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The COVID-19 pandemic has changed the way we all operate. Consumer habits have changed dramatically, and remote working is now the new norm.

Contemporary business applications face challenges on multiple fronts. A XOps approach will help organizations stay ahead. Contemporary organizations rely on business applications to help make critical decisions. Data about the business, competitors, the environment, and more are visualized in multiple ways.

Consumer Packaged Goods (CPG) companies are at the cusp of a significant digital transformation, backed by data-driven strategies. There is severe competition in this space from external players like online merchants, especially since the advent of the pandemic.

The propensity for enterprises to adopt AI has been at an all-time high for the last decade. From automating tasks to predicting possible pitfalls and improving efficiency, AI has a plethora of use cases in the business sector today.

The Internet of Things (IoT) is the internet connectivity between physical devices. It is one of the widely used technical development.

Sentiment analysis helps to analyze feedback data from customers and gain insights such as “what” the customer means or “how” they feel about a product.

Our NLP analytics solutions can help calibrate business strategies to meet customers’ needs by analyzing feedback and extracting the right information.

The ongoing COVID-19 pandemic overwhelmed the healthcare sector and exposed its limitations. But, it also opened the aperture for the adoption and scaling of artificial intelligence (AI) to solve critical healthcare challenges and make many aspects of healthcare more efficient.

With the potential to transform the business, optimize the process, streamline the workflow, and increase revenue, AI (Artificial Intelligence) has become the latest tech buzzword.

When deploying ML projects, the Kubernetes-powered Kubeflow ecosystem has a good shot at tackling scaling and infrastructure dependencies. It is the best fit for Trade Promo Effectiveness (TPE) models, which run at scale with incremental data loads.

Changes in the data distribution are monitored with Data Drift, one of the most common indicators when monitoring MLOps models. It is a metric that measures the change in distribution between two data sets.

The COVID-19 outbreak acted as an unanticipated catalyst in forcing the transition to a digital future. It laid bare a clear digital divide between those organizations that had already invested in building digital capabilities and those who were sitting on the sidelines.

Before singing praises on the wonders that MLOps can do, let me shine some lights on a few new learnings, thanks to the post-pandemic crisis, that the companies across the globe have learned, especially the CPG.

In the modern business landscape, timing is everything. Quickly identifying user’s intent can help you get a leg up your competition. How? It can enable you to respond actively to a potential customer’s interest and multiply your chances of influencing the key decision-makers through meaningful conversations.

The COVID‐19 pandemic that hit us last year brought a massive cultural shift, causing millions of people across the world to switch to remote work environments overnight and use various collaboration tools and business applications to overcome communication barriers.

Just a few years ago, AI seemed like some futuristic tech straight out of a Sci-Fi movie. But the tables have turned now. We probably experience AI-based tech more often than we think.

Machine learning – a tech buzz phrase that has been at the forefront of the tech industry for years. It is almost everywhere, from weather forecasts to the news feed on your social media platform of choice. It focuses on developing computer programs that can acquire data and “learn” by recognizing patterns and making decisions with them.

Machine learning – a tech buzz phrase that has been at the forefront of the tech industry for years. It is almost everywhere, from weather forecasts to the news feed on your social media platform of choice. It focuses on developing computer programs that can acquire data and “learn” by recognizing patterns and making decisions with them.

‘Efficiency’ roots from processes, solutions, and people. It is one of the main driving forces leading to significant changes in the way companies work in the first decade of the 21st century. The following decennary further accelerated this dynamic. Now, post-COVID, it is vital for us to become efficient, productive, and environmentally friendly.

Supply chain and operations planning (S&OP) is a critical supply chain planning process through which various teams agree on a fundamental governing plan for the next weeks and months, which then forms the basis of all the detailed planning and execution.

Nowadays, corporate executives recognize that analytics is pivotal for pricing teams to create solutions that enable them to achieve their firm’s pricing objectives. In the B2B domain, ‘dynamic pricing’ is a critical approach to bring substantial benefits to companies.

On June 18, 2020, Databricks announced the support of Apache Spark 3.0.0 release as part of the new Databricks Runtime 7.0. Interestingly, this year marks Apache Spark’s 10th anniversary as an open-source project. The continued adoption for data processing and ML makes Spark an essential component of any mature data and analytics platform.

Machine learning – a tech buzz phrase that has been at the forefront of the tech industry for years. It is almost everywhere, from weather forecasts to the news feed on your social media platform of choice. It focuses on developing computer programs that can acquire data and “learn” by recognizing patterns and making decisions with them.

