Sofia  Maggio

Sofia Maggio

1624667160

15 Key Learnings for Product Managers in ML engagements

Did you know that an overwhelming 35% of Amazon’s sales come from product recommendations?

Did you know that Spotify’s Discover Weekly and Release Radar playlists are machine-generated and account for 31% of all listening on the platform?

Add to it, did you know that an impressive 80% of Netflix’s stream time is through its recommender system that translated to $1B in savings on customer acquisition?

Well, it must come to you as no surprise that these technology companies share a common trait — a strong data-driven culture that leverages the latest technologies such as AI, Machine learning and deep learning.

This was evident when Gartner expanded its coverage of AI technologies in the ‘Gartner Hype Cycle for Emerging Technologies, 2020’ published earlier in the year. It also cited that 80% of emerging technologies would have AI foundations by 2021.

While the top stories are exemplary, many organisations across the globe are still grappling to derive substantial business value using these disruptive technologies. According to IDC, 25% of organisations worldwide that are already using AI solutions report up to 50% failure rate. The survey also revealed that unrealistic expectations and lack of skilled people were amongst the top reasons for failure.

For the successful companies, a ‘Data First’ or ‘AI First’, culture has helped them not only align their people and processes, but also their goals. They thrive in developing AI/ML platforms that are continuously tuned to stay efficient as business priorities change.

Me and Nithin Subhakar (@nithin subhaka) will try to break it down into 15 simple steps.

Foreword

Based on our experiences, product managers need to play a pivotal role not just in orchestrating business value delivery but also in working extensively with ML engineers, data scientists and data engineers with an appreciation for the experimental nature of ML engagements.

We are happy to share some of our key learnings from ML engagements and we hope that product managers who are starting out their ML journey find them useful.

Before we get to the key learnings, let’s first understand what an ML product development life cycle is.

#product-management #machine-learning #stakeholder-management #product-life-cycle #machine-learning-ai #ml engagements

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15 Key Learnings for Product Managers in ML engagements
Sofia  Maggio

Sofia Maggio

1624667160

15 Key Learnings for Product Managers in ML engagements

Did you know that an overwhelming 35% of Amazon’s sales come from product recommendations?

Did you know that Spotify’s Discover Weekly and Release Radar playlists are machine-generated and account for 31% of all listening on the platform?

Add to it, did you know that an impressive 80% of Netflix’s stream time is through its recommender system that translated to $1B in savings on customer acquisition?

Well, it must come to you as no surprise that these technology companies share a common trait — a strong data-driven culture that leverages the latest technologies such as AI, Machine learning and deep learning.

This was evident when Gartner expanded its coverage of AI technologies in the ‘Gartner Hype Cycle for Emerging Technologies, 2020’ published earlier in the year. It also cited that 80% of emerging technologies would have AI foundations by 2021.

While the top stories are exemplary, many organisations across the globe are still grappling to derive substantial business value using these disruptive technologies. According to IDC, 25% of organisations worldwide that are already using AI solutions report up to 50% failure rate. The survey also revealed that unrealistic expectations and lack of skilled people were amongst the top reasons for failure.

For the successful companies, a ‘Data First’ or ‘AI First’, culture has helped them not only align their people and processes, but also their goals. They thrive in developing AI/ML platforms that are continuously tuned to stay efficient as business priorities change.

Me and Nithin Subhakar (@nithin subhaka) will try to break it down into 15 simple steps.

Foreword

Based on our experiences, product managers need to play a pivotal role not just in orchestrating business value delivery but also in working extensively with ML engineers, data scientists and data engineers with an appreciation for the experimental nature of ML engagements.

We are happy to share some of our key learnings from ML engagements and we hope that product managers who are starting out their ML journey find them useful.

Before we get to the key learnings, let’s first understand what an ML product development life cycle is.

#product-management #machine-learning #stakeholder-management #product-life-cycle #machine-learning-ai #ml engagements

Tyrique  Littel

Tyrique Littel

1597305600

IOT Product Management: 4 Critical Success Factors

It’s been over 6 months since I joined KritiLabs. The learning that I have had been very steep and intense, considering its a career shift for me from a services based pre-sales to a product based pre-sales and product management.

KritiLabs’ core proposition has been to help solve some of the real world Remote Asset Management problems using IoT. As part of my initial 30 days to further understand their DNA, I was involved in understanding the products, the people & teams behind it, the customers we work with.

