Are you trying to get another 10% out of your development team? When it comes down to the wire, is development always taking the longest time to deliver? Today, I'm going to provide insights into how to get over that organizational obstacle. Hi, I'm Peter Nichol, Data Science CIO. One of the most significant changes in information technology is with development. Over the last decade, we've experienced a pivot in what's included in corporate development teams, where is development performance and many questions around the future of development.

Have you joined the meeting only to find out that a brand new initiative was being launched that you had no idea about? Have you ever been on a conference call curious about the next steps of the project only to find out that it pivoted 180 degrees and now is rushing off in a new direction? Hi, I'm Peter Nichol, Data Science CIO. Today, I will provide some insights and how to get ahead of some of those challenges. As our teams have transitioned from working almost all on-site—with some remote access—to nearly all remote, many micro-interactions have decreased. As a result, the team is feeling disconnected from their team members and their organizations. They are often not involved early in the initiatives but instead informed quite late in the process.

One of the exciting areas is machine learning operations or MLOps. Today, we're going to get into how to start and evaluate your machine learning operations environment. Hi, I'm Peter Nichol, Data Science CIO. What is ModelOps? One of the exciting areas of data science is understanding models. Usually we begin with model operations or ModelOps. ModelOps includes everything that's required to orchestrate your AI pipeline. A traditional definition of ModelOps might be the life cycle management of all AI and decision models (including models based on machine learning, knowledge graphs, rules, optimization, linguistics and agents). ModelOps is the foundation of many data science environments.

One of the exciting areas is machine learning operations or MLOps. Today, we're going to get into how to start and evaluate your machine learning operations environment. Hi, I'm Peter Nichol, Data Science CIO. What is ModelOps One of the exciting areas of data science is understanding models. Usually we begin with model operations or ModelOps. ModelOps includes everything that's required to orchestrate your AI pipeline. A traditional definition of ModelOps might be the life cycle management of all AI and decision models (including models based on machine learning, knowledge graphs, rules, optimization, linguistics and agents). ModelOps is the foundation of many data science environments.

Does your team have a clear expectation of how to win? Is the end goal absolutely defined? Probably not. Today, I'm going to offer insights on how to get your team there. One significant leadership challenge we are trying to set is a very obvious flagpole at the end of some less than delivered idea. Meaning we're trying to mature. We want to evolve. Yet, we're not precisely what that endpoint looks like. The result is a confused team. They don't understand the goal. They also don't understand the strategy to achieve the goal. Hi, I'm Peter Nichol, Data Science CIO.

How do you optimize a DevOps pipeline? It would help if you got into the weeds, but it's doable. How do you streamline business operations? Even if you're not a domain expert, somehow, you get in there and figure it out. These are all challenges executives face that are relatively easy to solve. Capturing organizational value is hard. There is one question that always seems to stump executives and leaders, even myself sometimes. The question is, what was the value provided by the team last quarter? This question causes leaders to think and reflect on what the heck did they do the prior quarter. And more importantly, articulate the value that anybody would care about. So today, I'll offer insights and approaches to solve this problem. Hi, I'm Peter Nichol, Data Science CIO.

Are you running a team based on legacy metrics? Are you accountable for running a portfolio? Are you using dated or antiquated numbers to drive strategic value? I'm going to provide some insights to help you step out of that rut. Hi, I'm Peter Nichol, Data Science CIO. One of the biggest challenges we experience as we're trying to optimize our portfolio is to lock down what executives really care about. What metrics do leaders want? What metrics are needed but not be directly asked for today? I’m here to share my insights on this topic.

Does your team have a strategy? Are they able to understand the team vision? Can they articulate how your strategy enables that vision? Probably not. Hi, I'm Peter Nichol, Data Science CIO. Today we're going to talk about roadmaps.

Do you think about the strategy for different parts of your business? How about procurement? Does procurement have a strategy defined? Today we're going to get into category management and discuss why dialing this capability can be strategically impactful. Hi, I'm Peter Nichol, Data Science CIO.

Do you run a team today? Do folks know what your team provides? If you asked a business partner to name three services your team offers, could they do it? Today, I'm going to share insights on making the services you offer both internally and externally transparent to your business partners. Hi, I'm Peter Nichol, Data Science CIO.