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The Secret Sauce for Machine Learning

By Sachin Andhare

Inventory forecasting is a critical business requirement for any enterprise that relies on the movement of supplies to and from warehouses. Whether it’s a manufacturing operation, logistics vendor, large distributor, or a large retailer, managing optimal inventory is a critical function of efficient operations management by creating the right balance between profit and cost. While there is a multitude of advantages in properly managing and moving inventory, there are three core benefits that most businesses highlight and that inventory management with AI can boost:

Which Data Science and ML platform is best for your business?

By Sachin Andhare

Automated feature engineering and AI-powered data preparation are the key differentiators for a code-free or code-first approach to data science Innovation, data, and analytics leaders looking for the best data science and machine learning platform have a hard nut to crack! Selecting a data science and machine learning (DSML) platform, given how fragmented the market is, where every vendor claims to be the ideal enterprise AI platform can be jarring. The challenge is even more complex for organizations that are new to machine learning or a traditional BI background without predictive analytics experience. And ditto for application developers and software architects searching for Cloud AI services to leverage AI and ML using APIs. What are some of the technical features that they need to consider? Which platform capabilities are most important? Gartner recently published the magic quadrant report for DSML platforms and evaluated over 20 platform vendors from AWS SageMaker,…

Reflections from ODSC East 2021

By Sachin Andhare

This was the second year in a row that the premier data science conference went virtual due to the Covid-19 pandemic. Overall the experience was much better this year with a breadth of research topics as well as industry coverage from machine learning for Time Series Data, Transformers in natural language processing (NLP) to Deep Neural Networks for visual quality inspection in manufacturing.

AI for Small and Medium Businesses; not an oxymoron anymore

By Sachin Andhare

With dotData Cloud, a fully managed end-to-end AI solution, SMBs have AI-powered predictive analytics with zero coding at their disposal. The latest Worldwide Artificial Intelligence Spending Guide from IDC shows that spending on AI is projected to double over the coming years to $110 billion by 2024, growing at a CAGR of 20.1%. While AI is becoming a must-have technology for large enterprises, small and medium businesses are getting left out. According to Deloitte’s State of AI survey, the top two benefits that enterprise customers are seeking from AI are making processes more efficient and enhancing existing products and services.  However, these benefits are equally applicable and important to SMBs. In fact, SMBs will benefit more from creating new products and services, an area where small companies and startups shine, making employees more productive and enhancing the product portfolio by leveraging AI. Unfortunately, few SMBs are using AI. The perception…

Can AI Enable Smart Factory and Realize Industry 4.0 Vision?

By Sachin Andhare

Accelerate Digital Journey for Manufacturers with AI Automation Smart factory technologies that leverage the industrial internet of things (IIoT), edge computing, autonomous vision systems, and AI and Machine Learning can significantly improve cost, throughput, quality, safety and provide revenue growth. Despite the benefits, a recent Deloitte survey showed that only 5% of US manufacturers had fully converted at least one smart factory, while about 30% reported ongoing smart factory initiatives. AI is critical for Smart Factory enablement, and Implementing AI in industrial operations is challenging. Many solutions are designed for greenfield (new factories) projects and do not address complex data management, challenging legacy machinery integration, enterprise security requirements, real-time analytics, and the capability to handle thousands of models in the production environment. A fundamental problem is finding skilled people to develop and deploy AI. Global manufacturing leaders often rely on citizen data scientists – subject matter experts with domain knowledge…

Is The Pace Of AI Development Slowing Down?

By Sachin Andhare

Got Data Science Platform, Visualization, and MLOps Tools, yet struggling with scaling AI? Feature Engineering holds the key to faster development! Data science, analytics, and BI leaders in disparate industries such as financial services, retail, and manufacturing have been spending heavily on AI tools, upgrading data infrastructure, augmenting BI with ML. Your organization may already have an AI Center of Excellence (CoE) to support LoB’s where the teams are building predictive applications that can predict churn, detect fraud, and forecast inventory.  Yet, for the vast majority of enterprise customers, the AI development has been slow, AI initiatives have not scaled according to expectations. What can you do to scale AI development, accelerate adoption and propel innovation?  You need to step back, analyze the data science process and focus on three core buckets in the  development workflow - Data Preparation, Feature Engineering, and Machine Learning. More specifically, answer three critical questions: Who…