“The Times They Are A Changing”

“A good design adds value faster than it adds costs!” – Is my campaign design future ready? Are they adding value or just more cost? These have been key challenges faced by marketeers across the globe for a while now and tectonic shifts…

Most of us are familiar with Continuous Integration (CI) and Continuous Deployment (CD) which are core parts of MLOps/DevOps processes.  But Continuous Monitoring (CM) may be the most overlooked part of MLOps process, especially when you are dealing with machine learning models. In the first part of this series, we looked at some of the challenges in scaling and maintaining ML projects in a company.  In part 2, we are going to look at how Tredence is helping customers overcome these challenges.

If there is a word that has seen a spike in usage over the three months, then it must be “UNPRECEDENTED.” There is no better word to describe the situation that businesses and people are faced with today in the face of CoVID 19 pandemic. There are three clear phases in which this event has played out – 1. Pre-CoVID 2. During CoVID and 3. Post-CoVID

Wine, Cheese and Blue Jeans – all of them get better with age.

What about ideas? For most ideas, the opposite is true. Who remembers the revolutionary 5 ¼ floppy disk? At a certain point, ideas need to be debunked. Alternative and novel approaches are needed.

Document Reconciliation and Accounts Process automation both these terminologies go hand in hand these days. Organizations are increasingly looking to automate their reconciliation processes mainly to address the following issues:

Customer satisfaction has become the central point in the E-commerce industry to attract new customers and, most importantly, retain them in the long run. Companies are competing to deliver products as fast as they can in a fast-paced environment. Corporations like Walmart, Amazon, and Home Depot are launching initiatives in this direction like the same day, 1-day, and 2-day delivery promises.

You have just completed a machine learning pilot, tackling a challenging business problem which has stymied your executives for the last few years. As the plaudits roll in, you start thinking about how to move your project from the pilot stage to a full-blown application which will drive business value to your stakeholders…..

The COVID-19 pandemic is fast becoming one of the biggest threats to human lives and the global economy. With governments across the world taking preventive measures of quarantine, social distancing and travel bans, hospitality is one of the first industries to be adversely hit. The impact is not just limited…

The Coronavirus outbreak seems to be having an unprecedented effect globally. While brave health workers are confronting the fatal virus head on, business leaders from several service providers are playing an important role in the background. In addition to medicine and healthcare, basic food items…

Good quality data is extremely important as it directly impacts business insights and this could be in the form of structured data sources like Customer, Supplier, Product etc. or unstructured data sources like sensors and logs. Traditional rule-based methods to manage data quality is no longer efficient…

Let me start this post with a provocative statement: More machine learning models than ever, but are they making it into Production? Or more provocative even: once the ML models make it to production, are the IT Support teams equipped to run it, maintain it, manage it and above all safeguard the investments made on these breakaway enterprise assets?

The era of Artificial Intelligence (AI) has already begun, and various industries are investing heavily in creating self-evolving AI applications. The CPG industry (consumer packaged goods) is carefully taking note and slowly establishing new applications to improve efficiency and decrease expenses. Businesses on the frontline of AI application …

AI has become the pillar of growth for companies when it comes to maintaining relevance as well as an edge over the competition. What’s more, AI based models have become the new revenue drivers for companies looking to capitalize on data as a competitive advantage. The rise in algorithmically driven successes can be attributed primarily to enhancements…

Priya, a budding data scientist, was upset when she was bombarded with programming-related questions in her recent job interview. “I spent the last two years working on various modelling techniques, but now I am being asked questions about Python? I would like to build my career in data science and not in application development,” she said, with genuine doubts…

Have you ever wondered how the complexity in the supply chain ecosystem is influencing your freight spend? What are the factors affecting the overall freight management curve in the supply chain? How can an enterprise meet the 7Rs of Logistics whilst handling a huge amount of customer data…

The hype around Artificial Intelligence is far from fizzling out anytime soon. Digitalization and big data have completely penetrated the supply chain industry and are ubiquitous in nature. This article discusses one of the more interesting trends in the current supply chain analytics space – The Control Tower.

A reliable vendor management system must execute performance-oriented deliverables within a stipulated time frame without errors. High-tech makers of industrial machinery are, thus, in constant lookout for such ‘Make-To-Order’ process settings…

The topic related to ‘Product Management’ has received quite a flake in recent years. Several rounds of discussions have happened to create an analogy out of client’s stand point.
As I heard more of these conversations, there …

‘Augmented data management’ is a key trend where AI/ML is transforming how enterprises manage their data.
In the last article, we looked at some of the key pain points that exist as IT and business leaders …

Let us start with an oft repeated question,” What do you know about your customer’s preferences”?
The answer could be any of the standard responses which talk about their tastes in your merchandise based on past transactional records…

“We are drowning in information, but starved for knowledge”
This is a famous quote by John Naisbitt which shows the key difference between information and knowledge.

Enterprises are rushing to transform themselves, and embrace the promise of digital transformation; and while the means to achieve this end are disputed, there is unanimous agreement on the fact that reliable data is the starting point.