All good things take time, and that is true for A.I. as will. However, it will also take money... A lot of money, more than you expect!

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We've said it before and we'll say it again: No Data Science project team is complete without a string Data Engineer contingent!

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Even though many people will tell you A.I. is a "quick fix", in reality it is anything but that and the actual time investment required for a good, grounded AI environment is always under estimated...

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Have a problem? Fix it with some A.I.! It's sooo easy, you won't believe it! And you shouldn't. Believe it I mean. Nope, nothing that promises so much, should ever be trusted blindly!

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Our guest today has an interesting background, moving from the dark side of data collection to a protector of your data. In this final part of our interview with Purandar Das, we talk about the practical side of protecting your data.

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Our guest today has an interesting background, moving from the dark side of data collection to a protector of your data. In this first half of our interview with Purandar Das, we talk about his journey and why protecting your data is just so damn hard!

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Why do so many people feel the need to cram A.I. in every conceivable nook and cranny, when traditional, proven methods would be just fine? No idea, but it's definitely a bad idea!

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So let us take a look at why A.I. projects fail and see how to fix the problem!

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Of all projects that an enterprise can undertake, a project involving anything to do with Artificial Intelligence seems to have a disproportionate chance of failure. Is this inevitable, or can something be done to make these highly visible projects more successful?

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Platform Engineering is another one of those hype words, the "thing" that solves all your problems, according to the marketing at least... But what is is actually, and what can it be?

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