The 10 Commandments of AI & ML (P2)

By Sachin Andhare

Concerned about accelerating AI workflows, addressing multiple use cases, and scaling ML initiatives? Here are the rules to help you succeed. In this two-part blog series, we review the ten rules that will ensure success with your first AI/ML project paving the way for many more. In the first part, we discussed the alignment of business objectives with use cases, getting a head start on data preparation and the mechanics of feature engineering. We also talked about understanding AutoML tools’ capabilities and ensuring the right modeling approach while balancing model accuracy and interpretability. This second part will discuss why visibility to the ML processes and results are critical, the importance of data science education, real-time analytics, infrastructure compatibility, and ML operationalization. Here are the five best practices: Ensure the ML project has visibility. ML initiatives fail because they operate with a silo mentality, like a secret science experiment that no…

The 10 Commandments of AI & ML (P1)

By Sachin Andhare

Building an enterprise AI application is problematic. Scaling ML and getting user adoption is even more challenging. Here are the rules to follow. The big day is finally here! After months of squabbles, discussions, and fistfights, you have managed to get the budget allocated for evaluating machine learning. You have identified the right AutoML tool and are eager to get started on your first proof of concept. Where do you begin? This project’s success will determine the course of the digital transformation journey, production roll-out at other business units, and perhaps earn you a fancy new title! If you want to do ML the right way, you need to follow a set of commandments. In this two-part blog series, we’ll cover the ten rules. Here are the top 5 : Start with critical objectives for the project, and think about alignment with business goals.  Identify top use cases, key performance…

Does The Popularity of AutoML Mean the End of Data Scientist Era?

By Sachin Andhare

McKinsey Analytics wrote an article on the evolution of automated machine learning (AutoML) titled “Rethinking AI talent strategy as AutoML comes of Age.” McKinsey argues that the growing popularity of AutoML tools drives a radical new way of thinking about data science talent. By automating the data science process, AutoML platforms expand the reach of users to include business experts with extensive domain knowledge, non-data scientists, and operational experts. The key takeaway is that companies are best served by not putting all their resources into the fight for sparse technical data science talent but should instead focus at least part of their attention on building up their troop of AutoML practitioners, who will become a substantial proportion of the talent pool for the next decade. CIOs, data science, and analytics leaders will have to rethink their AI talent strategy fundamentally. The Covid-19 pandemic, budget cuts, and the pressure to do…

6 Common Pitfalls You Must Avoid To Succeed with Digital Transformation

By Sachin Andhare

A lot of money is flowing into Digital Transformation (DX) initiatives. Worldwide spending on the technologies and services that enable the digital transformation (DX) of business practices, products, and organizations is forecast to reach $2.3 trillion in 2023, based on an IDC spending guide. The IDC findings reveal that DX spending will steadily expand throughout the 2019-2023 forecast period, achieving a five-year CAGR of 17.1%. Yet 70% of all DX initiatives do not reach their goals. A WSJ survey of business leaders and executives found that digital transformation (DX) risk was their #1 concern in 2019. Why do most transformations fail? What can business leaders do to improve the odds of success? George Westerman, principal research scientist, MIT, defines DX as “using technology to improve an organization’s performance and reach radically.” Research from MIT shows that businesses that have become digital masters are 26% more profitable. According to Gartner, DX…

Accelerating AI Transformation in Manufacturing

By Sachin Andhare

Many experts agree that AI will have the most significant impact on manufacturing. According to McKinsey Research, AI can create $1.2 Trillion to $2 Trillion of value in supply-chain and manufacturing. Manufacturing processes generate enormous amounts of data, involve repetitive tasks, and present multi-dimensional problems beyond the scope of many conventional tools. The industry is also projected to face a workforce shortage due to skilled employees’ looming retirement. AI and Automation are key technologies that can address this gap while increasing operational efficiency, improving quality, and boosting productivity. However, AI has yet to gain significant momentum and reshape manufacturing. Manufacturing executives and plant leaders must overcome several challenges before AI-led digital transformation transitions from a select few to a broad market at scale : Legacy Infrastructure - The production sites typically have a wide variety of machines, tools, and systems that use disparate and often competing technologies. For example, discrete…