Below is my perspective, based on what I had learned till date, on what is essential to make a hardware product succeed for mass adoption in industries which are still following decades old systems and processes:

User Empowerment

With IoT, it is very easy to reduce the user involvement within a process to a zero. Our products have been designed specifically for the non- tech savvy folks who take care of running the backbone of our country’s logistics - truck drivers, commercial vehicle drivers etc. We felt taking some of the decisions out of their hands would make these users feel demotivated, as they are no longer empowered to take decisions as part of the process, resulting in lower adoption of these devices in such an industry or a business process.

In such situations it is always prudent to empower the users to take decisions or operate these devices based on the information the device provides to these users, while logging their activities as part of audit trail.

User feedback

Regular user feedback is essential to help improve the product not only from improving the product’s experience but also for product iterations which can solve additional use cases within the same business process. To create a new product / device, we follow a very specific methodology to bring the product / device to fruition (more on this process in the next blog). As part of this process, when we create a prototype, we either deploy it on the field along with an existing product to to get user feedback or we work with one of our customers to help get that feedback.

We have seen that getting user feedback has helped make the product very robust and functionally closer to an ideal solution.

Better design & integration testing of hardware

Unlike software products, where certain features of the product can be rolled back, issues related directly to the firmware and hardware are not that easy to be solved through a simple update. It’s actually a better strategy to invest more in the design and test phase for better integration of the hardware & software as well as ensuring no issues related to hardware creep in production

Pilots

One of the corner stones for success for a product when newly introduced is beta phase or a pilot phase. Especially with regards to hardware, the chances of it failing outside of a controlled environment is way higher. In our pilots at KritiLabs we have seen multiple points of failure right from the vagaries of nature to human ingenuity to ensure the product fails in meeting its objectives.

Success of the pilot doesn’t hinge on the success / failure of the product during pilot, but amount of feedback and data it can collect during the pilot. This data and feedback must be fed back to product / solution development

These are some of the key things, when done right can help improve mass adoption of IOT devices in areas which still follow processes and systems which are decades old.

#product-management #product-development #success-factors #product #careers #ux #startups #iot-product-management

What is Machine learning and Why is it Important?

Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.

It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.

To help you understand this topic I will give answers to some relevant questions about machine learning.

But before we answer these questions, it is important to first know about the history of machine learning.

A Brief History of Machine Learning

You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.

In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.

In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.

In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.

Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.

During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.

Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning?

#machine-learning #machine-learning-uses #what-is-ml #supervised-learning #unsupervised-learning #reinforcement-learning #artificial-intelligence #ai

Obie  Rowe

Obie Rowe

1598403060

How To Get Started With Machine Learning With The Right Mindset

You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.

Before we dive into the machine learning world, you should take a step back and think, what is stopping you from getting started? If you think about it, most of the time, we presuppose things about ourselves and assume that to be true without question.

The most normal presumption that we make about ourselves is that we need to have prior knowledge before getting started. Get a degree, complete a course, or have a good understanding of a particular subject.

The truth is that most of the time, this is a lie, the prior knowledge you think you need is most of the time not required or is so big that even experts from the field don’t fully understand it. The Seek of this prior knowledge is a trap that will make you run in circles, which leads us to the next presumption.

The perfect condition, you can’t wait for the ideal environment or situation to get started, things will never be 100% ready, try and fail, then try again. It takes a lot of time to get good at machine learning; you won’t learn all at once and especially at the beginning.

Instead of trying to acknowledge everything before getting started, do a little bit every day; you can make significant progress by creating small things every day for a considerable amount of time. The perfect condition will never exist, do it in your path, be consistent with it, and the results will come.

After you start making little progress every day, you probably will end up having a struggle with something or failing to achieve your goal at a certain point. This feeling is tough; it’s hard to see yourself not making any progress, not having any sense of gratification, and then still not give up.

Machine learning is hard, it might take you a few weeks, months or even years to see progress in a certain point but isn’t any harder than any other technical skill, it requires repetition and dedication to get where you want, you need to test it, make a mistake and learn from i

#machine-learning #artificial-intelligence #python-machine-learning #learn-machine-learning #latest-tech-stories #machine-learning-uses #ml-top-story #ai-and-ml

Tech Avidus

Tech Avidus

1604379605

Digital Assets Management Software Solution | AI-based Assets Management System

A Digital Asset Management System makes it easier to store, manage, and share all of your digital assets on cloud-based storage.

We help you to build Digital Asset Management (DAM) systems with your precise business requirements, whether you want one for maintaining management, production management, brand management systems, or implementing with your sales department with the digital assets it needs.

To learn more about how the Digital Asset Management system will help your business, email us at hello@techavidus.com

#digital assets management #assets management solution #digital asset management system #production management #brand management