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For 24 months, I tried almost a dozen Twitter scheduling tools.
Then I found @typefully, and I've been using it for seven months straight.
When it comes down to the experience of scheduling and long-form content writing, Typefully is in a league of its own.
I forgot about Twitter for 10 years. Now I'm remembering why I liked it in the first place.
Huge part of my new love for it: @typefully. It makes writing threads easy and oddly fun.
This is my new go-to writing environment for Twitter threads.
They've built something wonderfully simple and distraction free with Typefully.
Such a huge fan of what @typefully has brought to the writing + publishing experience for Twitter.
Easy, elegant and almost effortless threads - and a beautiful UX that feels intuitive for the task - and inviting to use.
Luca Rossi ꩜@lucaronin
After trying literally all the major Twitter scheduling tools, I settled with @typefully.
Kudos to @frankdilo and @linuz90 for building such a delightful experience.
Killer feature to me is the native image editor — unique and super useful 🙏
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Neural Networks as Linear Combinations.
• NNs are multiple linear methods working in combination, but the power of non-linearity is what makes the difference.
• Let's see how many lines it'd take you to separate these two groups?
Who are the people in MLOps?
Data Science and MLOps are constantly evolving and have multiple roles and people.
Not only Data Scientists but many other people are also there in the ML Model lifecycle.
But who are they and what do they do?
1. Subject Matter Experts
Data scientists may not have a deep understanding of the businesses and domains they work for.
SMEs help them frame the problem better by working alongside data scientists.
They have defined goals, business questions and KPIs.
Need for Machine Learning Pipelines
• Machine learning pipelines are crucial for sustainable machine learning systems.
• But what good do they bring? What are the benefits that we get from them?
Let's discuss ↓