Manual Gone Fishing

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After a quick introduction to the gear we put on our snowshoes and lifejacket and walked over to the ponds which have a layer of ice about cms thick. There are a few large orange tents pitched on the ice which help keep the warmth in.

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I was actually surprised at how comfortable the temperature was inside the tents compared to outside. Before we began fishing, Arata-san demonstrated all the steps to catching fish through the ice. The lure itself is actually a very small piece of coloured fluff that sort of looks like an insect which is attached to a barbless hook. This special barbless hook is used so that it can easily be removed from the mouth of the fish without doing them any harm.

Keeping tension on the line means that you can feel when a fish is biting and then you can go about setting the hook. Arata-san then drops the line into the hole in the ice along with some bait, and immediately I could see dozens of fish swarming around the line.

He stays calm and slowly reels in, making sure to keep tension on the line. After a brief fight, Arata-san lifts the fish up through the hole and on to the ice. After some quick photos we gently release the fish back into the pond and head inside the tent. Now it was my turn to go fishing. I think I got too excited and reeled in too fast, because the fish was off the hook after just a few seconds. We had a good laugh before Arata-san explained that if I slow down when reeling in a fish, it will stay on the line.

Following his instructions more carefully I proceeded to land my first Rainbow Trout ever!

Moana - Maui Mini-Movie: Gone Fishing (UHD 4K)

It has been a dream of mine to catch Rainbow Trout in Hokkaido, so this was really special for me. I fished in this spot for another 30 minutes and caught 3 or 4 more fish before moving on to the next pond. Most of the ponds at Big Fight Fishing are set up for semi-captive breeding.

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The first one we were on was one such pond. The second one we moved to had completely wild trout. This was really exciting, because these fish are bigger, stronger and smarter than the fish from the other ponds. Arata-san warned that because these fish are bigger, they have more experience, and have learned to look out for the fishing line. We got set up inside the nice warm tent and after about 5 minutes, we had a hit!

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We have a domain history of having strong machine-physics-based domain knowledge. We have lots of people who have built really powerful models over time to understand and predict how assets will perform. This is now converging computer science and statistics-based models, using the data to understand how things will perform and what can be expected.

Today, we see three communities and usage patterns. The first is your data science community. Why is part X failing in this model asset for this particular part of the world? When an insight is found and an outcome is identified, how do we then codify that, and add a new alert, to detect when conditions are meeting the ones that drove us to find that relationship? Your second usage pattern would be your software engineering community, who will program something based on the data coming from the lake.

If you look at aviation, we started by trying to collect all the engine performance data coming off an aircraft engine in order to improve asset performance and other attributes. Excel serves a purpose. Over time, the belief is this data lake will largely disrupt many of the ways we manage data and do reporting in our companies.

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That is the bridge between raw technical computing and storing and IT infrastructure, and your data scientists, who are spending time in R and other languages to build models. Data engineering is a discipline that sits in between the two, makes data more accessible and provides the tools a data scientist would want to have. It allows the data scientist to focus more on developing the model, developing the insight, not on how to stitch the information or stitch the toolset to make it productive.

You have a pool of people out here on the west coast, at some of the consumer Internet giants or cloud-native companies.

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Then you have your math-based and stats-based folks, who can be really effective and productive if they already have a software engineering orientation. You have folks who architect and construct the architectures and the technologies for the data lake itself.

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  6. That would be more your data management profiles. Not building the models directly, not having the domain context to build the models, they can be more of a horizontal capability to allow you to do data science at scale. You go out and hunt for these coveted data scientists and bring them in, only to frustrate them. One of our first use cases, before using our current approach with the data lake plus data engineering we went through 10 months of organizing data and figuring out where it existed and breaking down silos, in order for someone to actually go after the outcome.