Ashley Fidler

Enterprise products & AI platforms. I just lurk now.

[Ashley Fidler @aefidler]

@shelbysmith_co Let most things go. The vast majority of them aren't more important than your relationship.

[Ashley Fidler @aefidler]

@Natalie_Furn I would say only do it if you're really passionate about the topic. It won't help you earn more and there's opportunity cost for sure. Happy to talk if you want more depth on this.

[Ashley Fidler @aefidler]

Want a product job where you talk to and think about users every day? Where amazing user experience is your top concern?

If so, DM me. We're hiring :)

JD: https://t.co/qyGzjMusFD

[Ashley Fidler @aefidler]

In your product resume, don't forget to mention what your products do.

You built a product strategy at Company X? Great! What the heck does that company do?

I'm noticing that at least half the resumes coming though miss this key info.

[Ashley Fidler @aefidler]

Back in the US building a new product and a new product-focused company. Having a blast so far. Looking forward to sharing more as we move forward.

[Ashley Fidler @aefidler]

@TheHonorableAT Totally worth it. It does change your perspective.

[Ashley Fidler @aefidler]

As my time at @Eigen_Tech officially comes to a close, I am so grateful to my amazing team. Not only are they consumate professionals who do their work with incredible care, they also planned such a touching send-off - including a poem! I will miss you all❤️

[Ashley Fidler @aefidler]

@johncutlefish Lack of clarity - about structure and probably also about strategy.

[Ashley Fidler @aefidler]

@sanjoshi You too!

[Ashley Fidler @aefidler]

Time for an end-of-year social media break. Wishing you all a restful and enjoyable holiday. See you in the New Year!

[Ashley Fidler @aefidler]

5/ AI products often entail simultaneously addressing technical feasibility and market fit. This is hard. Make sure you have a bread-and-butter problem that drives revenue with well-understood tech so that you can make these investments. Otherwise be prepared for pain.

[Ashley Fidler @aefidler]

One more ML Q&A before the holidays:

How does prod mgt differ between traditional SaaS and ML/AI products?

1. Platform + portfolio, rather than product
2. Services and margins
3. Non-determinism and testing
4. The R&D process
5. Technical + market feasibility in parallel

[Ashley Fidler @aefidler]

4/ Ongoing R&D is critical for ML products - people expect them to get better over time. Build flexibly from day one so that new science can be easily tested in the product. Also hire a science PM to facilitate the R&D into production process.

[Ashley Fidler @aefidler]

@Seema71710910 @ML_Tweet_Bot Great point about data. Seems like unrealistic goals for ML accuracy span every industry 🙃

[Ashley Fidler @aefidler]

Enterprise Product Q&A:

What does "good" look like for a machine learning model?

Thinking about this question often brings me back to natural language user research I was involved in at Microsoft....

[Ashley Fidler @aefidler]

Of course, "good" depends a lot on the problem and the business need. But watching people get frustrated with systems at the 75% accuracy mark gave me a visceral reminder of what ML accuracy means to product quality. 3/3

[Ashley Fidler @aefidler]

We did several small studies to figure out how people engage with voice systems.

When accuracy was about 75%, people were annoyed. The results seemed like crap.

At 80-85%, people would use it, but didn't really find it trustworthy. Above 90% started to feel natural. 2/3