is the kicker here – there is simply NO REASON to pick 6800k over 1700X

Hold on a minute; how much do i have to shell out ?

It’s been no secret that Intel has been using its market leader / tech leader tag to overcharge consumers for ever so minor improvements to their line ups.

And now they’re worried – big time , just look at the price cuts across the range of their processors – Source


The benchmarks trickling out almost unanimously place RYZEN head and shoulders above Intel’s priciest offerings – but you did miss a trick if you thought this points to a resurgence of AMD as the go to processor for gaming.

Will it be cheaper for team red? 

Now RYZEN’s pricing and target market is decidedly the “enthusiast” who wants the latest and the greatest available – when put into the context of gaming – SINGLE CORE performance is king, which is where Intel excels. It also helps that Intel has a wider product line up , going from entry level all the way to enthusiast.

AMD is doing the smart thing buy tackling Intel where it matters – Intel being Intel will not go down easily. Having resorted to underhanded tactics, with their humongous distribution network – forcing distributors to not stock AMD in return for discounts – this battle is far from over.

From a performance per dollar (or INR) perspective RYZEN has blown Intel out of the water in the top end of the bracket.


It’s still cheaper for someone to build an 7th generation i5 build (i5 7600K – happens to be our best pick for gaming), giving you all the processing power you need for the next HALF a decade at least!

On the upside – cutting edge performance is no longer monopolized by greedy Intel folk!

Do leave a comment – your take on this matter

Until next time