Researchers urge technology practitioners such as data scientists to consider the impacts and ethical implications of algorithmic decisions. However, unlike programming, statistics, and data management, discussion of ethical implications is rarely included in standard data science training. To begin to address this gap, we designed and tested a toolbox called the data ethics emergency drill (DEED) to help data science teams discuss and reflect on the ethical implications of their work. The DEED is a roleplay of a fictional ethical emergency scenario that is contextually situated in the team's specific workplace and applications. This paper outlines the DEED toolbox and describes three studies carried out with two different data science teams that iteratively shaped its design. Our findings show that practitioners can apply lessons learnt from the roleplay to real-life situations, and how the DEED opened up conversations around ethics and values.

Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure

Join us for a 2 hour hands-on workshop and learn how to easily create and deploy models. Tomorrow's AI systems will be built by AI while data science teams play a supervisory role. Much like Tony Stark instructing Jarvis, data science teams can instruct generative AI to executive tasks. We will be doing a 2-hour end to end demonstration and workshop of our state of the art AI-Assisted Data Science platform. The workshop will kick off with a technical talk surrounding Generative AI, LLMs, and its implications within the industry.

Manasi Vartak is founder and CEO of Verta, a Palo Alto-based provider of solutions for Operational AI and ML Model Management. Technology research firm Gartner, Inc. has estimated that 85% of artificial intelligence (AI) and machine learning (ML) projects fail to produce a return for the business. The reasons often cited for the high failure rate include poor scope definition, bad training data, organizational inertia, lack of process change, mission creep and insufficient experimentation. To this list, I would add another reason that I have seen many organizations struggle to achieve value from their AI projects. Companies often have invested heavily in building data science teams to create innovative ML models.

AI leader DataRobot announced the availability of DataRobot Notebooks, a fully integrated notebooks solution within the DataRobot AI platform that enables data scientists to collaborate across code-first workflows with one-click access to embedded notebooks. "Customers want a notebook solution that will allow them to focus on their data science work rather than infrastructure management" Notebooks are a crucial tool for data scientists to rapidly experiment and share insights through quick environment creation, interactive computation, and code snippets. As the number of notebook users in a data science organization grows, challenges including managing notebooks at scale and maintaining complex dependencies and libraries become overwhelming and costly for data science teams. "We are entering a phase of AI governance where the collaboration and productivity gains of data science teams become increasingly important," said Mike Leone, Senior Analyst at Enterprise Strategy Group. "With DataRobot Notebooks, the flexibility to develop in preferred environments, including open-source ML tooling or in the DataRobot AI platform, streamlines the code development experience and allows data scientists to better collaborate as a team in a unified environment."

Artificial intelligence (AI) adoption and its impact on organizations are at a critical juncture. As businesses see its significant benefits, AI is widely adopted every year. According to IBM Global AI Adoption Index 2022, the need to cut costs and automate crucial processes, increasing competitive pressure, and changing customer expectations are the top drivers influencing AI adoption. Without question, AI will significantly change how many industries operate. And for this reason, a lot of leaders are making rapid investments in AI.

Sometimes as engineers and scientists, we think of data only as bytes on RAM, matrices in GPUs, and numeric features that go into our predictive black-box. We forget they represent changes in some real-world patterns. For example, when real world events and trends arise, we tend to defer to Google first to acquire related information (i.e where to go for a hike, what does term X mean) -- which makes Google Search Trends a very good source of data for interpreting and understanding what is going on live around us. This is why we decided to study a complex interplay between Google Search trends using it to predict other temporal data, and see if perhaps it could be used as features for a temporal machine learning model, and any insights we can draw from it. In this project, we looked at how Google Trends data could be used as features for times series models or regression models.

Let's say your company's data science teams have documented business goals for areas where analytics and machine learning models can deliver business impacts. Now they are ready to start. They've tagged data sets, selected machine learning technologies, and established a process for developing machine learning models. They have access to scalable cloud infrastructure. Is that sufficient to give the team the green light to develop machine learning models and deploy the successful ones to production?

With everything going computerized and digital, the amount of data generated by us is humongous. Organizations collectively spend billions of dollars to just store and analyze this data. They make efforts to drive valuable business insights from this data using data mining. Data Mining is the process of discovering hidden patterns in a pile of big data. Business executives use these emerging patterns to make informed business strategy decisions.

IT leaders and business executives around the world recognize the strategic importance of operationalizing AI, yet surprisingly few have moved beyond experimentation. A recent Capgemini survey finds that only 13% of companies have moved beyond proofs of concept (POC) to scaling AI across the enterprise. The struggle to operationalize AI is painful because it represents lost time and resources and unrealized potential. Articles abound full of suggestions, frameworks and manifestos, shared with the intent of closing the gap between AI concept and enterprise delivery (including one proposal to eliminate the POC altogether). Many of these are smart and worthwhile.